Coalition for Sign Language Equity in Technology (CoSET)

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Hello.

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Hello, everyone.

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My name is Tim Riker

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and I am going to be the presenter today,

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one of the presenters.

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I’m from Brown University

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and I’m a member of this advisory board,

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which is for artificial intelligence

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and sign language interpreting.

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Today we are thrilled

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because we are going to be providing you

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a presentation.

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We’ll be talking about our report.

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And

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the advisory council is here today

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together.

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We’ll be talking about some of the work

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that we’ve done, collecting data

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from three webinars

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that we hosted last fall.

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The reason that we decided

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to host these webinars

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and do this research

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is because we wanted to get more

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of the deaf perspective

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and to take that perspective.

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And we do

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some more research and former

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Task Force for Safe AI.

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That report is going to be presented

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today and we

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would like to share now with you

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the topic of the session.

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I’ll go ahead and introduce myself.

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And so in terms of visual description,

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I am currently wearing a black shirt.

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It’s a long sleeve

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black shirt,

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collared shirt

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with buttons and a white male

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with reddish blondish hair.

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I have a bit of a mustache

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and facial hair.

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So today our presentation

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topic is going to be death safety.

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I

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a.i meaning artificial intelligence.

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And our goal

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is to have a legal foundation

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for ubiquitous automatic interpreting,

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using artificial intelligence

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and how that relates to interpreting.

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So

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I wanted to talk a bit about why

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this topic is so important.

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As you know,

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there are many users

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of sign language interpreters.

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We sometimes go through frustrations

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and situations

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because we are using technology,

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and sometimes technology

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can be very beneficial,

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while other times technology

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can cause harm.

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So with VR, I

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so with VR, AI, for example,

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we have video,

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remote

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video, remote interpreters

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that come up on the screen.

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And sometimes it can be a great idea

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because, you know,

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when it first came out, we saw that

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there was a lot of freezing.

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Sometimes there might be challenges

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internally, like,

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let’s say

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things are going on

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in that room that cause issues.

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Various issues

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would happen with this technology.

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Now, if you think about R2,

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we think about automatic interpreting

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or artificial intelligence

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and interpreting.

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Are we ready for that?

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How will that impact

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the greater community?

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What will be the community’s

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view of this?

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So last fall

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we hosted three webinars

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and those webinars happened here

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at Brown University.

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They were through Zoom

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and we had a panel

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and we had discussions

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and we gathered the view

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of multiple people.

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We also had deaf community members

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who came in to watch

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and make comments

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and talk about their perspective.

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So in that discussion we saw that

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there was a lot of rich information

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that was shared

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and the team, the advisory group,

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decided to work to analyze

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that information that came

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from the discussions.

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And right away,

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we knew we had a lot of rich content

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and that we would be able

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to take that content

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and add it to the report

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so that we could get

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a general understanding of what fire

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I would look like.

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And we had several different questions

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that we put into a survey

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and we sent those out and we were able

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to get those responses from the deaf.

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Unfortunately,

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we did not have a survey in ASL,

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but we knew that we needed

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to have these discussions

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in order to gather the information

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we were looking for.

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Let’s go ahead and

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head to the next slide.

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So

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I’d like

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to introduce

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you to other members of our team.

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We all participated together

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in gathering this research

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and we’ve been working hard as a group

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to get that information.

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And these lovely people here

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volunteer their time.

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I’d like to introduce you to them now

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so that you can get to know them a bit.

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And they will also be talking

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about the report today.

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So

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let me go ahead and pass it over to you.

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Let’s start with Theresa.

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Tracey,

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if you’d like to introduce yourself.

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Sure.

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Good morning, everyone.

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My name is Theresa Blake.

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Maya Burke

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and I work at Gallaudet University.

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I’m a professor of philosophy

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and my research

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is specifically in ethics.

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And it’s the ethical application

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to technology.

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And I’m thrilled to be here with you

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all today.

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I’m looking forward

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to discussing the webinar

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and having other discussions

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with all of you today.

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And I did like

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I would like to add

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that in terms of visual description,

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I am a middle aged

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woman with an olive, with olive skin

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and also I have brown eyes,

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I’m wearing

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glasses and I have a brown

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I have brown hair

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and my hair is in a bun today

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and I’m wearing a gray sweater

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and I’m here in my office at Gallaudet.

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Thank you, Theresa.

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This next, let’s have Jeff.

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Jeff,

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would you like to introduce yourself?

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Hello, My name is Jeff Schall

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and I am working.

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I work to develop

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AI for the deaf and hard of hearing.

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And in terms of a visual description,

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I am wearing a white

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and black plaid shirt.

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I have facial hair and brown eyes

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and I’m here.

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My office is a background

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and I work for go sign.

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I

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next will have Holly.

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Yes, hello.

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Good morning.

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My name is Holly.

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Last name is Jackson

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and visual description.

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I am an African-American black female.

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I have light skin and I have curly

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natural hair today.

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And

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I have a dark Navy

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blue suit jacket on.

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And I the shirt, my Navy blue

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suit

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jacket has light white stripes on it.

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And I have a shirt beneath my jacket,

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and it is a light blue tan

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lace

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top.

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That’s the design.

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And my background today

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is light gray

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plane, light gray background.

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And I’m an interpreter.

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I’m a hearing interpreter

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and also an educator

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and educator of ASL and interpreting.

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I work for any ASL program

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interpreting program, and also I am here

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for Naomi,

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the representation of Niobe,

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the Atlanta chapter, and

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I serve as the secretary for the board.

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That’s my position this year.

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Thank you very much.

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I’m happy to be here.

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Thank you, Holly.

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And last but not least, Anne Marie.

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Hello, I’m Anne Marie.

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Last name is Killian,

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and this is my signed name,

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and I am the CEO for

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Tie Access and visual description

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is that I’m a white

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female, middle aged with medium

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length hair,

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brown hair, and I’m wearing glasses.

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Today

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I have on a suit jacket

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that is black with a purple shirt

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beneath it.

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And in the background

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you can see my dining room table

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and blacks, black chairs and

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you might see two dogs

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running around in the background.

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If that happens, I apologize in advance.

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Like everyone else.

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I’m thrilled to be here today.

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Thank you.

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Great.

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So let’s go to the next slide.

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So today

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we will be talking about

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multiple things.

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And during these presentations,

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we’ll go in depth about our studies

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and what we have found through analyzing

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this data

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will be sharing with you

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this information today.

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The first thing we’re going to be doing

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is identifying

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three critical impact areas.

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And so these impact

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areas are quite important.

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We’ll be talking more in depth

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and describing what they are for you.

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Secondly,

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with this analysis

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and with this research in our webinars,

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we were able to go through the data

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and that data helped us

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to build a better understanding of what

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the deaf communities perspective

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and experience has been

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and their experiences with interpreters

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and the harms that have happened

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and the way that these experiences

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have impacted

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their life

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in terms of access,

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in terms of communication access.

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And so it’s very important

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to have this deaf community perspective

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so that we can understand

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what they’ve been through

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when it comes to

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their experiences in interpreting

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and where harm has happened.

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Often

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that is an experience

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that is common in our communities.

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So we would like to mitigate those harms

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and ensure that if we do put out

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new technology,

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that it’s going to be we’re going

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to be mindful of those harms

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that have been experience.

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Third, we’re going to talk

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about the value of the big picture

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lens on possibilities.

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We’ll talk about

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what it looks like to do right

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and prevent possible disaster.

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We want to make sure that we are looking

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through a lens where we are

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showing care and concern

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about the future of the community

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and taking all of these things

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into account. Next slide.

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So for today

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in this webinar, like you mentioned,

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we had a panel

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and we had

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today will be going

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through different presentations

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and we’ll be going through the report

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and having discussions about that.

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So we will be following this order,

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as you see outlined here and the slide

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we will be discussing

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ethics and fairness.

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We will be discussing how this research

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was studied

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and our approach to the research.

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We will be talking about what we found.

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So our findings,

00:12:04:10 – 00:12:07:13
we will also be discussing the results

00:12:08:03 – 00:12:11:07
and outcomes of our research.

00:12:12:02 – 00:12:13:17
And also we will be talking

00:12:13:17 – 00:12:14:18
about concerns

00:12:14:18 – 00:12:17:18
as it relates to the deaf community.

00:12:18:07 – 00:12:20:14
We will be also discussing techno

00:12:20:14 – 00:12:23:14
technology and the quality

00:12:25:05 – 00:12:27:23
and we will be asking,

00:12:27:23 – 00:12:29:10
are we ready for this?

00:12:29:10 – 00:12:30:20
Are we ready for eye,

00:12:32:06 – 00:12:33:00
for eye

00:12:33:00 – 00:12:36:00
and sign language to come together?

00:12:36:20 – 00:12:37:18
Are we ready?

00:12:37:18 – 00:12:40:18
And if we are, what does that look like?

00:12:41:18 – 00:12:44:18
What kind of risks are involved?

00:12:46:00 – 00:12:48:11
We need to be proactive

00:12:48:11 – 00:12:50:19
in understanding

00:12:50:19 – 00:12:52:09
and predicting those risks

00:12:52:09 – 00:12:53:21
so that we can mitigate

00:12:53:21 – 00:12:56:21
or resolve them before they occur.

00:12:59:18 – 00:13:02:08
We will also discuss the future

00:13:02:08 – 00:13:04:17
and what we can anticipate

00:13:04:17 – 00:13:08:02
and what we can recommend for

00:13:09:06 – 00:13:11:03
any anyone

00:13:11:03 – 00:13:13:22
who is going to be working in relation

00:13:13:22 – 00:13:15:08
to this topic.

00:13:15:08 – 00:13:18:08
All of us here today are impacted

00:13:18:08 – 00:13:22:23
or affected by this topic and many people

00:13:22:23 – 00:13:23:15
who are not here

00:13:23:15 – 00:13:24:15
today,

00:13:24:15 – 00:13:27:15
the entire community that we represent

00:13:27:22 – 00:13:30:22
and also work with.

00:13:32:05 – 00:13:34:17
So now

00:13:34:17 – 00:13:36:11
Teresa

00:13:36:11 – 00:13:39:11
will go ahead and begin this discussion

00:13:39:13 – 00:13:40:20
and we’re going to start off again

00:13:40:20 – 00:13:42:09
with ethics and fairness.

00:13:42:09 – 00:13:43:18
Teresa, I’ll turn it over to you.

00:13:49:06 – 00:13:50:09
one moment.

00:13:50:09 – 00:13:53:09
Let me make sure I have this up.

00:13:54:11 – 00:13:55:09
I always have to make sure

00:13:55:09 – 00:13:58:05
I have my spotlight on so that I’m seen.

00:13:58:05 – 00:13:58:17
Okay.

00:13:58:17 – 00:14:02:12
So ethics, ethics and fairness,

00:14:03:01 – 00:14:04:23
what emphasis

00:14:04:23 – 00:14:06:14
and what are we looking at here

00:14:06:14 – 00:14:07:18
with ethics and fairness?

00:14:07:18 – 00:14:09:12
What do we want to avoid?

00:14:09:12 – 00:14:11:00
We don’t want to cause harm.

00:14:11:00 – 00:14:13:17
How do we reduce the cause of harm?

00:14:13:17 – 00:14:15:02
And what does harm mean

00:14:15:02 – 00:14:16:14
to the deaf community

00:14:16:14 – 00:14:18:16
and deaf individuals?

00:14:18:16 – 00:14:21:22
Harm in general, to humanity?

00:14:21:22 – 00:14:23:12
We want to avoid.

00:14:23:12 – 00:14:27:00
So we have two

00:14:27:00 – 00:14:30:00
topics really

00:14:30:00 – 00:14:32:08
coexisting here ethics and fairness.

00:14:32:08 – 00:14:35:13
So what you’ll notice here,

00:14:35:20 – 00:14:38:24
we are concerned with controlling bias

00:14:39:24 – 00:14:42:00
and we want to

00:14:42:00 – 00:14:43:10
assign responsibility

00:14:43:10 – 00:14:46:10
or accountability, rather, as it

00:14:46:22 – 00:14:49:11
relates to AI to avoid harm.

00:14:51:13 – 00:14:52:04
The second

00:14:52:04 – 00:14:52:17
point I like

00:14:52:17 – 00:14:53:00
to make

00:14:53:00 – 00:14:55:05
is that we need to be very clear

00:14:55:05 – 00:14:58:14
and transparent with our documentation

00:14:58:14 – 00:15:00:02
about who is accountable

00:15:00:02 – 00:15:02:00
in the design of AI

00:15:02:00 – 00:15:03:24
and the development of AI

00:15:03:24 – 00:15:06:22
and the application and evaluation.

00:15:06:22 – 00:15:09:07
Everything related to A.I.

00:15:09:07 – 00:15:11:19
who is responsible for this portion

00:15:11:19 – 00:15:13:11
ethically?

00:15:13:11 – 00:15:16:11
Next slide.

00:15:20:01 – 00:15:20:13
Okay.

00:15:20:13 – 00:15:22:05
So we spoke about ethics,

00:15:22:05 – 00:15:23:01
and now I want to talk

00:15:23:01 – 00:15:25:08
a little bit about fairness.

00:15:25:08 – 00:15:28:19
How we in our society

00:15:28:20 – 00:15:31:20
have developed the meaning behind this.

00:15:31:20 – 00:15:35:12
We have a lot of negative biases,

00:15:35:16 – 00:15:36:08
and we also have

00:15:36:08 – 00:15:38:18
positive biases in society.

00:15:38:18 – 00:15:39:18
But what we want to do

00:15:39:18 – 00:15:42:02
is make sure that in air

00:15:42:02 – 00:15:44:07
we want to reflect the best of society

00:15:44:07 – 00:15:45:24
and not the worst.

00:15:45:24 – 00:15:46:20
So with that being said,

00:15:46:20 – 00:15:48:05
we want to eliminate bias

00:15:48:05 – 00:15:49:10
and we want to eliminate

00:15:49:10 – 00:15:50:24
eliminate favoritism

00:15:50:24 – 00:15:52:19
with measurable results.

00:15:52:19 – 00:15:56:12
And also we want to be able to observe

00:15:56:12 – 00:15:59:21
how people are interacting with that.

00:15:59:21 – 00:16:01:13
We have statistics and evidence

00:16:01:13 – 00:16:03:01
related to the fairness,

00:16:03:01 – 00:16:04:10
and we need to use that

00:16:04:10 – 00:16:06:10
fairness evidence based.

00:16:07:15 – 00:16:10:15
Next slide.

00:16:14:17 – 00:16:15:13
Okay.

00:16:15:13 – 00:16:17:01
This one is a little bit interesting

00:16:17:01 – 00:16:18:09
because it’s a little bit

00:16:18:09 – 00:16:21:09
of a mixture of English and

00:16:21:09 – 00:16:23:09
written English and sign language.

00:16:23:09 – 00:16:26:09
So what we see here is the word A.I.

00:16:26:09 – 00:16:29:09
by A.I.. A.I.

00:16:29:18 – 00:16:30:20
Times, A.I.

00:16:30:20 – 00:16:32:21
on the screen in a mathematical look.

00:16:32:21 – 00:16:34:09
But we have a few different ways

00:16:34:09 – 00:16:37:10
that we are expressing this concept Now.

00:16:37:10 – 00:16:38:13
If we’re signing it,

00:16:38:13 – 00:16:40:13
you may see a sign A.I.

00:16:40:13 – 00:16:42:05
squared. Okay?

00:16:42:05 – 00:16:43:01
But when we say

00:16:43:01 – 00:16:46:01
that we’re referring to automated

00:16:46:01 – 00:16:46:19
or automatic

00:16:46:19 – 00:16:48:00
interpreting by

00:16:48:00 – 00:16:50:02
artificial intelligence, A.I.

00:16:50:02 – 00:16:51:05
by A.I..

00:16:51:05 – 00:16:51:23
Now, sometimes

00:16:51:23 – 00:16:55:13
you may see people sign a AI by A.I.,

00:16:55:24 – 00:16:57:05
and it’s the same concept.

00:16:57:05 – 00:16:58:21
It means the same thing.

00:16:58:21 – 00:17:00:00
Now, in written form.

00:17:00:00 – 00:17:03:00
We’ll see A.I., X, A.I..

00:17:03:01 – 00:17:04:11
Those are the three ways you’ll see it.

00:17:04:11 – 00:17:06:08
But the main point is understanding

00:17:06:08 – 00:17:07:03
that we’re referring

00:17:07:03 – 00:17:10:12
to automatic interpreting by artificial

00:17:11:09 – 00:17:12:04
intelligence.

00:17:15:02 – 00:17:16:07
Now, with that being said,

00:17:16:07 – 00:17:19:07
I’m going to turn this over to

00:17:20:13 – 00:17:21:18
I’m sorry, I don’t remember

00:17:21:18 – 00:17:22:23
who’s next on here.

00:17:22:23 – 00:17:25:23
Let me work

00:17:26:13 – 00:17:29:13
just.

00:17:31:12 – 00:17:31:23
Hello.

00:17:31:23 – 00:17:34:23
Okay, so

00:17:35:24 – 00:17:37:12
as Teresa mentioned,

00:17:37:12 – 00:17:39:23
we hosted three webinars.

00:17:39:23 – 00:17:41:12
And during those webinars,

00:17:41:12 – 00:17:42:06
we invited

00:17:42:06 – 00:17:43:09
so many of the deaf

00:17:43:09 – 00:17:46:09
community members to participate with us.

00:17:46:19 – 00:17:48:05
Those webinars,

00:17:48:05 – 00:17:50:22
we discussed a variety of topics

00:17:50:22 – 00:17:51:23
and issues

00:17:51:23 – 00:17:54:11
covering AI and interpreting

00:17:54:11 – 00:17:55:18
and how they relate.

00:17:55:18 – 00:17:57:14
As Teresa mentioned, A.I.

00:17:57:14 – 00:17:59:21
Squared is how we were referring to A.I.

00:17:59:21 – 00:18:00:21
by A.I..

00:18:00:21 – 00:18:02:22
There was so much

00:18:02:22 – 00:18:05:09
great dialog conversation,

00:18:05:09 – 00:18:07:07
ideas and issues

00:18:07:07 – 00:18:09:18
discussed during these webinars.

00:18:09:18 – 00:18:12:16
Now we recorded these webinars

00:18:12:16 – 00:18:15:16
and we made a transcript of them

00:18:15:22 – 00:18:17:18
and we went through these transcripts

00:18:17:18 – 00:18:19:01
with a fine tooth comb

00:18:19:01 – 00:18:20:07
and we looked at them

00:18:20:07 – 00:18:21:12
and looked at patterns

00:18:21:12 – 00:18:23:04
that arose in each

00:18:23:04 – 00:18:24:04
discussion, different

00:18:24:04 – 00:18:25:11
themes that popped up

00:18:25:11 – 00:18:26:21
throughout the entirety

00:18:26:21 – 00:18:28:02
of these webinars.

00:18:28:02 – 00:18:29:11
And we wanted to make sure

00:18:29:11 – 00:18:31:12
that we were able to pinpoint

00:18:31:12 – 00:18:32:21
and really understand

00:18:32:21 – 00:18:34:21
what issues are most prevalent

00:18:34:21 – 00:18:36:10
to the community at large.

00:18:38:02 – 00:18:39:17
We did

00:18:39:17 – 00:18:42:08
a time analysis as well

00:18:42:08 – 00:18:44:22
and we looked at how frequently

00:18:44:22 – 00:18:46:14
different themes

00:18:46:14 – 00:18:48:18
popped up in said webinars

00:18:48:18 – 00:18:50:13
and we found

00:18:50:13 – 00:18:52:07
our findings

00:18:52:07 – 00:18:53:06
are shown in the next slide.

00:18:53:06 – 00:18:54:01
And if you’re interested

00:18:54:01 – 00:18:55:09
in more detailed information,

00:18:55:09 – 00:18:56:19
please read our report.

00:18:56:19 – 00:18:59:22
It goes into a varied in depth

00:19:01:05 – 00:19:03:02
reporting of our findings

00:19:03:02 – 00:19:05:00
and that is available online as well.

00:19:05:00 – 00:19:08:00
Next slide, please.

00:19:10:20 – 00:19:14:12
So this is a snapshot of our findings.

00:19:14:18 – 00:19:16:01
We put down

00:19:16:01 – 00:19:16:19
all the themes

00:19:16:19 – 00:19:17:12
that popped up

00:19:17:12 – 00:19:20:08
most frequently in our webinars,

00:19:20:08 – 00:19:21:22
and we categorized those

00:19:21:22 – 00:19:25:22
into three different areas of study.

00:19:27:00 – 00:19:30:08
The first area is related

00:19:30:08 – 00:19:33:08
to results and outcomes,

00:19:34:01 – 00:19:35:03
and we’ll talk about this

00:19:35:03 – 00:19:36:09
more in the next slide.

00:19:36:09 – 00:19:39:09
We’re focused more on discussing about

00:19:39:20 – 00:19:42:19
what kind of society,

00:19:42:19 – 00:19:44:24
what kind of societal results

00:19:44:24 – 00:19:48:20
will arise from the impact of AI by AI.

00:19:50:08 – 00:19:53:04
Our next theme was readiness.

00:19:53:04 – 00:19:56:04
How ready is the community at large?

00:19:56:21 – 00:20:00:05
How ready are stakeholders for

00:20:01:22 – 00:20:03:14
this technology?

00:20:03:14 – 00:20:04:19
We looked at the feedback

00:20:04:19 – 00:20:06:03
and the requirements and things

00:20:06:03 – 00:20:07:04
that need to happen

00:20:07:04 – 00:20:08:15
for the community at large

00:20:08:15 – 00:20:10:19
in this technological realm.

00:20:10:19 – 00:20:13:05
But as you can see on here,

00:20:13:05 – 00:20:16:05
by over half and half,

00:20:16:08 – 00:20:17:09
the biggest issue

00:20:17:09 – 00:20:19:01
that came up most frequently

00:20:19:01 – 00:20:20:08
and most prevalent on people’s

00:20:20:08 – 00:20:22:17
minds was technological quality.

00:20:22:17 – 00:20:24:08
What type of regulations

00:20:24:08 – 00:20:27:19
and standards are required to minimize

00:20:28:03 – 00:20:31:03
the reduce or the possibility of harm?

00:20:31:15 – 00:20:33:08
And how do we maximize

00:20:33:08 – 00:20:35:01
the potential benefits

00:20:35:01 – 00:20:36:17
of this technology?

00:20:36:17 – 00:20:37:22
Now, for this,

00:20:37:22 – 00:20:39:14
we went into more in-depth

00:20:39:14 – 00:20:42:03
research in our report,

00:20:42:03 – 00:20:43:24
and I’m going to turn it

00:20:43:24 – 00:20:44:20
over to Ann Marie.

00:20:44:20 – 00:20:45:12
If she can.

00:20:45:12 – 00:20:46:10
She can discuss this

00:20:46:10 – 00:20:47:08
a little bit more in depth.

00:20:49:05 – 00:20:50:08
Thank you so much, Jeff.

00:20:50:08 – 00:20:53:08
I really appreciate it.

00:20:53:08 – 00:20:54:06
The first thing that we’re going

00:20:54:06 – 00:20:55:07
to look at, again,

00:20:55:07 – 00:20:59:03
as Jeff mentioned in his synopsis

00:20:59:03 – 00:21:01:13
of the percentages that we looked at,

00:21:01:13 – 00:21:03:06
it is staggering.

00:21:03:06 – 00:21:06:06
The biggest section was on technological

00:21:06:20 – 00:21:09:20
aspects, but

00:21:09:20 – 00:21:13:02
I’m looking at the desired results

00:21:13:02 – 00:21:14:15
and outcomes as well,

00:21:14:15 – 00:21:16:07
and it was a very hot topic

00:21:16:07 – 00:21:18:18
in the discussions in the webinar.

00:21:18:18 – 00:21:19:20
It was really important

00:21:19:20 – 00:21:22:20
to emphasize that as well.

00:21:24:05 – 00:21:25:08
Now, more than one

00:21:25:08 – 00:21:26:17
third of the discussions

00:21:26:17 – 00:21:29:17
regarding this were really focused on

00:21:30:02 – 00:21:33:02
these critical areas of control,

00:21:34:16 – 00:21:36:19
control

00:21:36:19 – 00:21:39:16
by the language community

00:21:39:16 – 00:21:40:18
and the authority

00:21:40:18 – 00:21:42:24
who is involved in the decision making,

00:21:42:24 – 00:21:44:15
who is involved in the device

00:21:44:15 – 00:21:45:23
development of design

00:21:45:23 – 00:21:48:02
and the impact the A.I. is going to have.

00:21:49:16 – 00:21:50:24
The biggest concern

00:21:50:24 – 00:21:53:24
was really regarding the legality,

00:21:54:01 – 00:21:58:20
the structure and the synthesis of this.

00:21:59:03 – 00:22:00:20
And it was critical.

00:22:00:20 – 00:22:02:00
It was impressive

00:22:02:00 – 00:22:05:00
to see how much the deaf leaders

00:22:05:01 – 00:22:07:01
felt that,

00:22:07:01 – 00:22:08:03
you know, in history

00:22:08:03 – 00:22:09:20
in their based on their experience,

00:22:09:20 – 00:22:11:18
the deaf have not always

00:22:11:18 – 00:22:13:17
had a voice at the table,

00:22:13:17 – 00:22:15:05
whether it was in the development

00:22:15:05 – 00:22:17:15
of different views or different

00:22:17:15 – 00:22:19:01
processes.

00:22:19:01 – 00:22:20:01
The deaf perspective

00:22:20:01 – 00:22:20:15
has often

00:22:20:15 – 00:22:21:07
been missing

00:22:21:07 – 00:22:22:18
and it’s really critical

00:22:22:18 – 00:22:23:18
that they are included

00:22:23:18 – 00:22:24:17
in the conversation.

00:22:24:17 – 00:22:26:03
And what does that look like?

00:22:26:03 – 00:22:27:19
What does the deaf representation

00:22:27:19 – 00:22:28:06
look like

00:22:28:06 – 00:22:29:15
in the authoritative process

00:22:29:15 – 00:22:32:15
of developing these standards?

00:22:32:21 – 00:22:34:17
Also,

00:22:34:17 – 00:22:38:07
it is very important and very stringent

00:22:38:07 – 00:22:42:08
that any violation of these processes,

00:22:44:14 – 00:22:45:09
you know, if

00:22:45:09 – 00:22:47:01
these processes are violated,

00:22:47:01 – 00:22:48:20
what type of ramifications

00:22:48:20 – 00:22:49:15
does that have?

00:22:49:15 – 00:22:51:11
That’s been a very big discussion

00:22:51:11 – 00:22:53:07
topic as well. Next slide.

00:23:03:20 – 00:23:05:02
Now you see this next slide.

00:23:05:02 – 00:23:06:06
We’ve also included

00:23:06:06 – 00:23:09:09
some concerns based on our webinars

00:23:10:01 – 00:23:11:21
about control.

00:23:11:21 – 00:23:13:16
There are two levels of control,

00:23:13:16 – 00:23:17:06
individual control and cultural groups,

00:23:17:12 – 00:23:18:17
their control

00:23:18:17 – 00:23:19:20
and the impact

00:23:19:20 – 00:23:21:04
and the protections

00:23:21:04 – 00:23:23:16
needed for each of these groups.

00:23:23:16 – 00:23:25:02
We need to be sensitive to those,

00:23:25:02 – 00:23:28:02
especially with children.

00:23:29:21 – 00:23:31:19
The focus of the community at large.

00:23:31:19 – 00:23:33:16
The biggest priority here

00:23:33:16 – 00:23:37:08
was to make sure that the legal framework

00:23:39:01 – 00:23:41:00
is established with ABI.

00:23:41:00 – 00:23:44:00
I again, you know, it’s

00:23:44:12 – 00:23:48:11
one we’ve had to look at leaders involved

00:23:48:16 – 00:23:50:00
with the deaf community

00:23:50:00 – 00:23:51:17
involvement in research

00:23:51:17 – 00:23:54:17
and in development for these

00:23:55:02 – 00:23:55:20
technologies.

00:23:55:20 – 00:23:57:05
It’s very imperative

00:23:57:05 – 00:23:58:20
because it impacts their life

00:23:58:20 – 00:23:59:21
for the deaf community

00:23:59:21 – 00:24:00:10
at large,

00:24:00:10 – 00:24:00:23
for the deaf

00:24:00:23 – 00:24:01:20
blind community

00:24:01:20 – 00:24:02:18
as well,

00:24:02:18 – 00:24:04:14
for those that are losing their hearing

00:24:04:14 – 00:24:05:21
or hard of hearing

00:24:05:21 – 00:24:07:10
and learning sign,

00:24:07:10 – 00:24:09:14
this impacts all of them.

00:24:09:14 – 00:24:11:13
Again, it really can’t emphasize

00:24:11:13 – 00:24:12:17
this enough.

00:24:12:17 – 00:24:13:24
You know, the deaf

00:24:13:24 – 00:24:15:18
leadership needs to be involved

00:24:15:18 – 00:24:16:14
in the developmental

00:24:16:14 – 00:24:18:23
process of these platforms.

00:24:20:09 – 00:24:23:09
For I by

00:24:25:04 – 00:24:26:10
also it’s really interesting

00:24:26:10 – 00:24:27:04
to note

00:24:27:04 – 00:24:30:04
the importance of the concern that

00:24:30:24 – 00:24:33:24
I, I the data.

00:24:33:24 – 00:24:36:07
What is that data stored storage

00:24:36:07 – 00:24:37:01
look like?

00:24:37:01 – 00:24:39:10
Who is in conservatorship

00:24:39:10 – 00:24:40:20
of this storage?

00:24:40:20 – 00:24:41:21
What are the analytics

00:24:41:21 – 00:24:44:00
and the statistics looking at?

00:24:44:00 – 00:24:45:15
Where is that data held

00:24:45:15 – 00:24:47:00
and how is it protected?

00:24:47:00 – 00:24:49:10
That’s a very big discussion as well.

00:24:49:10 – 00:24:51:15
There’s a lot of concern about,

00:24:51:15 – 00:24:54:24
you know, hiring individuals

00:24:54:24 – 00:24:57:24
that could influence the development and

00:25:00:20 – 00:25:02:09
leadership of this.

00:25:02:09 – 00:25:03:13
That doesn’t always happen

00:25:03:13 – 00:25:05:00
to have deaf individuals

00:25:05:00 – 00:25:07:04
in this type of role

00:25:07:04 – 00:25:09:08
and involved in that process.

00:25:09:08 – 00:25:11:21
So will this process in the future

00:25:11:21 – 00:25:12:17
involve deaf

00:25:12:17 – 00:25:14:11
individuals in the beginning,

00:25:14:11 – 00:25:16:17
a foundational development of this,

00:25:16:17 – 00:25:18:16
or will they only bring them in for A

00:25:18:16 – 00:25:20:20
to perspective and that’s it.

00:25:20:20 – 00:25:22:00
So I think another good point

00:25:22:00 – 00:25:22:19
of discussion

00:25:22:19 – 00:25:23:08
as well,

00:25:23:08 – 00:25:24:17
who have the ownership and the

00:25:24:17 – 00:25:26:14
influence in this process.

00:25:29:22 – 00:25:31:20
You know, not only

00:25:31:20 – 00:25:33:19
hiring deaf consultants,

00:25:33:19 – 00:25:35:09
but all through the line,

00:25:35:09 – 00:25:36:01
through A.I.,

00:25:36:01 – 00:25:38:04
by having deaf

00:25:38:04 – 00:25:40:16
hands on the process is vital.

00:25:40:16 – 00:25:42:08
And I’m going to turn this over

00:25:42:08 – 00:25:45:08
to Jeff again.

00:25:49:08 – 00:25:51:20
Hi, it’s Jeff here again for you.

00:25:51:20 – 00:25:53:12
So

00:25:53:12 – 00:25:55:22
about the technological quality.

00:25:55:22 – 00:25:56:16
During the webinar,

00:25:56:16 – 00:25:58:19
we discussed this quite in depth

00:25:58:19 – 00:26:00:09
and how we can break down

00:26:00:09 – 00:26:03:10
this idea into several subcategories.

00:26:04:16 – 00:26:07:03
The first or the major subcategory

00:26:07:03 – 00:26:08:10
that a lot of people really

00:26:08:10 – 00:26:11:10
discussed in the webinar was data.

00:26:11:14 – 00:26:14:21
Many people agreed that the quality

00:26:15:05 – 00:26:16:19
and the diversity of the data

00:26:16:19 – 00:26:19:10
is key to the foundation of building A.I.

00:26:19:10 – 00:26:21:07
by A.I..

00:26:21:07 – 00:26:22:08
You know, with machine

00:26:22:08 – 00:26:25:08
learning in the community,

00:26:26:15 – 00:26:28:22
it’s garbage in, garbage out

00:26:28:22 – 00:26:30:06
more often than not.

00:26:30:06 – 00:26:32:12
And so what does that look like?

00:26:32:12 – 00:26:34:13
So if we have a model training

00:26:35:14 – 00:26:36:08
by that

00:26:36:08 – 00:26:38:03
with that data,

00:26:38:03 – 00:26:40:19
it will be the same in the output.

00:26:40:19 – 00:26:42:11
So

00:26:42:11 – 00:26:44:08
that leads us to our second

00:26:44:08 – 00:26:47:08
largest issue modeling.

00:26:47:23 – 00:26:50:09
So if we have the garbage in modeling,

00:26:50:09 – 00:26:51:07
it’s garbage out.

00:26:51:07 – 00:26:52:09
But people are looking at it

00:26:52:09 – 00:26:53:09
and saying, okay,

00:26:53:09 – 00:26:55:15
so what is the primary use?

00:26:55:15 – 00:26:57:11
What is the primary model?

00:26:57:11 – 00:26:59:07
What is what features are they using?

00:26:59:07 – 00:27:02:03
Are they focusing only on hand shape

00:27:02:03 – 00:27:02:18
or will

00:27:02:18 – 00:27:04:18
they also be including facial shape

00:27:04:18 – 00:27:05:08
and mouth

00:27:05:08 – 00:27:08:08
shapes, expressions and other key

00:27:08:15 – 00:27:11:15
contextual clues of the language?

00:27:12:09 – 00:27:14:22
How will we be able to evaluate

00:27:14:22 – 00:27:15:24
that model?

00:27:15:24 – 00:27:17:07
Can we decide

00:27:17:07 – 00:27:20:09
which model to use over another model,

00:27:20:21 – 00:27:23:21
and how do we use metrics

00:27:24:01 – 00:27:24:22
to decide

00:27:24:22 – 00:27:26:06
if that is the quality

00:27:26:06 – 00:27:27:20
that we need or not?

00:27:27:20 – 00:27:29:06
Many people really had

00:27:29:06 – 00:27:30:06
that on their minds

00:27:30:06 – 00:27:32:00
in the webinar discussions.

00:27:32:00 – 00:27:33:24
Now, throughout these discussions,

00:27:33:24 – 00:27:35:07
we also noticed the need

00:27:35:07 – 00:27:37:07
for deaf leadership

00:27:37:07 – 00:27:40:02
involvement and oversight as well.

00:27:40:02 – 00:27:44:01
It was key that that topic came up

00:27:44:01 – 00:27:44:24
many times.

00:27:44:24 – 00:27:46:13
We needed to set up

00:27:46:13 – 00:27:50:19
at least a minimum criteria for

00:27:52:00 – 00:27:54:15
the bidirectional interpreting for A.I.

00:27:54:15 – 00:27:55:13
by A.I..

00:27:55:13 – 00:27:58:13
Next slide, please.

00:28:01:16 – 00:28:04:18
Now many more subcategories

00:28:04:18 – 00:28:06:05
arose from this discussion,

00:28:06:05 – 00:28:08:02
and one of them was quality,

00:28:08:02 – 00:28:10:02
and it was very imperative

00:28:10:02 – 00:28:13:02
This topic popped up quite a bit

00:28:13:24 – 00:28:16:24
as well.

00:28:17:01 – 00:28:18:24
The participants all agreed

00:28:18:24 – 00:28:23:03
that during the process of gathering

00:28:23:13 – 00:28:26:18
data, storing data, using data,

00:28:27:05 – 00:28:29:24
sharing data, publishing

00:28:29:24 – 00:28:30:17
all of that,

00:28:30:17 – 00:28:31:18
and that process

00:28:31:18 – 00:28:33:19
needed to be quite transparent

00:28:33:19 – 00:28:34:23
so that we could understand

00:28:34:23 – 00:28:37:12
everything as it happened.

00:28:37:12 – 00:28:39:14
We need the opt in or opt out

00:28:39:14 – 00:28:41:07
option as well.

00:28:41:07 – 00:28:43:03
Also, discussing the ability

00:28:43:03 – 00:28:44:12
to withdrawal.

00:28:44:12 – 00:28:46:14
Our can fit in the future,

00:28:46:14 – 00:28:48:12
meaning if we opt into this

00:28:48:12 – 00:28:49:06
and then later

00:28:49:06 – 00:28:50:09
we change our minds and say,

00:28:50:09 – 00:28:50:18
you know what,

00:28:50:18 – 00:28:52:10
we don’t want to participate.

00:28:52:10 – 00:28:54:23
We have the option to withdraw

00:28:54:23 – 00:28:55:18
that consent.

00:28:57:10 – 00:28:59:04
Another topic that we discussed

00:28:59:04 – 00:28:59:22
quite in death

00:28:59:22 – 00:29:03:01
was the topic of safeguards.

00:29:03:23 – 00:29:05:12
Those are needed to be put in place

00:29:05:12 – 00:29:07:10
to minimize harm.

00:29:07:10 – 00:29:10:10
Many of the what ifs

00:29:10:13 – 00:29:12:18
that arose during our discussion

00:29:12:18 – 00:29:14:03
really related to this.

00:29:14:03 – 00:29:16:12
What if someone downloads this?

00:29:16:12 – 00:29:20:15
What if there is a breach of data?

00:29:20:21 – 00:29:23:14
What if the information is leaked?

00:29:23:14 – 00:29:25:19
What if the system crashes?

00:29:25:19 – 00:29:26:15
There were so many

00:29:26:15 – 00:29:28:09
what ifs and questions

00:29:28:09 – 00:29:30:09
that popped up in that discussion.

00:29:30:09 – 00:29:31:00
And again,

00:29:31:00 – 00:29:33:00
for more analysis of this, please

00:29:33:00 – 00:29:33:23
look at our report.

00:29:33:23 – 00:29:36:17
We have a lot of it detailed in there.

00:29:36:17 – 00:29:39:17
And we also talked about readiness.

00:29:40:10 – 00:29:42:08
And Theresa is going to talk about

00:29:42:08 – 00:29:44:19
that more in depth now.

00:29:44:19 – 00:29:47:19
I’ll hand it over to Theresa.

00:29:51:10 – 00:29:53:20
Okay.

00:29:53:20 – 00:29:56:20
So

00:29:57:08 – 00:29:59:20
in terms of readiness,

00:29:59:20 – 00:30:02:05
when we look at what that includes,

00:30:02:05 – 00:30:04:06
we have readiness

00:30:04:06 – 00:30:05:16
when it comes to technology,

00:30:05:16 – 00:30:09:03
but also there’s a discussion of society

00:30:09:10 – 00:30:11:12
and what our awareness

00:30:11:12 – 00:30:12:01
looks like

00:30:12:01 – 00:30:13:19
in terms of ethical issues

00:30:13:19 – 00:30:15:22
and ethical concerns.

00:30:15:22 – 00:30:18:21
So do we have

00:30:18:21 – 00:30:21:05
technological availability?

00:30:21:05 – 00:30:23:00
Do we have representation

00:30:23:00 – 00:30:25:16
and understanding and creation?

00:30:25:16 – 00:30:28:09
That’s one part of readiness.

00:30:30:12 – 00:30:33:12
Next slide.

00:30:37:24 – 00:30:39:22
So now we’ll see that there’s

00:30:39:22 – 00:30:41:12
there’s various components here.

00:30:41:12 – 00:30:42:11
So you can see here

00:30:42:11 – 00:30:43:19
where it talks

00:30:43:19 – 00:30:44:12
about readiness

00:30:44:12 – 00:30:45:13
when it comes to sign

00:30:45:13 – 00:30:47:12
language recognition.

00:30:47:12 – 00:30:50:24
And we have readiness of American

00:30:50:24 – 00:30:53:01
the American Deaf community.

00:30:53:01 – 00:30:55:07
That’s another component.

00:30:55:07 – 00:30:58:09
Does the deaf community have an in-depth

00:30:58:09 – 00:31:01:23
understanding of the having good quality,

00:31:02:06 – 00:31:05:00
ethical and technological aspects

00:31:05:00 – 00:31:06:17
when it comes to A.I.

00:31:06:17 – 00:31:08:04
by A.I.?

00:31:08:04 – 00:31:11:20
And the last sign of readiness would be

00:31:11:20 – 00:31:12:14
when it comes

00:31:12:14 – 00:31:15:22
to public and private entities,

00:31:16:09 – 00:31:20:08
Are we ready for this kind of technology

00:31:20:15 – 00:31:22:04
and what the responsibilities

00:31:22:04 – 00:31:24:02
be that come along with that

00:31:24:02 – 00:31:27:02
and the accountability?

00:31:28:04 – 00:31:31:04
Next slide, please.

00:31:34:15 – 00:31:36:06
So as you can see here,

00:31:36:06 – 00:31:37:05
one of the components

00:31:37:05 – 00:31:38:24
that we talked about are civil

00:31:38:24 – 00:31:40:13
rights and civil protections.

00:31:41:12 – 00:31:43:04
So what we have

00:31:43:04 – 00:31:45:23
quality control and certification

00:31:45:23 – 00:31:47:13
when it comes to the interpreters

00:31:47:13 – 00:31:49:01
and certification for the A.I.

00:31:49:01 – 00:31:50:17
technology itself.

00:31:50:17 – 00:31:51:06
Also,

00:31:51:06 – 00:31:53:03
we have to consider the impact

00:31:53:03 – 00:31:55:09
that this would have on the culture,

00:31:55:09 – 00:31:56:18
the current culture

00:31:56:18 – 00:31:58:08
and the culture of the future.

00:31:58:08 – 00:31:59:20
And what

00:31:59:20 – 00:32:00:19
what does

00:32:00:19 – 00:32:03:11
state of the art technology mean?

00:32:03:11 – 00:32:06:05
How would we make sure that we are up

00:32:06:05 – 00:32:07:12
to date with technology

00:32:07:12 – 00:32:09:04
as it changes over time

00:32:09:04 – 00:32:10:02
and ensure

00:32:10:02 – 00:32:11:12
that we have the appropriate

00:32:11:12 – 00:32:13:03
response to those changes?

00:32:13:03 – 00:32:16:03
Next slide.

00:32:26:00 – 00:32:27:08
So I believe that, well,

00:32:27:08 – 00:32:28:22
I can go ahead and do this part.

00:32:28:22 – 00:32:33:06
And so in just a few hours.

00:32:33:08 – 00:32:36:08
Next slide, please.

00:32:40:04 – 00:32:40:21
Okay, great.

00:32:40:21 – 00:32:42:01
Sorry about that.

00:32:42:01 – 00:32:45:22
And okay, So within a few hours,

00:32:46:05 – 00:32:48:08
we had these internal discussions

00:32:48:08 – 00:32:50:00
at the three webinars,

00:32:50:00 – 00:32:51:11
and within those hours

00:32:51:11 – 00:32:52:23
we had a lot of ethical issues

00:32:52:23 – 00:32:54:10
that came up to be discussed

00:32:54:10 – 00:32:56:06
and we touched on ethics,

00:32:56:06 – 00:32:58:10
we touched on fairness and safety.

00:32:58:10 – 00:32:59:09
And I believe

00:32:59:09 – 00:33:00:23
all of these things were covered.

00:33:00:23 – 00:33:02:20
Also, the deaf participants

00:33:02:20 – 00:33:04:21
were able to explain

00:33:04:21 – 00:33:07:08
how we can use our principles

00:33:07:08 – 00:33:09:03
and how we can use our models

00:33:09:03 – 00:33:10:15
in a way that’s ethical

00:33:10:15 – 00:33:11:19
in order to prevent

00:33:11:19 – 00:33:13:00
any ongoing

00:33:13:00 – 00:33:14:00
harm

00:33:14:00 – 00:33:15:09
or anything that could come

00:33:15:09 – 00:33:16:24
against the deaf community.

00:33:16:24 – 00:33:18:00
So we talked about that

00:33:18:00 – 00:33:19:04
fairness and equity

00:33:19:04 – 00:33:20:21
in these conversations,

00:33:20:21 – 00:33:23:09
how it pertains to society.

00:33:23:09 – 00:33:24:00
Next slide.

00:33:29:06 – 00:33:31:18
So we do have a few suggestions

00:33:31:18 – 00:33:35:02
and we suggest that there be a

00:33:35:08 – 00:33:36:23
the appropriate level of protection

00:33:36:23 – 00:33:38:19
and privacy and confidentiality

00:33:38:19 – 00:33:39:20
when it comes to A.I.

00:33:39:20 – 00:33:41:07
backed by A.I.,

00:33:41:07 – 00:33:42:11
and that will allow us

00:33:42:11 – 00:33:43:22
to have better protections

00:33:43:22 – 00:33:44:22
across the Internet

00:33:44:22 – 00:33:47:09
for all kinds of applications.

00:33:47:09 – 00:33:51:05
Also, another suggestion was that we have

00:33:52:15 – 00:33:55:19
that these concerns regarding risk

00:33:55:23 – 00:33:58:21
be established and considered in order

00:33:58:21 – 00:33:59:13
to make sure

00:33:59:13 – 00:34:01:20
that we have strict regulations in place

00:34:01:20 – 00:34:03:21
to avoid any adverse

00:34:03:21 – 00:34:05:10
downstream consequences

00:34:05:10 – 00:34:06:18
for governance, business

00:34:06:18 – 00:34:09:18
and social infrastructures.

00:34:10:13 – 00:34:13:13
Next slide.

00:34:14:15 – 00:34:15:08
Okay.

00:34:15:08 – 00:34:17:06
So this word stress

00:34:17:06 – 00:34:20:06
or socio technical systems

00:34:21:01 – 00:34:23:12
and again asks for short

00:34:23:12 – 00:34:26:07
means that we have to recognize

00:34:26:07 – 00:34:28:07
that we do have technology

00:34:28:07 – 00:34:31:12
and we also have our the socio aspect

00:34:31:20 – 00:34:34:01
when these two things come together,

00:34:34:01 – 00:34:34:19
they’re going to have

00:34:34:19 – 00:34:36:15
an influence on each other.

00:34:36:15 – 00:34:38:03
So we need to always

00:34:38:03 – 00:34:38:20
be sure

00:34:38:20 – 00:34:41:18
to look at the system of technology

00:34:41:18 – 00:34:44:04
while considering its impact on society

00:34:44:04 – 00:34:45:14
and vice versa.

00:34:45:14 – 00:34:47:03
We have to look at how both of these

00:34:47:03 – 00:34:48:20
things interact. Next slide.

00:34:54:12 – 00:34:55:17
So I’ll give you a moment

00:34:55:17 – 00:34:58:17
to read through the slide.

00:35:06:07 – 00:35:07:06
So it’s important.

00:35:07:06 – 00:35:08:09
Here to take note of

00:35:08:09 – 00:35:09:20
is that Estes

00:35:09:20 – 00:35:13:18
refers to how things are correlated, how

00:35:13:18 – 00:35:15:11
these co influences

00:35:15:11 – 00:35:18:01
happen in both society and technology

00:35:18:01 – 00:35:21:01
as it pertains to the organization.

00:35:21:08 – 00:35:23:03
So it’s so important

00:35:23:03 – 00:35:25:04
that during the design process

00:35:25:04 – 00:35:28:03
we consider both of these areas

00:35:28:03 – 00:35:29:19
and look at our results

00:35:29:19 – 00:35:34:04
to ensure that air by air is optimizing

00:35:34:09 – 00:35:37:09
both of these two subsystems.

00:35:41:18 – 00:35:44:19
So the key here is to attend to

00:35:44:19 – 00:35:47:21
how the social behaviors of humans

00:35:47:21 – 00:35:50:00
are going to combine and influence

00:35:50:00 – 00:35:52:16
by the structures of technology

00:35:52:16 – 00:35:55:16
and vice versa.

00:35:56:06 – 00:35:59:06
So these are not two separate things.

00:35:59:15 – 00:36:02:10
We do have a few recommendations

00:36:02:10 – 00:36:04:02
that we would like to share.

00:36:04:02 – 00:36:05:19
The first one is that

00:36:05:19 – 00:36:09:10
it needs to be understood that AI by AI

00:36:09:10 – 00:36:12:11
is a socio tech and technological system

00:36:13:10 – 00:36:15:11
and also we need to

00:36:15:11 – 00:36:16:07
make sure

00:36:16:07 – 00:36:19:10
that the deaf wisdom from the community

00:36:19:15 – 00:36:21:02
is a vital part of this.

00:36:21:02 – 00:36:22:10
We want to use our wisdom

00:36:22:10 – 00:36:24:03
and our experience

00:36:24:03 – 00:36:26:09
from interpreting experiences

00:36:26:09 – 00:36:29:19
to our experience with VR s VR AI,

00:36:29:24 – 00:36:31:01
and also

00:36:31:01 – 00:36:33:09
how we as a community

00:36:33:09 – 00:36:35:08
experience technology.

00:36:35:08 – 00:36:38:21
And we want to be able to impart

00:36:38:24 – 00:36:40:00
our experience

00:36:40:00 – 00:36:43:01
and our wisdom as the framework is being

00:36:43:01 – 00:36:46:01
developed in this realm.

00:36:46:01 – 00:36:50:00
Thirdly, we want to continue to engage

00:36:50:00 – 00:36:51:14
with the deaf community

00:36:51:14 – 00:36:52:21
and build our knowledge

00:36:52:21 – 00:36:55:17
and our awareness through funding.

00:36:55:17 – 00:36:58:17
One example of that funding are grants,

00:36:58:20 – 00:37:00:07
and so an example

00:37:00:07 – 00:37:02:15
is the Civic Innovation grant,

00:37:02:15 – 00:37:04:05
where we can receive money

00:37:04:05 – 00:37:04:21
for this

00:37:04:21 – 00:37:06:02
and continue

00:37:06:02 – 00:37:07:20
to plug in to the deaf community

00:37:07:20 – 00:37:09:04
and get their input

00:37:09:04 – 00:37:10:09
and find what the deaf

00:37:10:09 – 00:37:11:15
community would like to see

00:37:11:15 – 00:37:12:14
and what they feel needs

00:37:12:14 – 00:37:14:04
to be brought to attention.

00:37:14:04 – 00:37:15:08
And as these laws and

00:37:15:08 – 00:37:16:13
policies are developed.

00:37:18:11 – 00:37:21:11
Next slide, please.

00:37:24:21 – 00:37:25:08
And as we

00:37:25:08 – 00:37:26:09
get close to wrapping up,

00:37:26:09 – 00:37:28:13
I’d like to make the point that I by

00:37:28:13 – 00:37:30:04
I will probably never be able

00:37:30:04 – 00:37:31:22
to totally replace

00:37:31:22 – 00:37:32:16
humans

00:37:32:16 – 00:37:35:16
because of the interactivity issues.

00:37:35:20 – 00:37:37:01
Often we can predict

00:37:37:01 – 00:37:39:00
there would be misunderstandings

00:37:39:00 – 00:37:42:00
and so we need to have that deep

00:37:42:05 – 00:37:44:03
human knowledge there.

00:37:44:03 – 00:37:47:03
So we’ll have to look for

00:37:47:15 – 00:37:50:00
a human in the loop design

00:37:50:00 – 00:37:51:06
as this technology

00:37:51:06 – 00:37:53:18
is developed over time.

00:37:53:18 – 00:37:57:03
And in terms of human in the loop design

00:37:57:05 – 00:38:00:05
to explain a bit about about that,

00:38:00:12 – 00:38:01:22
of course we would have the A.I.

00:38:01:22 – 00:38:05:03
technology and often

00:38:05:03 – 00:38:08:03
we see that I, I get information

00:38:08:08 – 00:38:10:05
through different sources,

00:38:10:05 – 00:38:12:14
but we want to be able to emphasize

00:38:12:14 – 00:38:14:12
the importance of A.I.

00:38:14:12 – 00:38:16:00
getting feedback

00:38:16:00 – 00:38:19:23
from human interactivity.

00:38:20:12 – 00:38:22:08
There needs to be a feedback loop

00:38:22:08 – 00:38:23:07
food feedback loop

00:38:23:07 – 00:38:24:05
where humans are always

00:38:24:05 – 00:38:27:12
involved in this process so that

00:38:28:05 – 00:38:30:05
the design and the development

00:38:30:05 – 00:38:31:24
and the operation

00:38:31:24 – 00:38:33:22
and everything is verified

00:38:33:22 – 00:38:34:21
by humans

00:38:34:21 – 00:38:37:02
who are involved with the project.

00:38:37:02 – 00:38:38:17
That would be the end goal.

00:38:38:17 – 00:38:39:18
Next slide, please.

00:38:45:23 – 00:38:46:17
Hello everyone.

00:38:46:17 – 00:38:50:03
I’m back so I would like to

00:38:50:05 – 00:38:53:22
we have the advisory group on AI

00:38:53:22 – 00:38:55:02
and final language interpreting

00:38:55:02 – 00:38:56:00
and this has been

00:38:56:00 – 00:38:57:24
there’s been so many

00:38:57:24 – 00:38:59:06
benefits to this group,

00:38:59:06 – 00:39:00:15
so much AB many hours

00:39:00:15 – 00:39:02:18
and work has gone into this.

00:39:02:18 – 00:39:05:15
And so this isn’t the end of our work.

00:39:05:15 – 00:39:07:00
This is just the springboard

00:39:07:00 – 00:39:08:15
to the future for more discussions

00:39:08:15 – 00:39:09:11
and more partnerships

00:39:09:11 – 00:39:11:00
with the community at large.

00:39:11:00 – 00:39:13:05
So we have an event to share with you.

00:39:13:05 – 00:39:14:21
A Save the Date.

00:39:14:21 – 00:39:17:21
We have a symposium

00:39:18:02 – 00:39:21:02
on AI and sign language interpreting

00:39:21:14 – 00:39:23:10
and it will be hosted on April

00:39:23:10 – 00:39:27:08
20th and 21st is a Saturday and Sunday

00:39:27:08 – 00:39:28:11
this year

00:39:28:11 – 00:39:31:11
and will be here at Brown University.

00:39:32:21 – 00:39:35:04
And we also will have

00:39:35:04 – 00:39:37:13
accessibility options to join us

00:39:37:13 – 00:39:38:14
through Zoom.

00:39:38:14 – 00:39:40:16
Anyone can join from anywhere

00:39:40:16 – 00:39:43:09
we are planning to on Saturday.

00:39:43:09 – 00:39:46:16
It will be from 9 to 6 and Sunday 9

00:39:46:16 – 00:39:49:16
to 2, just a half day on Sunday.

00:39:49:22 – 00:39:52:00
And this is Eastern Standard Time.

00:39:52:24 – 00:39:53:21
This is very

00:39:53:21 – 00:39:55:02
important for us

00:39:55:02 – 00:39:57:20
to bring in different perspectives,

00:39:57:20 – 00:40:00:08
different experts and different people

00:40:00:08 – 00:40:02:16
who have in-depth experience in A.I.

00:40:02:16 – 00:40:04:10
and sign language interpreting

00:40:04:10 – 00:40:05:07
so we can really have

00:40:05:07 – 00:40:06:15
an in-depth discussion

00:40:06:15 – 00:40:08:02
on what this looks like.

00:40:08:02 – 00:40:09:15
And it’s also an opportunity

00:40:09:15 – 00:40:11:12
for us to do a deeper dive

00:40:11:12 – 00:40:12:21
into what our presenters

00:40:12:21 – 00:40:13:13
have really talked

00:40:13:13 – 00:40:14:21
about during this session

00:40:14:21 – 00:40:15:18
as well,

00:40:15:18 – 00:40:17:21
to flesh out different topics and issues

00:40:17:21 – 00:40:18:20
that may arise.

00:40:18:20 – 00:40:20:01
I to see you there.

00:40:20:01 – 00:40:23:01
I’m very excited about it.

00:40:23:11 – 00:40:24:15
I’m going to go ahead

00:40:24:15 – 00:40:27:15
and turn this over to Anne Marie.

00:40:28:03 – 00:40:30:13
Thank you so much, Tim.

00:40:30:13 – 00:40:32:20
So I would just like to add

00:40:32:20 – 00:40:34:11
a little bit more information

00:40:34:11 – 00:40:35:21
for your awareness.

00:40:35:21 – 00:40:38:21
And in terms of the participations,

00:40:39:03 – 00:40:42:08
the participants, we did have 300

00:40:42:20 – 00:40:44:21
people come in that participated

00:40:44:21 – 00:40:46:11
during these webinars.

00:40:46:11 – 00:40:48:15
So we had a very great showing for that

00:40:48:15 – 00:40:50:12
and we were on Zoom,

00:40:50:12 – 00:40:52:05
so we were able to see

00:40:52:05 – 00:40:54:04
a lot of comments in the Q&A.

00:40:54:04 – 00:40:55:21
We saw lots of questions come through,

00:40:55:21 – 00:40:57:05
which was great.

00:40:57:05 – 00:41:00:09
And so after this meeting today,

00:41:00:11 – 00:41:01:15
we can share

00:41:01:15 – 00:41:02:16
some more information with you.

00:41:02:16 – 00:41:04:02
But we had a lot of people

00:41:04:02 – 00:41:06:17
that were coming in

00:41:06:17 – 00:41:09:08
for this research, for these webinars,

00:41:11:08 – 00:41:14:17
and we had eight we had 98%

00:41:14:17 – 00:41:16:12
of the people who participated

00:41:16:12 – 00:41:18:05
sign and agreed to be able

00:41:18:05 – 00:41:20:01
to share their information.

00:41:20:01 – 00:41:25:10
And we also had ASL involved

00:41:25:10 – 00:41:26:24
for all of those participant

00:41:26:24 – 00:41:29:24
participants out of 55,

00:41:31:01 – 00:41:32:00
out of the fit

00:41:32:00 – 00:41:32:20
out of the people

00:41:32:20 – 00:41:35:20
that were on the forum out of,

00:41:36:06 – 00:41:38:18
we had 98% of the people

00:41:38:18 – 00:41:40:07
who were involved agreed

00:41:40:07 – 00:41:43:07
to share the information

00:41:47:00 – 00:41:50:00
and to add to that

00:41:51:05 – 00:41:54:05
in terms of the topics and

00:41:54:15 – 00:41:56:15
what we what we were talking about,

00:41:56:15 – 00:41:58:22
we talked about the values and these

00:41:58:22 – 00:42:01:22
and the perspectives of the community.

00:42:03:15 – 00:42:04:19
We talked about research

00:42:04:19 – 00:42:05:23
findings

00:42:05:23 – 00:42:07:16
and the level of participation

00:42:07:16 – 00:42:08:10
was just great.

00:42:08:10 – 00:42:10:23
We were able to do a lot of research

00:42:10:23 – 00:42:13:13
and go through those topics

00:42:13:13 – 00:42:14:15
and get the specifics

00:42:14:15 – 00:42:15:20
from the participants

00:42:15:20 – 00:42:18:24
so that we had very clear results.

00:42:19:07 – 00:42:20:11
Next slide, please.

00:42:28:01 – 00:42:29:21
And in terms of the

00:42:29:21 – 00:42:31:16
discussion we were on

00:42:31:16 – 00:42:34:03
for about 173 minutes

00:42:34:03 – 00:42:37:10
and we were able to show

00:42:37:11 – 00:42:39:07
a lot of in-depth discussion

00:42:39:07 – 00:42:41:11
and comments that came through.

00:42:41:11 – 00:42:43:22
It gave a lot of value

00:42:43:22 – 00:42:44:21
to our discussion.

00:42:44:21 – 00:42:47:00
We had quite a bit of participation

00:42:47:00 – 00:42:48:18
and consideration to go over

00:42:48:18 – 00:42:50:04
and some of the topics

00:42:50:04 – 00:42:52:12
that were discussed really focused.

00:42:52:12 – 00:42:55:10
We talked about some of those earlier.

00:42:55:10 – 00:42:56:01
For example,

00:42:56:01 – 00:42:57:04
we talked about deaf

00:42:57:04 – 00:42:59:09
community readiness for AI.

00:42:59:09 – 00:43:00:14
Is the deaf community

00:43:00:14 – 00:43:02:04
actually ready for this?

00:43:02:04 – 00:43:06:09
So these discussions were so important

00:43:06:09 – 00:43:08:01
and I think having the participants

00:43:08:01 – 00:43:10:03
there to talk about AI

00:43:10:03 – 00:43:11:06
and to consider

00:43:11:06 – 00:43:11:18
whether or not

00:43:11:18 – 00:43:12:19
they were ready

00:43:12:19 – 00:43:15:21
to have this method of communication

00:43:15:21 – 00:43:19:14
as part of their life, to have AI there.

00:43:20:03 – 00:43:20:23
They discussed whether or

00:43:20:23 – 00:43:21:24
not they were ready for that.

00:43:23:06 – 00:43:24:14
And many of the

00:43:24:14 – 00:43:27:14
participants, 17

00:43:29:17 – 00:43:30:23
many of the participants

00:43:30:23 – 00:43:33:23
that were there from the advisory group,

00:43:37:21 – 00:43:38:14
were involved

00:43:38:14 – 00:43:41:14
in this process.

00:43:43:13 – 00:43:46:13
And

00:43:55:09 – 00:43:56:02
let me go back

00:43:56:02 – 00:43:59:02
for a second.

00:44:08:21 – 00:44:09:03
Okay.

00:44:09:03 – 00:44:12:03
Going back a bit to the process,

00:44:15:23 – 00:44:18:23
the discussion went on for 173 minutes

00:44:19:10 – 00:44:20:13
and we were able

00:44:20:13 – 00:44:22:01
to share different comments.

00:44:22:01 – 00:44:24:18
The participants went over

00:44:24:18 – 00:44:26:08
the things that we talked about today

00:44:26:08 – 00:44:29:08
and also

00:44:30:09 – 00:44:32:18
hashtag Save death.

00:44:32:18 – 00:44:37:06
I became a hashtag that we used

00:44:37:06 – 00:44:40:06
and we got a lot of feedback

00:44:41:21 – 00:44:44:18
and also recommendations.

00:44:44:18 – 00:44:46:16
And we talked about the broader influence

00:44:46:16 – 00:44:49:16
that this will have on the community.

00:44:55:22 – 00:44:56:09
We talked

00:44:56:09 – 00:44:59:09
about the organizations,

00:45:00:19 – 00:45:02:20
and these discussions were very important

00:45:02:20 – 00:45:05:06
to talk about collecting the data

00:45:05:06 – 00:45:08:06
and the process

00:45:09:07 – 00:45:10:01
of.

00:45:10:01 – 00:45:11:06
Next slide, please.

00:45:31:09 – 00:45:32:02
Okay.

00:45:32:02 – 00:45:33:17
So we had three

00:45:33:17 – 00:45:34:15
categories

00:45:34:15 – 00:45:36:02
to flesh out

00:45:36:02 – 00:45:38:00
all of this information through.

00:45:38:00 – 00:45:40:04
We wanted to go through

00:45:40:04 – 00:45:44:00
and with our advisory group members and

00:45:44:00 – 00:45:47:20
we decided for more thematic

00:45:47:20 – 00:45:51:06
areas of research as a team had arisen.

00:45:51:16 – 00:45:53:04
All six of them were shown

00:45:53:04 – 00:45:56:04
in the first passcode book,

00:45:56:12 – 00:45:58:04
and now we’re going to add

00:45:58:04 – 00:46:01:10
that to what we’ll send out to all.

00:46:01:17 – 00:46:04:03
And that’s also included in the report.

00:46:04:03 – 00:46:07:14
I think I saw some questions in the Q A

00:46:07:16 – 00:46:10:16
It is available online.

00:46:10:20 – 00:46:12:22
It will be available

00:46:12:22 – 00:46:16:00
as we’re doing an invitation process

00:46:16:00 – 00:46:19:23
to add that to our website.

00:46:30:18 – 00:46:32:05
I

00:46:32:05 – 00:46:33:03
do you want me to

00:46:33:03 – 00:46:36:03
move on to the next slide?

00:46:36:06 – 00:46:37:04
I think we’re at the end.

00:46:37:04 – 00:46:37:13
Okay.

00:47:00:05 – 00:47:03:05
So

00:47:05:07 – 00:47:06:08
I’m saying if the team

00:47:06:08 – 00:47:08:12
could all turn on their cameras,

00:47:08:12 – 00:47:11:12
we’ll start in with the Q&A.

00:47:16:08 – 00:47:16:19
All right.

00:47:16:19 – 00:47:17:20
Are we ready to address

00:47:17:20 – 00:47:20:03
the questions in the Q&A?

00:47:20:03 – 00:47:22:08
Let’s do it, though.

00:47:22:08 – 00:47:23:21
The first thing I’d like to do

00:47:23:21 – 00:47:25:19
is thank you all for your questions.

00:47:25:19 – 00:47:28:19
Thank you so very much.

00:47:29:04 – 00:47:29:24
The first question

00:47:29:24 – 00:47:32:24
that we have for the panel

00:47:34:01 – 00:47:35:16
in the future,

00:47:35:16 – 00:47:38:16
say 10 to 15 years down the road,

00:47:38:20 – 00:47:39:08
A.I.

00:47:39:08 – 00:47:42:08
by A.I., what will that look like?

00:47:43:21 – 00:47:46:21
How many applications

00:47:46:22 – 00:47:48:12
are possible?

00:47:48:12 – 00:47:53:08
For example, TV theaters, movie theaters?

00:47:53:19 – 00:47:55:16
Will there be an automatic interpreter

00:47:55:16 – 00:47:58:16
pop up on the screen?

00:48:00:08 – 00:48:01:12
What do you think

00:48:01:12 – 00:48:02:24
is the potential for A.I.

00:48:02:24 – 00:48:05:24
in the future?

00:48:08:21 – 00:48:10:14
Jeff Would you like to go?

00:48:10:14 – 00:48:13:05
Jeff saying yes, I will talk about that.

00:48:13:05 – 00:48:13:24
I bet

00:48:13:24 – 00:48:15:19
I will have so many applications

00:48:15:19 – 00:48:16:14
in the future.

00:48:16:14 – 00:48:18:07
I think in 10 to 15 years,

00:48:18:07 – 00:48:20:09
the possibilities are astounding.

00:48:20:09 – 00:48:23:23
There’s you know, it I would say that

00:48:24:00 – 00:48:25:08
computes two equates

00:48:25:08 – 00:48:28:00
to about 100 years of human development

00:48:28:00 – 00:48:29:06
because technology moves

00:48:29:06 – 00:48:30:21
at such a rapid pace.

00:48:30:21 – 00:48:31:10
One thing

00:48:31:10 – 00:48:33:03
I would like to say that for sure,

00:48:33:03 – 00:48:34:19
I know that it will have improved

00:48:34:19 – 00:48:36:02
drastically by then

00:48:36:02 – 00:48:38:00
because I think it’s going to continue

00:48:38:00 – 00:48:39:16
marching on

00:48:39:16 – 00:48:41:14
and improving as time goes by.

00:48:41:14 – 00:48:43:12
Now, will we be more trusting

00:48:43:12 – 00:48:44:18
of these applications

00:48:44:18 – 00:48:47:18
in the future and more confident with it?

00:48:47:19 – 00:48:51:10
I think in low risk situations,

00:48:52:22 – 00:48:54:14
I don’t think it should be too bad.

00:48:54:14 – 00:48:57:14
For example,

00:48:57:14 – 00:48:59:18
not a police encounter,

00:48:59:18 – 00:49:00:17
medical encounter

00:49:00:17 – 00:49:03:20
or anything of that sort, but I bet

00:49:03:20 – 00:49:06:12
I could be used for automatic

00:49:06:12 – 00:49:08:09
conversation and dialogs like,

00:49:08:09 – 00:49:10:04
you know, with robocalls,

00:49:10:04 – 00:49:12:14
sharing information,

00:49:12:14 – 00:49:13:24
having something set,

00:49:13:24 – 00:49:17:13
you know, an automated system.

00:49:18:03 – 00:49:19:10
But in the future

00:49:19:10 – 00:49:22:10
I foresee more captioning being involved,

00:49:22:17 – 00:49:24:20
not necessarily ASL only,

00:49:24:20 – 00:49:26:19
but I think captioning

00:49:26:19 – 00:49:29:16
and multilingual captioning as a whole

00:49:29:16 – 00:49:32:06
will have developed so much over time.

00:49:32:06 – 00:49:33:21
I think there are so many possibilities

00:49:33:21 – 00:49:34:16
and different directions

00:49:34:16 – 00:49:35:23
it could go again.

00:49:35:23 – 00:49:37:11
It’s hard to predict everything

00:49:37:11 – 00:49:38:19
that may happen in the future,

00:49:38:19 – 00:49:39:20
but in a nutshell,

00:49:39:20 – 00:49:42:01
i believe that’s what would happen

00:49:42:01 – 00:49:45:03
and I think that some examples of that

00:49:45:08 – 00:49:46:13
do so.

00:49:46:13 – 00:49:47:06
For example,

00:49:47:06 – 00:49:49:21
when you’re driving through a place

00:49:49:21 – 00:49:51:20
and you want to order coffee

00:49:51:20 – 00:49:53:12
or you want to order food

00:49:53:12 – 00:49:55:22
and that kind of situation,

00:49:55:22 – 00:49:58:13
and when you’re ordering your food,

00:49:58:13 – 00:49:59:00
you know

00:50:00:04 – 00:50:00:14
it.

00:50:00:14 – 00:50:01:05
It’s not

00:50:01:05 – 00:50:02:03
going to be something

00:50:02:03 – 00:50:03:10
that’s disastrous to your life

00:50:03:10 – 00:50:05:00
if something messes up.

00:50:05:00 – 00:50:06:20
But when it comes to the barriers,

00:50:06:20 – 00:50:09:01
the deaf community experiences

00:50:09:01 – 00:50:10:12
in other situations,

00:50:10:12 – 00:50:12:06
there’s so many different possibilities

00:50:12:06 – 00:50:13:17
of how this could go.

00:50:13:17 – 00:50:15:04
And I think, again,

00:50:15:04 – 00:50:17:14
we have to make sure that the technology

00:50:17:14 – 00:50:19:08
is ready, verified

00:50:19:08 – 00:50:21:16
and it’s going to be more beneficial

00:50:21:16 – 00:50:23:18
and and not harmful.

00:50:23:18 – 00:50:25:18
I think that’s what we have to see,

00:50:25:18 – 00:50:27:02
you know?

00:50:27:02 – 00:50:27:19
Theresa here,

00:50:27:19 – 00:50:29:03
I’d like to make a comment as well,

00:50:29:03 – 00:50:30:15
and I’d like to emphasize the point

00:50:30:15 – 00:50:34:01
about low risk situations for a moment.

00:50:34:01 – 00:50:35:06
I think that’s important.

00:50:35:06 – 00:50:37:08
It’s imperative for us to realize

00:50:37:08 – 00:50:38:23
that we always have a need

00:50:38:23 – 00:50:41:23
for human interpreters,

00:50:41:24 – 00:50:43:12
specific situations

00:50:43:12 – 00:50:44:24
like medical situations,

00:50:44:24 – 00:50:47:06
legal situations, court

00:50:47:06 – 00:50:49:04
law enforcement interactions.

00:50:49:04 – 00:50:50:07
It’s really imperative

00:50:50:07 – 00:50:51:24
that we include the human aspect

00:50:51:24 – 00:50:54:08
and human judgment in those situations

00:50:55:19 – 00:50:56:24
in this century.

00:50:56:24 – 00:51:00:19
And to add to that, I think one thing

00:51:00:19 – 00:51:01:16
that we also have to

00:51:01:16 – 00:51:04:16
emphasize is knowing the language itself

00:51:04:19 – 00:51:07:06
and knowing that process,

00:51:07:06 – 00:51:09:14
whether the responses

00:51:09:14 – 00:51:11:00
and also looking at

00:51:11:00 – 00:51:12:22
whether it’s a live person,

00:51:12:22 – 00:51:16:06
if it’s going to be a mixed situation,

00:51:16:10 – 00:51:17:15
we have to recognize

00:51:17:15 – 00:51:18:23
exactly what is involved

00:51:18:23 – 00:51:20:21
situation by situation

00:51:20:21 – 00:51:24:08
so that we can make sure that the access

00:51:24:08 – 00:51:25:21
that’s being provided is as close

00:51:25:21 – 00:51:27:17
to 100% accurate as possible.

00:51:27:17 – 00:51:29:16
So like Theresa said, humans

00:51:29:16 – 00:51:31:13
have to be involved in this

00:51:31:13 – 00:51:33:05
and in this feedback loop,

00:51:33:05 – 00:51:35:15
and it’s very critical that we

00:51:35:15 – 00:51:37:00
emphasize that.

00:51:37:00 – 00:51:37:16
Theresa saying,

00:51:37:16 – 00:51:39:01
I’d like to add a little bit more.

00:51:39:01 – 00:51:40:01
One thing to think about

00:51:40:01 – 00:51:41:12
is in my past experience,

00:51:41:12 – 00:51:45:13
for many years, living in Mexico,

00:51:45:13 – 00:51:47:00
the deaf population

00:51:47:00 – 00:51:48:20
in the community, in New Mexico,

00:51:48:20 – 00:51:51:22
New Mexico, you know,

00:51:52:19 – 00:51:56:00
they were able to be

00:51:56:00 – 00:51:57:15
mainstreamed into the school

00:51:59:14 – 00:52:00:20
and the dialects are

00:52:00:20 – 00:52:02:22
different in different areas.

00:52:02:22 – 00:52:05:06
I may not recognize that.

00:52:05:06 – 00:52:06:14
So growing up in those schools

00:52:06:14 – 00:52:08:13
where I where the communities were small,

00:52:08:13 – 00:52:10:07
the dialects were diverse,

00:52:10:07 – 00:52:12:21
I may not have that capability

00:52:12:21 – 00:52:15:05
just yet to recognize that.

00:52:15:05 – 00:52:17:17
So we need to make sure that our air

00:52:17:17 – 00:52:20:17
is ready for this specifically,

00:52:20:18 – 00:52:21:14
for example,

00:52:21:14 – 00:52:24:14
with our black deaf signers,

00:52:24:21 – 00:52:26:08
the American sign language

00:52:26:08 – 00:52:27:02
that they use

00:52:27:02 – 00:52:28:17
is so culturally diverse

00:52:28:17 – 00:52:30:12
than American Sign language.

00:52:30:12 – 00:52:32:00
We have to make sure

00:52:32:00 – 00:52:32:19
that we have the right

00:52:32:19 – 00:52:35:10
understanding of interpreting.

00:52:35:10 – 00:52:38:10
And for this approach.

00:52:43:14 – 00:52:44:03
Yes.

00:52:44:03 – 00:52:46:19
And more questions.

00:52:46:19 – 00:52:47:17
Very exciting.

00:52:47:17 – 00:52:49:12
Okay, so another question.

00:52:49:12 – 00:52:52:22
Question number two is related

00:52:52:22 – 00:52:55:22
to the term

00:52:56:06 – 00:52:59:06
sign language

00:53:03:19 – 00:53:05:01
and atomization.

00:53:05:01 – 00:53:08:01

00:53:09:05 – 00:53:09:23
So basically

00:53:09:23 – 00:53:12:23
related to privacy,

00:53:13:03 – 00:53:15:23
protecting individuals and their data.

00:53:18:00 – 00:53:19:14
So from

00:53:19:14 – 00:53:20:21
the research and the report

00:53:20:21 – 00:53:22:15
that you all produced,

00:53:22:15 – 00:53:24:20
can you guys discuss and share a bit

00:53:24:20 – 00:53:27:20
about the data privacy

00:53:28:18 – 00:53:30:06
of confidentiality

00:53:30:06 – 00:53:33:14
and also for automatic interpretation,

00:53:33:23 – 00:53:36:24
would you use an avatar and

00:53:37:10 – 00:53:38:13
what does this look like?

00:53:38:13 – 00:53:39:22
How would you be able to

00:53:39:22 – 00:53:41:06
represent individuals

00:53:41:06 – 00:53:44:06
while also protecting them?

00:53:44:09 – 00:53:44:24
Jeff Here

00:53:44:24 – 00:53:46:03
I would like to take that one,

00:53:46:03 – 00:53:47:18
if that’s all right with the group.

00:53:47:18 – 00:53:50:12
So I prefer to sign

00:53:50:12 – 00:53:51:20
automatic interpreting

00:53:51:20 – 00:53:53:14
for data collection.

00:53:53:14 – 00:53:56:00
The faith is involved in that.

00:53:56:00 – 00:53:58:21
So one aspect of data collection is worth

00:53:58:21 – 00:54:00:05
signing is

00:54:00:05 – 00:54:01:09
they don’t have our faith,

00:54:01:09 – 00:54:02:18
and it’s imperative

00:54:02:18 – 00:54:05:15
to compare that with speech recognition

00:54:05:15 – 00:54:07:06
through automatic speech recognition.

00:54:07:06 – 00:54:08:23
You have voice and intonation,

00:54:08:23 – 00:54:09:15
but with signing,

00:54:09:15 – 00:54:11:06
if you don’t include the faith,

00:54:11:06 – 00:54:12:22
that’s a deep part of the language

00:54:12:22 – 00:54:14:15
that’s missing itself.

00:54:14:15 – 00:54:18:16
So there’s no real easy way to avoid that

00:54:18:16 – 00:54:20:24
with data collection,

00:54:20:24 – 00:54:22:23
because we have to have our faith

00:54:22:23 – 00:54:25:13
for the language foundation and tone

00:54:25:13 – 00:54:27:02
and meaning to be there.

00:54:27:02 – 00:54:28:13
So we have to be very careful

00:54:28:13 – 00:54:30:08
with the data itself.

00:54:30:08 – 00:54:31:24
We have to protect that

00:54:31:24 – 00:54:32:12
to make sure

00:54:32:12 – 00:54:35:03
that people are fully informed.

00:54:35:03 – 00:54:37:10
Now, informed consent,

00:54:37:10 – 00:54:38:22
we’re going to use that in the training,

00:54:38:22 – 00:54:40:02
in AI by AI.

00:54:40:02 – 00:54:41:20
We will be talking about that

00:54:41:20 – 00:54:42:23
facial recognition

00:54:42:23 – 00:54:44:23
and other identifying information.

00:54:44:23 – 00:54:47:07
My background may be there as well.

00:54:47:07 – 00:54:49:22
So we have to really filter out

00:54:49:22 – 00:54:51:04
that information

00:54:51:04 – 00:54:54:03
and see what part of that is protected

00:54:54:03 – 00:54:56:11
and what part of it is stored.

00:54:56:11 – 00:54:58:00
And we also have to think about

00:54:58:00 – 00:55:00:06
what is private.

00:55:00:06 – 00:55:02:21
And with avatars, you know,

00:55:02:21 – 00:55:03:22
it may be possible

00:55:03:22 – 00:55:06:11
to produce signs with avatars,

00:55:06:11 – 00:55:08:03
but it could be a little bit off.

00:55:08:03 – 00:55:11:03
For example, the avatar.

00:55:11:07 – 00:55:13:14
What can we train that avatar

00:55:13:14 – 00:55:14:18
to make the signs?

00:55:14:18 – 00:55:16:24
And can we identify that

00:55:16:24 – 00:55:18:02
and know that that’s

00:55:18:02 – 00:55:20:03
what the person is signing?

00:55:20:03 – 00:55:21:23
That can be a little bit ambiguous

00:55:21:23 – 00:55:23:00
in trying to identify

00:55:23:00 – 00:55:26:15
what the data and the the machine

00:55:26:15 – 00:55:29:22
learning is actually tracking in.

00:55:31:18 – 00:55:34:15
All of that goes back to

00:55:34:15 – 00:55:37:15
the subject of informed consent.

00:55:39:02 – 00:55:40:23
And I’d like to add

00:55:40:23 – 00:55:42:17
to that one more thing.

00:55:42:17 – 00:55:45:18
In terms of informed consent, often

00:55:45:18 – 00:55:47:07
we think, okay,

00:55:47:07 – 00:55:48:20
this is just a one time thing

00:55:48:20 – 00:55:51:08
I’m signing and I’m giving my consent,

00:55:51:08 – 00:55:52:24
but informed consent

00:55:52:24 – 00:55:54:08
really needs to happen

00:55:54:08 – 00:55:56:03
on an ongoing basis.

00:55:56:03 – 00:55:57:17
We need to be reminded

00:55:57:17 – 00:55:58:20
and we need to make sure

00:55:58:20 – 00:56:01:06
that we continue to give that consent

00:56:01:06 – 00:56:04:07
and continue to agree and remind people

00:56:04:13 – 00:56:06:11
that they have permission

00:56:06:11 – 00:56:07:13
to remove themselves

00:56:07:13 – 00:56:09:14
or to take that consent back.

00:56:09:14 – 00:56:09:23

00:56:09:23 – 00:56:10:19
And so they have

00:56:10:19 – 00:56:11:16
the right to be involved.

00:56:11:16 – 00:56:14:16
They had the right to decline.

00:56:19:04 – 00:56:20:09
Okay.

00:56:20:09 – 00:56:21:19
QUESTION

00:56:21:19 – 00:56:23:01
Thank you so much for your question.

00:56:23:01 – 00:56:23:21
So the next question

00:56:23:21 – 00:56:24:21
we’re going to address

00:56:24:21 – 00:56:28:01
is I’d like to add a little bit to

00:56:28:01 – 00:56:29:23
this as well.

00:56:32:08 – 00:56:35:08
The discussion about

00:56:35:23 – 00:56:38:23
I’m sorry, the discussion about how

00:56:40:18 – 00:56:43:24
this task force and this advisory group

00:56:44:05 – 00:56:45:20
has collaborated

00:56:45:20 – 00:56:48:02
with other organizations,

00:56:48:02 – 00:56:50:15
the NAD Gallaudet University,

00:56:50:15 – 00:56:53:18
other educational bodies.

00:56:53:18 – 00:56:55:10
What does that look like?

00:56:55:10 – 00:56:58:10
What does your partnership look like?

00:57:02:05 – 00:57:04:02
And I can answer that question.

00:57:04:02 – 00:57:08:15
And so I think that first of all,

00:57:09:03 – 00:57:09:24
the advisory

00:57:09:24 – 00:57:12:07
council is really a device,

00:57:12:07 – 00:57:13:18
a diverse group.

00:57:13:18 – 00:57:16:14
We welcome people

00:57:16:14 – 00:57:18:15
from various organizations

00:57:18:15 – 00:57:21:06
to make sure that we’re reflecting

00:57:21:06 – 00:57:23:00
various people, like, for example,

00:57:23:00 – 00:57:24:22
from Gallaudet University,

00:57:24:22 – 00:57:28:16
people from Nairobi, people from Nadi.

00:57:29:00 – 00:57:32:00
We have participants from all over, and

00:57:32:06 – 00:57:33:18
I think that’s important.

00:57:33:18 – 00:57:36:01
But also we want to recognize

00:57:36:01 – 00:57:37:06
that we want to continue

00:57:37:06 – 00:57:40:22
to involve individuals like, for example,

00:57:40:22 – 00:57:45:10
management.

00:57:45:10 – 00:57:47:03
Omar That’s another one.

00:57:47:03 – 00:57:51:12
We want to have diverse representation

00:57:51:12 – 00:57:52:21
to be able to continue

00:57:52:21 – 00:57:54:14
to working with everyone,

00:57:54:14 – 00:57:56:22
because there are groups that have been

00:57:56:22 – 00:57:59:15
have not been included

00:57:59:15 – 00:58:01:05
in these types of processes in the past.

00:58:01:05 – 00:58:03:12
So we want to make sure that our research

00:58:03:12 – 00:58:05:06
and our study can continue

00:58:05:06 – 00:58:06:17
that kind of collaboration

00:58:06:17 – 00:58:08:06
that we’ve already established.

00:58:08:06 – 00:58:08:22
Like I said,

00:58:08:22 – 00:58:12:07
whether it be with Gallaudet University,

00:58:12:07 – 00:58:13:09
other organizations, I’m

00:58:13:09 – 00:58:14:04
not sure if anyone else

00:58:14:04 – 00:58:15:20
has something to add to that comment.

00:58:17:23 – 00:58:19:07
I’d like to add to the comment.

00:58:19:07 – 00:58:20:15
So we have individuals

00:58:20:15 – 00:58:23:15
with specific skills and backgrounds.

00:58:23:15 – 00:58:27:08
We have work where, you know, I myself

00:58:27:08 – 00:58:28:19
work at Gallaudet University.

00:58:28:19 – 00:58:32:01
I’m a research focused on bioethics

00:58:32:01 – 00:58:35:12
and I volunteer to participate

00:58:35:12 – 00:58:37:09
as an individual researcher.

00:58:37:09 – 00:58:38:15
I’m not representing

00:58:38:15 – 00:58:39:14
Gallaudet University,

00:58:39:14 – 00:58:40:13
but at the same time,

00:58:40:13 – 00:58:41:24
we do have discussions

00:58:41:24 – 00:58:44:24
with many people in Gallaudet University

00:58:45:09 – 00:58:47:03
throughout different areas,

00:58:47:03 – 00:58:48:23
recognizing the importance of this.

00:58:48:23 – 00:58:50:01
And sometimes we have a person

00:58:50:01 – 00:58:51:08
who’s willing to work

00:58:51:08 – 00:58:53:06
who is working for Gallaudet University,

00:58:53:06 – 00:58:54:10
but they’re not representing

00:58:54:10 – 00:58:55:24
the university itself

00:58:55:24 – 00:58:58:07
in their role in this research.

00:58:58:07 – 00:59:00:14
So in my opinion, my research

00:59:00:14 – 00:59:01:21
and what I’m looking at

00:59:01:21 – 00:59:03:19
is not representing the university

00:59:03:19 – 00:59:05:20
at large itself.

00:59:05:20 – 00:59:07:18
So I hope that clarifies that

00:59:07:18 – 00:59:08:14
to a degree.

00:59:11:14 – 00:59:13:19
This is Anne-Marie and

00:59:13:19 – 00:59:17:09
I think in terms of education

00:59:17:09 – 00:59:20:11
and nonprofit groups and organizations,

00:59:20:11 – 00:59:21:22
as an ADDY

00:59:21:22 – 00:59:23:00
and other organizations

00:59:23:00 – 00:59:24:14
that we’ve had involved,

00:59:24:14 – 00:59:26:15
these different signing

00:59:26:15 – 00:59:29:03
groups are working closely together,

00:59:29:03 – 00:59:32:03
different companies who have technology.

00:59:32:08 – 00:59:33:19
And I’d like to emphasize

00:59:33:19 – 00:59:36:22
that we are all working together

00:59:37:06 – 00:59:40:22
and that the thing is to have

00:59:41:03 – 00:59:42:14
a platform for discussion

00:59:42:14 – 00:59:44:00
for the deaf community.

00:59:44:00 – 00:59:45:08
And I know often

00:59:45:08 – 00:59:46:07
a lot of individuals

00:59:46:07 – 00:59:47:21
are overlooked in our community

00:59:47:21 – 00:59:48:15
are they’re pushed

00:59:48:15 – 00:59:50:15
out of these discussions.

00:59:50:15 – 00:59:51:16
They don’t get the opportunity

00:59:51:16 – 00:59:53:12
to explain their perspective.

00:59:53:12 – 00:59:55:04
There’s education issues.

00:59:55:04 – 00:59:59:20
And so, you know, I think that A.I.

01:00:00:00 – 01:00:02:06
here and we often see people say,

01:00:02:06 – 01:00:03:23
you know, AI is here.

01:00:03:23 – 01:00:05:13
What is that going to look like?

01:00:05:13 – 01:00:06:23
And some people say, no,

01:00:06:23 – 01:00:08:03
we don’t want to see this happen.

01:00:08:03 – 01:00:09:09
We don’t want A.I.

01:00:09:09 – 01:00:11:21
to part of this interpreting process

01:00:11:21 – 01:00:14:11
while we know it’s coming.

01:00:14:11 – 01:00:16:11
And so I think it’s our responsibility

01:00:16:11 – 01:00:18:21
to ensure that this collaborative effort

01:00:18:21 – 01:00:20:17
stays in place

01:00:20:17 – 01:00:21:24
so that we have deaf community

01:00:21:24 – 01:00:23:18
representation and representation

01:00:23:18 – 01:00:26:00
in the development of AI over time,

01:00:26:00 – 01:00:27:16
not just for education,

01:00:27:16 – 01:00:30:16
but also that these all over America,

01:00:30:21 – 01:00:32:04
they have the responsibility

01:00:32:04 – 01:00:34:08
of ensuring that deaf individuals are

01:00:34:08 – 01:00:35:07
they’re at the table

01:00:35:07 – 01:00:36:14
when these discussions happen.

01:00:40:00 – 01:00:42:16
Let’s move on to the next question.

01:00:42:16 – 01:00:42:23
Okay.

01:00:42:23 – 01:00:44:21
So for our next question,

01:00:44:21 – 01:00:47:14
what are we working on related

01:00:47:14 – 01:00:50:20
to the legislation

01:00:50:20 – 01:00:53:20
and for protection of the deaf community?

01:00:54:07 – 01:00:57:06
What is what does that look like?

01:00:57:06 – 01:00:58:18
I could take that.

01:00:58:18 – 01:01:01:19
So in terms of this, we

01:01:02:22 – 01:01:04:11
I can maybe probably clarify

01:01:04:11 – 01:01:05:13
a bit of the explanation

01:01:05:13 – 01:01:07:07
we’ve given already, but

01:01:07:07 – 01:01:08:24
when it comes to structure,

01:01:08:24 – 01:01:11:06
we do have our advisory group.

01:01:11:06 – 01:01:14:06
We also have safe A.I..

01:01:14:06 – 01:01:16:00
It’s a task force.

01:01:16:00 – 01:01:18:11
And that task force represents

01:01:18:11 – 01:01:20:12
so many different languages

01:01:20:12 – 01:01:23:12
and interpreters, providers

01:01:23:21 – 01:01:25:19
and tech people

01:01:25:19 – 01:01:27:21
that are involved in the tech development

01:01:27:21 – 01:01:28:22
of it.

01:01:28:22 – 01:01:31:00
We also have consumers

01:01:31:00 – 01:01:32:21
who would be using the services,

01:01:32:21 – 01:01:33:20
so we have a broad

01:01:33:20 – 01:01:34:20
range of people involved

01:01:34:20 – 01:01:36:05
in the task force.

01:01:36:05 – 01:01:39:24
Now, in terms of the deaf perspective,

01:01:39:24 – 01:01:43:03
we wanted to make sure that

01:01:43:19 – 01:01:44:19
they got involved,

01:01:44:19 – 01:01:46:18
but it was not until a bit later

01:01:46:18 – 01:01:47:12
after task

01:01:47:12 – 01:01:49:00
Force was formed that they got involved.

01:01:49:00 – 01:01:49:14
And so we said,

01:01:49:14 – 01:01:50:08
you know, we want silos,

01:01:50:08 – 01:01:51:00
which include we want

01:01:51:00 – 01:01:52:08
all of this included.

01:01:52:08 – 01:01:55:08
So we established the advisory council

01:01:55:08 – 01:01:58:04
to ensure that AI and say sign language

01:01:58:04 – 01:01:59:23
interpreting was represented

01:01:59:23 – 01:02:01:06
from the deaf perspective.

01:02:01:06 – 01:02:02:08
And of course

01:02:02:08 – 01:02:03:18
there’s diverse organizations,

01:02:03:18 – 01:02:05:15
the academic perspective,

01:02:05:15 – 01:02:07:10
designers, developers, all of that.

01:02:07:10 – 01:02:10:18
They were involved and in the group

01:02:11:02 – 01:02:11:11
so that

01:02:11:11 – 01:02:14:18
they could give their advice to say, I

01:02:15:06 – 01:02:17:03
now say saved by

01:02:17:03 – 01:02:18:02
AI has been working

01:02:18:02 – 01:02:19:08
with the advisory group

01:02:19:08 – 01:02:20:22
and the goal is to continue

01:02:20:22 – 01:02:24:19
to develop the policies and the law.

01:02:24:24 – 01:02:27:21
Suggestions that we have for the group

01:02:27:21 – 01:02:31:19
so that our parts and our recommendations

01:02:31:19 – 01:02:32:12
are included

01:02:32:12 – 01:02:33:20
in their reporting

01:02:33:20 – 01:02:36:08
and in their data collection and surveys.

01:02:36:08 – 01:02:37:07
Right now

01:02:37:07 – 01:02:38:18
there are ten different languages

01:02:38:18 – 01:02:39:23
that are being looked at.

01:02:39:23 – 01:02:41:06
And so in terms of American

01:02:41:06 – 01:02:44:15
Sign Language, it was noted

01:02:44:15 – 01:02:46:04
that there

01:02:46:04 – 01:02:47:23
needed to be another opportunity

01:02:47:23 – 01:02:48:14
to collaborate

01:02:48:14 – 01:02:50:02
more with the deaf community

01:02:50:02 – 01:02:51:05
to send out surveys

01:02:51:05 – 01:02:52:10
in American Sign Language.

01:02:52:10 – 01:02:53:12
So that’s where we are right now.

01:02:53:12 – 01:02:54:13
In the process.

01:02:54:13 – 01:02:56:17
We’re hoping that our dialog

01:02:56:17 – 01:02:57:17
and our discussion

01:02:57:17 – 01:03:00:04
and our collection of information

01:03:00:04 – 01:03:03:17
will become a great contributor

01:03:03:17 – 01:03:05:08
to the bigger picture.

01:03:05:08 – 01:03:06:23
Does anyone have anything to add?

01:03:06:23 – 01:03:08:12
I just wanted to clarify a comment

01:03:08:12 – 01:03:09:14
that I had made earlier.

01:03:15:22 – 01:03:16:19
Okay.

01:03:16:19 – 01:03:19:19
Moving on to the next question

01:03:20:02 – 01:03:23:03
for this report and research.

01:03:23:23 – 01:03:26:06
We are focusing on ASL.

01:03:26:06 – 01:03:28:10
What about other signed languages

01:03:28:10 – 01:03:29:15
in other countries?

01:03:29:15 – 01:03:32:03
Will we be looking at others

01:03:32:03 – 01:03:35:03
in the future?

01:03:36:13 – 01:03:37:22
I can take that question.

01:03:37:22 – 01:03:39:12
It would be great.

01:03:39:12 – 01:03:40:16
It’s a dream

01:03:40:16 – 01:03:43:10
not for ASL to be the only language

01:03:43:10 – 01:03:44:06
that we look at, right?

01:03:44:06 – 01:03:46:04
We want to consider all of this,

01:03:46:04 – 01:03:48:05
but in terms of the task force

01:03:48:05 – 01:03:51:08
and the advisory council where

01:03:51:12 – 01:03:53:01
we’re working with different

01:03:53:01 – 01:03:54:19
American organizations

01:03:54:19 – 01:03:57:20
and we have been mostly focusing on ASL,

01:03:58:01 – 01:04:01:01
but we are seeing more effort right now

01:04:01:01 – 01:04:02:17
in other parts of the world

01:04:02:17 – 01:04:04:05
where they are focusing

01:04:04:05 – 01:04:06:09
on the automatic interpreting

01:04:06:09 – 01:04:08:01
for other languages.

01:04:08:01 – 01:04:11:21
And so I am aware of the fact

01:04:11:21 – 01:04:14:15
that there are some places in Europe

01:04:14:15 – 01:04:15:23
that are focusing on things.

01:04:15:23 – 01:04:17:09
I don’t have the specific names.

01:04:17:09 – 01:04:19:19
They don’t they don’t come to me.

01:04:19:19 – 01:04:20:21
These names are not coming to me

01:04:20:21 – 01:04:21:06
right now.

01:04:21:06 – 01:04:25:06
But yeah, and Tim can talk more about

01:04:26:05 – 01:04:26:12
one of

01:04:26:12 – 01:04:29:12
those organizations.

01:04:31:16 – 01:04:32:16
We do have people involved

01:04:32:16 – 01:04:33:23
in Europe and Canada.

01:04:33:23 – 01:04:35:10
I know that there are many more

01:04:35:10 – 01:04:36:09
all over the world

01:04:36:09 – 01:04:37:09
who are also looking

01:04:37:09 – 01:04:39:05
at the same technology,

01:04:39:05 – 01:04:42:20
but because right now, Safe Eye

01:04:42:21 – 01:04:45:01
Task Force is focusing on

01:04:45:01 – 01:04:47:04
American policy legislation

01:04:47:04 – 01:04:50:12
and all of that current focus is

01:04:50:12 – 01:04:52:20
specifically what’s happening in America

01:04:52:20 – 01:04:53:21
and North America.

01:04:53:21 – 01:04:55:01
But at the same time,

01:04:55:01 – 01:04:58:13
we could have an impact on Canada

01:04:58:22 – 01:04:59:18
some of our research,

01:04:59:18 – 01:05:01:11
could impact Europe as well.

01:05:01:11 – 01:05:03:17
So I think this is with the process,

01:05:03:17 – 01:05:06:17
we will probably

01:05:07:02 – 01:05:09:01
continue to move forward

01:05:09:01 – 01:05:12:01
and see more replication

01:05:12:01 – 01:05:15:01
of our studies or expansion of our focus.

01:05:15:06 – 01:05:17:05
Looking at it on a more global scale

01:05:17:05 – 01:05:19:08
as we move forward.

01:05:19:08 – 01:05:21:24
Theresa Here I’d like to also add as well

01:05:21:24 – 01:05:23:01
that I think it’s imperative

01:05:23:01 – 01:05:24:12
that we recognize people

01:05:24:12 – 01:05:26:17
using sign language here in the U.S.

01:05:26:17 – 01:05:29:02
are not only using ASL.

01:05:29:02 – 01:05:30:19
So it’s important for us to know

01:05:30:19 – 01:05:32:23
that there are many foreign

01:05:32:23 – 01:05:35:16
languages used here in the US

01:05:35:16 – 01:05:38:08
and we’ve feedback from users of sign

01:05:38:08 – 01:05:39:09
language in the U.S.,

01:05:39:09 – 01:05:41:15
not just American Sign language.

01:05:41:15 – 01:05:45:06
We will or we will be reaching out to

01:05:45:07 – 01:05:46:11
other sign languages

01:05:46:11 – 01:05:48:15
and pulling in that data and information

01:05:48:15 – 01:05:50:11
and their experiences as well.

01:05:50:11 – 01:05:51:08
And it’s important

01:05:51:08 – 01:05:54:14
for us to include everyone at the table.

01:05:58:22 – 01:05:59:12
Okay.

01:05:59:12 – 01:06:01:01
Next question.

01:06:01:01 – 01:06:04:01
So this question is related to readiness.

01:06:05:21 – 01:06:07:20
So in terms

01:06:07:20 – 01:06:10:04
of these technological companies

01:06:10:04 – 01:06:11:12
in development

01:06:11:12 – 01:06:13:16
and a lot of these people

01:06:13:16 – 01:06:14:06
who are working

01:06:14:06 – 01:06:14:24
in this

01:06:14:24 – 01:06:16:10
arena and make these decisions

01:06:16:10 – 01:06:18:08
don’t always include deaf people.

01:06:18:08 – 01:06:21:04
So what approach do we think?

01:06:21:04 – 01:06:23:08
Or how can we make sure

01:06:23:08 – 01:06:24:17
that deaf individuals

01:06:24:17 – 01:06:25:07
are involved

01:06:25:07 – 01:06:26:09
in these conversations

01:06:26:09 – 01:06:27:02
and that they’re always

01:06:27:02 – 01:06:31:07
a part of the process for AI by AI,

01:06:32:06 – 01:06:35:06
machine learning and everything are

01:06:37:02 – 01:06:38:12
Emery Here, I’ll take that.

01:06:38:12 – 01:06:39:01
I’d be happy

01:06:39:01 – 01:06:40:18
to make a comment about that.

01:06:40:18 – 01:06:42:18
So with regards to

01:06:42:18 – 01:06:44:13
what we’ve been speaking about,

01:06:44:13 – 01:06:45:15
it’s important

01:06:45:15 – 01:06:49:06
that if all of the impact organizations

01:06:49:10 – 01:06:51:09
that were serving the community,

01:06:51:09 – 01:06:55:01
educational, corporate America,

01:06:55:01 – 01:06:55:24
different areas,

01:06:55:24 – 01:06:57:10
we know that part

01:06:57:10 – 01:06:59:18
of our screening process,

01:06:59:18 – 01:07:01:21
people want to sponsor our organization.

01:07:01:21 – 01:07:04:07
They want to to look into this.

01:07:04:07 – 01:07:05:21
They want to look in the screening

01:07:05:21 – 01:07:10:00
is disability access included?

01:07:10:00 – 01:07:11:01
Is that supported?

01:07:11:01 – 01:07:12:10
Are your hiring deaf

01:07:12:10 – 01:07:14:13
and hard of hearing employees?

01:07:14:13 – 01:07:15:14
There’s so many things

01:07:15:14 – 01:07:17:15
to look at discuss with them.

01:07:17:15 – 01:07:19:10
It’s very creative

01:07:19:10 – 01:07:21:12
in the approach to HIPAA.

01:07:21:12 – 01:07:23:17
And so it’s a big it’s a very hot topic.

01:07:25:01 – 01:07:27:16
Not only are the

01:07:27:16 – 01:07:30:05
the community and organizations at large,

01:07:30:05 – 01:07:31:14
but also the individual.

01:07:31:14 – 01:07:32:17
Again,

01:07:32:17 – 01:07:33:21
what it looks like to us

01:07:33:21 – 01:07:35:13
is we have to really partner

01:07:35:13 – 01:07:38:19
and really push this,

01:07:38:19 – 01:07:41:19
that all of them

01:07:42:05 – 01:07:43:08
it’s important for all

01:07:43:08 – 01:07:45:20
because they’re impacted

01:07:45:20 – 01:07:47:18
by the development and design of this.

01:07:47:18 – 01:07:48:05
It’s important

01:07:48:05 – 01:07:49:24
for all people to be included,

01:07:49:24 – 01:07:52:01
not just hire them for, you know,

01:07:52:01 – 01:07:53:10
a consulting position,

01:07:53:10 – 01:07:55:22
a temporary feedback on this,

01:07:55:22 – 01:07:58:00
but have them involved in the development

01:07:58:00 – 01:08:00:20
and design of this. It’s imperative.

01:08:00:20 – 01:08:03:20
It’s so important.

01:08:06:17 – 01:08:08:10
Okay.

01:08:08:10 – 01:08:10:00
For the next question,

01:08:10:00 – 01:08:12:22
automatic speech recognition.

01:08:12:22 – 01:08:16:19
ESR In comparison to eBay.

01:08:16:24 – 01:08:20:08
AI how comfortable

01:08:20:08 – 01:08:22:15
and how much can you trust these?

01:08:22:15 – 01:08:25:15
Two And can you compare the two?

01:08:25:22 – 01:08:28:02
Jeff Here I’d like to take that.

01:08:28:02 – 01:08:31:07
You know, it’s like comparing apples to

01:08:31:07 – 01:08:32:24
oranges, both of them are fruit,

01:08:32:24 – 01:08:34:15
but they are a little bit different.

01:08:34:15 – 01:08:37:22
So this is a very specific portion

01:08:37:22 – 01:08:39:07
of technology

01:08:39:07 – 01:08:42:05
and it focuses on translating from one

01:08:42:05 – 01:08:45:14
language to another.

01:08:45:14 – 01:08:46:17
So so

01:08:48:00 – 01:08:48:19
in its

01:08:48:19 – 01:08:51:20
form, so spoken speech into written form

01:08:52:02 – 01:08:54:24
Abe AI focuses on interpreting

01:08:54:24 – 01:08:57:20
and that is an automatic on the spot

01:08:57:20 – 01:09:00:17
that moment find production.

01:09:00:17 – 01:09:03:03
So that means we have information

01:09:03:03 – 01:09:07:07
feeding into the process

01:09:07:14 – 01:09:10:13
and speech recognition is just picking up

01:09:10:13 – 01:09:12:08
on the kid’s speech.

01:09:12:08 – 01:09:13:14
The context is different,

01:09:13:14 – 01:09:15:18
the information processing is different,

01:09:15:18 – 01:09:17:15
and most of the equivalent

01:09:17:15 – 01:09:20:14
for ASL and speech recognition

01:09:20:14 – 01:09:23:16
would be ASL are signed recognition

01:09:24:01 – 01:09:27:14
and that technology is part of A.I.

01:09:27:14 – 01:09:28:14
by A.I..

01:09:28:14 – 01:09:29:19
But Abe A.I.

01:09:29:19 – 01:09:31:03
includes a variety

01:09:31:03 – 01:09:32:05
other technologies

01:09:32:05 – 01:09:35:05
to help those components work together.

01:09:35:10 – 01:09:37:22
It picks up on that subject

01:09:37:22 – 01:09:40:22
processing, body language,

01:09:41:05 – 01:09:44:01
situational context, clues

01:09:44:01 – 01:09:45:23
and

01:09:45:23 – 01:09:48:23
pragmatics

01:09:49:04 – 01:09:50:13
are the concepts

01:09:50:13 – 01:09:53:13
of the transcriptions.

01:09:53:24 – 01:09:55:05
And just to add to that,

01:09:55:05 – 01:09:56:07
this is Tim here.

01:09:56:07 – 01:10:00:15
And in terms of speech recognition,

01:10:01:16 – 01:10:02:05
that is

01:10:02:05 – 01:10:05:05
something where we already see

01:10:05:15 – 01:10:06:19
a much more development

01:10:06:19 – 01:10:08:11
because there have been years

01:10:08:11 – 01:10:09:05
of investment

01:10:09:05 – 01:10:11:14
in the development of that technology

01:10:11:14 – 01:10:12:19
while sign language

01:10:12:19 – 01:10:14:24
recognition is behind.

01:10:14:24 – 01:10:18:07
And so that becomes an issue of equity.

01:10:18:14 – 01:10:20:01
And there’s a concern there

01:10:20:01 – 01:10:21:17
that with speech recognition,

01:10:21:17 – 01:10:23:20
because of the time and the investment

01:10:23:20 – 01:10:24:22
that’s already there,

01:10:24:22 – 01:10:26:09
we’ve spoken language,

01:10:26:09 – 01:10:28:15
caring people continue to benefit from,

01:10:28:15 – 01:10:29:10
while deaf

01:10:29:10 – 01:10:31:18
individuals cannot have the same access

01:10:31:18 – 01:10:33:09
or the same benefit.

01:10:33:09 – 01:10:35:00
We go back to, for example,

01:10:35:00 – 01:10:37:00
the invention of the telephone.

01:10:37:00 – 01:10:38:04
Of course, hearing

01:10:38:04 – 01:10:39:17
people were able to use the phone

01:10:39:17 – 01:10:42:02
for many years and enjoy that technology

01:10:42:02 – 01:10:43:06
until 80 years later.

01:10:43:06 – 01:10:44:21
We finally got the video phone

01:10:44:21 – 01:10:46:16
and these other forms of technology

01:10:46:16 – 01:10:46:24
where deaf

01:10:46:24 – 01:10:48:05
people could benefit

01:10:48:05 – 01:10:50:18
from the same kind of experience.

01:10:50:18 – 01:10:53:06
So we always have to look at

01:10:53:06 – 01:10:54:08
these situations

01:10:54:08 – 01:10:56:24
and make sure that there is funding

01:10:56:24 – 01:11:00:12
and that research is being done to try to

01:11:00:19 – 01:11:04:08
ensure that sign language is caught up

01:11:04:12 – 01:11:07:08
to what the hearing community is able

01:11:07:08 – 01:11:07:22
to enjoy.

01:11:11:08 – 01:11:12:23
As A next question is

01:11:12:23 – 01:11:14:06
regarding people

01:11:14:06 – 01:11:18:01
with intellectual disabilities, autism

01:11:18:17 – 01:11:21:06
learning disabilities, language

01:11:21:06 – 01:11:24:06
fluency

01:11:24:08 – 01:11:25:07
and the like.

01:11:25:07 – 01:11:28:18
How does this relate to Abe?

01:11:29:01 – 01:11:32:14
I will their abilities and disabilities

01:11:32:14 – 01:11:35:14
be included in this process?

01:11:36:23 – 01:11:38:24
I can take that, Yeah.

01:11:38:24 – 01:11:41:09
Thank you. This is a great question.

01:11:41:09 – 01:11:42:13
Similar

01:11:42:13 – 01:11:44:12
to what we were talking about earlier.

01:11:44:12 – 01:11:47:22
When it comes to, for example,

01:11:48:16 – 01:11:49:24
the FCC,

01:11:49:24 – 01:11:52:13
the Federal Communications Commission,

01:11:52:13 – 01:11:55:13
and talking about language itself

01:11:55:23 – 01:11:58:23
and speech recognition,

01:12:00:21 – 01:12:03:05
there’s understanding

01:12:03:05 – 01:12:05:05
of people

01:12:05:05 – 01:12:06:23
that have different backgrounds.

01:12:06:23 – 01:12:09:24
Same thing happens with this API

01:12:09:24 – 01:12:11:02
and the approach.

01:12:11:02 – 01:12:12:20
We want to make sure that we resolve

01:12:12:20 – 01:12:13:22
some of these issues.

01:12:13:22 – 01:12:14:24
We need to be able

01:12:14:24 – 01:12:16:17
develop something for them

01:12:16:17 – 01:12:19:20
to make sure that it’s successful.

01:12:20:02 – 01:12:23:02
And so it’s still to be seen.

01:12:23:10 – 01:12:24:20
We are not able

01:12:24:20 – 01:12:26:04
answer that at this time

01:12:26:04 – 01:12:28:04
unless there’s anyone else here involved.

01:12:28:04 – 01:12:30:02
But and from what I understand,

01:12:30:02 – 01:12:31:01
I think it’s still a hot

01:12:31:01 – 01:12:32:10
topic of discussion

01:12:32:10 – 01:12:33:12
and it’s something

01:12:33:12 – 01:12:34:17
that the community of people

01:12:34:17 – 01:12:36:13
is still talking about.

01:12:36:13 – 01:12:38:18
But yeah, great question

01:12:38:18 – 01:12:41:12
and saying, yes, Jeff had mentioned the

01:12:41:12 – 01:12:44:18
data, They’re good data and

01:12:45:23 – 01:12:47:00
good models.

01:12:47:00 – 01:12:50:08
So with that design process,

01:12:50:14 – 01:12:51:17
we have to prepare

01:12:51:17 – 01:12:53:09
for a variety of deaf members

01:12:53:09 – 01:12:54:15
in the community.

01:12:54:15 – 01:12:55:00
You know,

01:12:55:00 – 01:12:55:16
in the beginning

01:12:55:16 – 01:12:57:00
we’ve got to collect data

01:12:57:00 – 01:12:58:23
from the community at large

01:12:58:23 – 01:13:01:23
and make sure that it is

01:13:02:09 – 01:13:05:00
appropriate in moving forward

01:13:05:00 – 01:13:05:16
that it’s going to be

01:13:05:16 – 01:13:07:10
beneficial to the community.

01:13:07:10 – 01:13:08:18
Now, if we don’t include

01:13:08:18 – 01:13:09:20
those in the beginning,

01:13:09:20 – 01:13:12:10
that could be a problem with our models.

01:13:12:10 – 01:13:15:10
They won’t be prepared for that.

01:13:17:10 – 01:13:18:19
This is Teresa,

01:13:18:19 – 01:13:21:17
and I’d like to add a specific example

01:13:21:17 – 01:13:23:07
in terms of writing English.

01:13:23:07 – 01:13:25:01
So we see that I know

01:13:25:01 – 01:13:26:10
most people are probably familiar

01:13:26:10 – 01:13:29:10
with Chat. JPT

01:13:29:24 – 01:13:32:12
Currently you’re able to ask chat.

01:13:32:12 – 01:13:36:20
JPT and to develop

01:13:37:00 – 01:13:40:16
some draft of, for example,

01:13:40:16 – 01:13:44:24
plain language meaning the concept

01:13:44:24 – 01:13:46:19
of using it for people

01:13:46:19 – 01:13:49:16
with intellectual disabilities. So

01:13:51:05 – 01:13:52:07
that reflects the

01:13:52:07 – 01:13:53:23
importance of the inclusion

01:13:53:23 – 01:13:56:08
of people in design.

01:13:56:08 – 01:13:57:17
And also

01:13:57:17 – 01:14:00:05
when we are having these discussions

01:14:00:05 – 01:14:03:01
about how to design technology,

01:14:03:01 – 01:14:04:13
we have to ask the question

01:14:04:13 – 01:14:06:06
who’s going to be involved?

01:14:06:06 – 01:14:08:00
Because sometimes the people who are

01:14:08:00 – 01:14:10:01
there are not the people

01:14:10:01 – 01:14:11:01
that need to be involved.

01:14:11:01 – 01:14:12:05
So we want to make sure

01:14:12:05 – 01:14:13:15
that we recognizing

01:14:13:15 – 01:14:14:10
and not forgetting

01:14:14:10 – 01:14:15:16
about these individuals

01:14:15:16 – 01:14:18:16
and these various communities.

01:14:19:08 – 01:14:20:21
I mean, one thing

01:14:20:21 – 01:14:22:03
that I think about is,

01:14:22:03 – 01:14:23:23
for example, a CDI.

01:14:23:23 – 01:14:24:15
Often

01:14:24:15 – 01:14:25:23
we have seen the use

01:14:25:23 – 01:14:27:21
of a certified deaf interpreter

01:14:27:21 – 01:14:30:05
who comes into the situation

01:14:30:05 – 01:14:32:05
and we look at how that changes

01:14:32:05 – 01:14:34:01
and improves communication.

01:14:34:01 – 01:14:35:09
The experience for the deaf

01:14:35:09 – 01:14:37:04
individual is improved,

01:14:37:04 – 01:14:39:02
and I think we can see that benefit

01:14:39:02 – 01:14:40:02
and understand how it would

01:14:40:02 – 01:14:42:10
apply as well to I by I.

01:14:47:02 – 01:14:47:12
Okay.

01:14:47:12 – 01:14:51:03
Next question is related to

01:14:53:01 – 01:14:55:03
there are three different webinars

01:14:55:03 – 01:14:58:03
that you guys hosted

01:14:58:15 – 01:15:01:08
and during those webinars,

01:15:01:08 – 01:15:04:08
did you have a group

01:15:04:13 – 01:15:07:05
or did you have people

01:15:07:05 – 01:15:10:00
who were not using sign language

01:15:10:00 – 01:15:11:24
involved in the group

01:15:11:24 – 01:15:13:19
that they do not use sign language

01:15:13:19 – 01:15:18:00
at all to communicate, to discuss by I

01:15:23:22 – 01:15:26:12
and this is Emery here.

01:15:26:12 – 01:15:28:09
Another good question

01:15:28:09 – 01:15:32:00
and yeah, we had asked them for

01:15:32:12 – 01:15:34:04
to specifically identify

01:15:34:04 – 01:15:37:14
their level of sign skill and

01:15:38:01 – 01:15:39:06
I know that

01:15:39:06 – 01:15:40:19
the panel was talking about that.

01:15:40:19 – 01:15:43:23
I can’t remember exactly how they said,

01:15:43:23 – 01:15:44:16
but it was

01:15:44:16 – 01:15:45:08
the question

01:15:45:08 – 01:15:47:08
was about their level of signing.

01:15:47:08 – 01:15:50:14
And so the it was an open discussion

01:15:50:14 – 01:15:51:21
and we told everyone that

01:15:51:21 – 01:15:53:16
it was going to be in sign language.

01:15:53:16 – 01:15:55:09
But the percentage of people

01:15:55:09 – 01:15:57:07
who were not fluent in

01:15:57:07 – 01:15:59:10
sign, I’m not totally sure.

01:15:59:10 – 01:16:01:22
I don’t know if anyone has a different

01:16:01:22 – 01:16:03:08
recall, something different and

01:16:05:09 – 01:16:05:19
I don’t

01:16:05:19 – 01:16:06:19
think that we had

01:16:06:19 – 01:16:08:19
collected enough data about that.

01:16:08:19 – 01:16:11:15
I think it was hard for us to evaluate,

01:16:11:15 – 01:16:12:14
let alone

01:16:12:14 – 01:16:14:08
evaluate our own data

01:16:14:08 – 01:16:15:17
that we had collected.

01:16:15:17 – 01:16:18:17
So

01:16:21:08 – 01:16:24:08
and in terms of the captioning

01:16:24:08 – 01:16:26:17
and English based caption,

01:16:26:17 – 01:16:28:16
we were not focused on that.

01:16:28:16 – 01:16:29:05
We were more

01:16:29:05 – 01:16:30:19
focused on the sign language

01:16:30:19 – 01:16:32:10
interpreting aspect.

01:16:32:10 – 01:16:36:16
So we do already have a lot of discussion

01:16:36:21 – 01:16:38:00
happening

01:16:38:00 – 01:16:40:22
to the text based aspect of this,

01:16:40:22 – 01:16:43:13
but sign language, there are some gaps,

01:16:43:13 – 01:16:46:13
so we want to focus on that specifically.

01:16:51:00 – 01:16:54:00
Okay,

01:16:55:18 – 01:16:56:17
please help one moment

01:16:56:17 – 01:16:59:17
while through these questions.

01:17:04:07 – 01:17:04:16
Okay.

01:17:04:16 – 01:17:08:00
This question is regarding

01:17:08:00 – 01:17:11:00
the development of the curriculum

01:17:11:07 – 01:17:13:19
at the university level regarding

01:17:13:19 – 01:17:16:19
different interpreting processes

01:17:19:10 – 01:17:22:04
and

01:17:22:04 – 01:17:24:03
for VRA,

01:17:24:03 – 01:17:26:24
VRA and the like.

01:17:26:24 – 01:17:29:21
Now are we going to be adding a I

01:17:31:02 – 01:17:34:01
and there any feedback

01:17:34:01 – 01:17:36:00
that you could share regarding

01:17:36:00 – 01:17:39:12
curriculum development on this subject?

01:17:47:08 – 01:17:48:17
Okay, that’s a good question.

01:17:48:17 – 01:17:49:07
Again,

01:17:49:07 – 01:17:53:09
I think maybe for a future discussion,

01:17:53:09 – 01:17:54:24
it might be more beneficial.

01:17:54:24 – 01:17:55:14
We haven’t

01:17:55:14 – 01:17:57:08
necessarily gotten to that point yet.

01:17:57:08 – 01:17:59:00
We’ll have to do some training

01:17:59:00 – 01:17:59:19
with the curriculum

01:17:59:19 – 01:18:01:10
and workshops and all of.

01:18:01:10 – 01:18:02:03
But again,

01:18:02:03 – 01:18:02:21
I would say

01:18:02:21 – 01:18:06:12
that it could come up at a symposium

01:18:06:22 – 01:18:09:17
or maybe another opportunity

01:18:09:17 – 01:18:13:20
for discussion could become available

01:18:13:20 – 01:18:16:20
in the future.

01:18:21:17 – 01:18:23:24
We haven’t discussed this as of yet,

01:18:23:24 – 01:18:27:05
but in considering the

01:18:27:05 – 01:18:30:05
ethical foundations of this.

01:18:30:10 – 01:18:33:04
So for interpreting,

01:18:33:04 – 01:18:36:16
we do have the ethical foundation

01:18:36:23 – 01:18:38:00
for the Deaf community.

01:18:38:00 – 01:18:42:02
We do have our ethical expectations

01:18:42:20 – 01:18:44:12
and norms

01:18:44:12 – 01:18:46:05
as well as other aspects of that.

01:18:46:05 – 01:18:48:01
But when we enter into this

01:18:48:01 – 01:18:48:24
with some knowledge,

01:18:48:24 – 01:18:52:13
now we have new technology

01:18:52:18 – 01:18:54:07
and what kind of new questions

01:18:54:07 – 01:18:56:05
are going to arise from this?

01:18:56:05 – 01:18:57:17
And that’s part of what we’re hoping

01:18:57:17 – 01:18:59:24
to have some discussions regarding

01:18:59:24 – 01:19:03:01
that topic in the next symposium

01:19:03:01 – 01:19:03:17
next month.

01:19:07:14 – 01:19:08:04
wow, I forgot.

01:19:08:04 – 01:19:09:18
It’s March already.

01:19:09:18 – 01:19:12:18
It is.

01:19:14:16 – 01:19:15:00
Okay.

01:19:15:00 – 01:19:16:08
Next question.

01:19:16:08 – 01:19:19:01
One person had a comment

01:19:19:01 – 01:19:20:13
and said, a lot is happening

01:19:20:13 – 01:19:20:23
right now

01:19:20:23 – 01:19:24:11
in Europe as it relates to AI by AI.

01:19:24:18 – 01:19:28:04
Are you familiar with that and also how

01:19:28:04 – 01:19:32:16
that relates to D GDP?

01:19:33:14 – 01:19:36:11
R And so that’s

01:19:36:11 – 01:19:39:17
the general data protection

01:19:41:01 – 01:19:44:06
regulation to that law.

01:19:44:15 – 01:19:47:20
It’s a very strict

01:19:48:00 – 01:19:51:00
law on privacy and protection.

01:19:51:09 – 01:19:52:08
So can you guys

01:19:52:08 – 01:19:55:08
discuss in touch a bit on that topic?

01:19:56:11 – 01:19:57:12
I would like to do that.

01:19:57:12 – 01:19:59:14
Thank you for bringing that up.

01:19:59:14 – 01:20:04:16
In general, the data has been

01:20:04:18 – 01:20:07:10
it’s been one of the best

01:20:07:10 – 01:20:09:17
data privacy laws.

01:20:09:17 – 01:20:13:07
Hats off to them to the EU

01:20:13:07 – 01:20:14:10
for developing that.

01:20:14:10 – 01:20:15:09
It’s been wonderful

01:20:15:09 – 01:20:17:17
so I think one of the biggest concepts

01:20:17:17 – 01:20:19:02
or take away from this

01:20:19:02 – 01:20:22:02
is the topic of the right

01:20:22:05 – 01:20:24:02
to be forgotten.

01:20:24:02 – 01:20:25:02
Meaning

01:20:25:02 – 01:20:27:06
we can remind them and say, Hey,

01:20:27:06 – 01:20:29:17
I want you to remove my information

01:20:29:17 – 01:20:32:17
and they have to honor your request.

01:20:32:18 – 01:20:35:00
And that is one of the biggest takeaways

01:20:35:00 – 01:20:37:23
for this foundational concept.

01:20:37:23 – 01:20:40:13
And so another thing to look at as

01:20:40:13 – 01:20:43:08
well is the minors.

01:20:43:08 – 01:20:46:08
The age of the data collection.

01:20:46:10 – 01:20:48:21
Parents have to approve that or

01:20:50:03 – 01:20:51:15
revoke approval.

01:20:51:15 – 01:20:52:14
And so really,

01:20:52:14 – 01:20:54:17
in some of these aspects, the EU

01:20:54:17 – 01:20:55:20
ahead of us

01:20:55:20 – 01:20:59:12
and, you know, the American legislature

01:20:59:12 – 01:21:01:22
does have some protections,

01:21:01:22 – 01:21:03:15
but it’s not as focused.

01:21:03:15 – 01:21:06:07
It’s more focused. Children under age.

01:21:06:07 – 01:21:09:07
I believe it’s 13 or 14,

01:21:10:03 – 01:21:11:15
but the EU is ahead of us

01:21:11:15 – 01:21:13:07
in this respect.

01:21:13:07 – 01:21:16:01
The legislation there

01:21:16:01 – 01:21:17:09
there’s a lot of harm

01:21:17:09 – 01:21:19:10
that can be done with this data.

01:21:19:10 – 01:21:20:21
And so that’s something that really needs

01:21:20:21 – 01:21:22:09
to be fleshed out with anyone else.

01:21:22:09 – 01:21:25:09
Like to add to that,

01:21:30:02 – 01:21:31:05
I could go on for,

01:21:31:05 – 01:21:32:23
for ages about this,

01:21:32:23 – 01:21:34:14
but it’s the biggest thing

01:21:34:14 – 01:21:35:15
since

01:21:35:15 – 01:21:39:09
the organization had been established.

01:21:39:09 – 01:21:44:01
The Data collection and privacy

01:21:44:01 – 01:21:47:20
and who is responsible for making sure

01:21:47:20 – 01:21:50:20
that the data is retained safely,

01:21:51:02 – 01:21:53:12
that it’s not leaked?

01:21:53:12 – 01:21:56:16
And if that data is leaked,

01:21:56:18 – 01:21:59:17
how are they going to inform individuals

01:21:59:17 – 01:22:02:13
whose data has been breached that?

01:22:02:13 – 01:22:04:01
This has occurred.

01:22:04:01 – 01:22:05:21
So that’s part of the process

01:22:05:21 – 01:22:07:14
that should be included

01:22:07:14 – 01:22:09:07
in the transparency

01:22:09:07 – 01:22:12:00
and, you know, maintaining that contact

01:22:12:00 – 01:22:15:11
with individuals on the subject.

01:22:16:16 – 01:22:19:16
Thank you.

01:22:21:17 – 01:22:22:04
Okay.

01:22:22:04 – 01:22:25:04
We still have more questions.

01:22:26:02 – 01:22:27:17
This question is related

01:22:27:17 – 01:22:30:17
to machine learning AML.

01:22:30:20 – 01:22:32:24
So and with sign language

01:22:32:24 – 01:22:35:24
recognition, ASL are

01:22:36:19 – 01:22:38:15
who will be training

01:22:38:15 – 01:22:41:15
and teaching the language data.

01:22:41:22 – 01:22:43:17
Where is it going to come from?

01:22:43:17 – 01:22:46:05
From interpreters, from people?

01:22:46:05 – 01:22:49:05
Where will that come from?

01:22:51:01 – 01:22:52:14
So that is an excellent question.

01:22:52:14 – 01:22:53:03
Again,

01:22:53:03 – 01:22:54:07
that is something that

01:22:54:07 – 01:22:56:20
we can’t really control.

01:22:56:20 – 01:22:59:15
It’s up to the company

01:22:59:15 – 01:23:01:18
who is developing that

01:23:01:18 – 01:23:03:06
and doing the work.

01:23:03:06 – 01:23:06:06
So I know that in academia

01:23:06:08 – 01:23:07:19
there’s a lot of evaluations

01:23:07:19 – 01:23:08:24
on authors,

01:23:08:24 – 01:23:12:11
like, for example Oscar Kilner.

01:23:12:19 – 01:23:15:06

01:23:15:06 – 01:23:19:14
Lopez was an author in the ASL,

01:23:19:14 – 01:23:23:14
are community and used interpreter data.

01:23:23:23 – 01:23:27:24
For example, there was a very well-known

01:23:29:03 – 01:23:31:00
individual from Germany

01:23:31:00 – 01:23:34:03
and they had

01:23:35:05 – 01:23:37:11
a a system

01:23:37:11 – 01:23:38:14
that would do

01:23:38:14 – 01:23:40:03
weather reports and alerts

01:23:40:03 – 01:23:41:06
and it had an interpreter

01:23:41:06 – 01:23:42:13
down in the corner.

01:23:42:13 – 01:23:45:17
They recorded that for many years

01:23:46:07 – 01:23:49:07
and they used that data to train

01:23:49:07 – 01:23:51:11
the machine.

01:23:51:11 – 01:23:55:09
And so there was a very limited context

01:23:55:09 – 01:23:57:23
only using that one interpreter.

01:23:57:23 – 01:24:01:02
So it’s a great idea in theory

01:24:01:02 – 01:24:04:02
in the future to use,

01:24:04:19 – 01:24:06:04
you know, in other situations,

01:24:06:04 – 01:24:08:01
it may impact it in that way.

01:24:08:01 – 01:24:11:01
It’s very hard

01:24:11:24 – 01:24:14:11
because the data requires

01:24:14:11 – 01:24:16:16
retention and storage

01:24:16:16 – 01:24:19:16
and a lot of video data available

01:24:19:16 – 01:24:20:10
on the web out

01:24:20:10 – 01:24:23:04
there is not the best quality.

01:24:23:04 – 01:24:24:03
For example,

01:24:24:03 – 01:24:27:03
if you look at like an ASL, one class

01:24:27:05 – 01:24:31:15
student signing hey or a song or whatever

01:24:31:21 – 01:24:33:07
you’re looking and you’re saying,

01:24:33:07 – 01:24:34:07
Hey, you’re doing a good job,

01:24:34:07 – 01:24:35:11
you’re learning, you’re doing well.

01:24:35:11 – 01:24:36:15
But that’s not the model

01:24:36:15 – 01:24:37:22
that we want to use in

01:24:37:22 – 01:24:39:02
training the machines

01:24:39:02 – 01:24:40:03
for machine learning.

01:24:41:04 – 01:24:44:04
And the issue that arises, you know,

01:24:44:07 – 01:24:47:02
is a standard for A.I.,

01:24:47:02 – 01:24:47:12
you know,

01:24:47:12 – 01:24:48:09
where do we get this

01:24:48:09 – 01:24:49:08
data collection from?

01:24:49:08 – 01:24:50:18
From various,

01:24:50:18 – 01:24:51:02
you know,

01:24:51:02 – 01:24:53:06
and that is a bidirectional approach

01:24:53:06 – 01:24:53:24
to interpreting.

01:24:53:24 – 01:24:55:16
But there’s a big problem with that

01:24:55:16 – 01:24:56:18
as well.

01:24:56:18 – 01:24:58:11
Consent and privacy

01:24:58:11 – 01:25:02:01
and confidentiality are highly regulated.

01:25:02:01 – 01:25:04:10
And so we can’t use VR if

01:25:04:10 – 01:25:06:02
even though that would be the best place

01:25:06:02 – 01:25:07:13
for data collection.

01:25:07:13 – 01:25:10:19
So we have both signed and speech

01:25:11:06 – 01:25:13:04
and we are working on

01:25:13:04 – 01:25:15:04
how the two interact

01:25:15:04 – 01:25:19:05
and that’s what is really good.

01:25:19:05 – 01:25:20:06
But we’ve got to look

01:25:20:06 – 01:25:21:11
at different sources

01:25:21:11 – 01:25:23:18
and the organizations themselves.

01:25:23:18 – 01:25:26:12
It’s not

01:25:26:12 – 01:25:28:01
we’ve we’ve got to choose

01:25:28:01 – 01:25:28:22
what data we use.

01:25:28:22 – 01:25:30:16
It’s a very important to start

01:25:30:16 – 01:25:34:07
thinking about our legal framework

01:25:35:12 – 01:25:36:23
and, try to

01:25:36:23 – 01:25:39:23
encourage that and remind them to use

01:25:40:00 – 01:25:40:07
you know,

01:25:40:07 – 01:25:43:19
we’ve got to make sure that the data set

01:25:44:07 – 01:25:48:11
will be include from different sources.

01:25:48:17 – 01:25:50:13
The accuracy is there,

01:25:50:13 – 01:25:52:09
the variety is there.

01:25:52:09 – 01:25:54:06
And the group models,

01:25:54:06 – 01:25:57:22
for example, may be there

01:25:57:22 – 01:26:00:07
may be some fluent individuals,

01:26:00:07 – 01:26:01:14
deaf individuals there.

01:26:01:14 – 01:26:02:13
And that’s not the best

01:26:02:13 – 01:26:03:23
planning model to have.

01:26:03:23 – 01:26:05:03
I know that I’m not the best

01:26:05:03 – 01:26:06:07
signing model to have.

01:26:06:07 – 01:26:08:02
I’m not perfectly fluent myself.

01:26:08:02 – 01:26:09:18
It’s my native language

01:26:09:18 – 01:26:12:10
and I want to understand me.

01:26:12:10 – 01:26:15:02
And the same applies to other

01:26:15:02 – 01:26:16:14
signing styles

01:26:16:14 – 01:26:18:23
and signers with different abilities.

01:26:18:23 – 01:26:20:08
So there’s so many groups

01:26:20:08 – 01:26:21:13
that need to be included

01:26:21:13 – 01:26:24:13
and represented in this machine learning.

01:26:30:01 – 01:26:31:11
And to add to that,

01:26:31:11 – 01:26:32:23
it can be very challenging,

01:26:32:23 – 01:26:35:23
but also it can become an opportunity.

01:26:36:00 – 01:26:36:20
So for example,

01:26:36:20 – 01:26:38:01
we have a lot of deaf

01:26:38:01 – 01:26:39:14
individuals all over

01:26:39:14 – 01:26:42:04
their stories, their history.

01:26:42:04 – 01:26:43:06
A lot of times

01:26:43:06 – 01:26:44:20
this is not shared

01:26:44:20 – 01:26:47:00
and so we want to collect and and

01:26:47:00 – 01:26:48:03
save that data.

01:26:49:22 – 01:26:50:21
So I think it becomes a

01:26:50:21 – 01:26:51:15
bit of a project

01:26:51:15 – 01:26:54:15
to see what the impact is.

01:26:54:15 – 01:26:55:23
Understanding stories

01:26:55:23 – 01:26:58:24
and being able to record this history.

01:26:58:24 – 01:27:00:12
And also it’s an opportunity

01:27:00:12 – 01:27:01:16
to take a deeper dive

01:27:01:16 – 01:27:05:02
into these generational situations and,

01:27:05:11 – 01:27:06:10
getting information

01:27:06:10 – 01:27:08:21
from all over from diverse groups.

01:27:08:21 – 01:27:11:03
But I think the challenge can be funding,

01:27:11:03 – 01:27:13:13
and it’s also challenging to find people

01:27:13:13 – 01:27:14:21
who are able to go out

01:27:14:21 – 01:27:16:21
and record this information

01:27:16:21 – 01:27:18:02
and a quality way.

01:27:18:02 – 01:27:20:20
And so that’s a project in and of itself.

01:27:20:20 – 01:27:22:24
But we have this opportunity now

01:27:22:24 – 01:27:25:24
to look at different domains

01:27:26:03 – 01:27:27:24
and how to use sign language

01:27:27:24 – 01:27:30:17
and the medical and legal legal realms.

01:27:30:17 – 01:27:31:24
And we can see how

01:27:31:24 – 01:27:35:06
that becomes bigger project.

01:27:36:04 – 01:27:39:04
But again,

01:27:40:09 – 01:27:43:12
I think we need to go into that more

01:27:43:12 – 01:27:44:18
and we need to see where

01:27:44:18 – 01:27:46:01
we can get that funding from,

01:27:46:01 – 01:27:47:20
because that’s one of the challenges.

01:27:53:00 – 01:27:53:15
Okay.

01:27:53:15 – 01:27:55:10
Next question.

01:27:55:10 – 01:28:00:02
We have a question from hang on.

01:28:00:02 – 01:28:03:02
I left a deaf interpreter

01:28:05:02 – 01:28:06:02
and the question

01:28:06:02 – 01:28:10:11
is using AI captioning or work

01:28:10:11 – 01:28:13:23
or for a meeting or something of the like

01:28:14:15 – 01:28:15:20
are one of the many tools

01:28:15:20 – 01:28:17:00
that have been tested

01:28:17:00 – 01:28:18:11
with other countries

01:28:18:11 – 01:28:21:11
and their language in their access.

01:28:21:11 – 01:28:23:15
But it seems to fail

01:28:23:15 – 01:28:26:07
or it’s not as accurate

01:28:26:07 – 01:28:28:09
at capturing everything.

01:28:28:09 – 01:28:31:09
What approach would you use for A.I.

01:28:31:10 – 01:28:35:04
by making sure it’s accurate and

01:28:36:06 – 01:28:39:06
successful?

01:28:44:15 – 01:28:45:05
It’s all about

01:28:45:05 – 01:28:48:05
the data collection.

01:28:48:06 – 01:28:49:07
This is the memory here.

01:28:49:07 – 01:28:52:03
I think. Again, a great question.

01:28:52:03 – 01:28:53:14
And to expand on that,

01:28:53:14 – 01:28:55:17
I think we see the same situation

01:28:55:17 – 01:28:57:11
with live captioning

01:28:57:11 – 01:29:01:00
that is nuanced voice tone.

01:29:01:04 – 01:29:04:04
Sign language is just

01:29:04:04 – 01:29:05:12
we have to see how we’re going

01:29:05:12 – 01:29:07:18
to approach that when it comes to AI,

01:29:08:17 – 01:29:10:11
a AI is already challenging

01:29:10:11 – 01:29:12:09
for American Sign language

01:29:12:09 – 01:29:14:03
because it’s a conceptual

01:29:14:03 – 01:29:16:06
and a visual language.

01:29:16:06 – 01:29:18:12
So to be able to capture that

01:29:18:12 – 01:29:21:02
and replicate it and I you know,

01:29:21:02 – 01:29:22:02
this is a great question

01:29:22:02 – 01:29:24:05
because how can we approach this

01:29:24:05 – 01:29:25:15
to make this happen?

01:29:25:15 – 01:29:27:07
It’s still a hot topic.

01:29:27:07 – 01:29:28:17
There’s a lot of discussion on this

01:29:28:17 – 01:29:31:10
because people are still on this process

01:29:31:10 – 01:29:32:14
of trying to screen

01:29:32:14 – 01:29:35:00
and figure out how this pertains to A.I..

01:29:35:00 – 01:29:37:04
But yeah,

01:29:37:04 – 01:29:38:14
Tim, here I’d like to add,

01:29:38:14 – 01:29:40:13
if you look at different fields,

01:29:40:13 – 01:29:43:13
for example, linguistic studies,

01:29:43:16 – 01:29:45:01
the sign language,

01:29:45:01 – 01:29:48:23
it starts in about 1960,

01:29:49:07 – 01:29:51:23
68 with Stokie and his team.

01:29:51:23 – 01:29:53:23
And so you look at ASL

01:29:53:23 – 01:29:55:06
and you look at that

01:29:55:06 – 01:29:57:05
in how it’s developed over time,

01:29:57:05 – 01:29:58:17
and it’s really only been studied

01:29:58:17 – 01:30:01:02
in depth for 50 to 60 years.

01:30:01:02 – 01:30:03:12
It’s in its infancy at best.

01:30:03:12 – 01:30:05:15
And so there’s so many fine languages

01:30:05:15 – 01:30:06:11
all around the world

01:30:06:11 – 01:30:07:17
that have not been studied

01:30:07:17 – 01:30:09:05
and have not been documented

01:30:09:05 – 01:30:10:13
to that degree.

01:30:10:13 – 01:30:12:24
So we still need some more research.

01:30:12:24 – 01:30:15:24
And where you know,

01:30:16:08 – 01:30:17:12
it’s very important

01:30:17:12 – 01:30:19:13
for us to really look at that

01:30:19:13 – 01:30:20:18
with science languages,

01:30:20:18 – 01:30:22:10
but also with many spoken languages

01:30:22:10 – 01:30:23:07
as well.

01:30:23:07 – 01:30:24:24
There’s, you know,

01:30:24:24 – 01:30:26:21
thousands of languages in the world

01:30:26:21 – 01:30:29:06
and they don’t have a written form

01:30:29:06 – 01:30:31:07
for every one of them.

01:30:31:07 – 01:30:32:22
And so

01:30:32:22 – 01:30:34:10
there’s a lot of minority languages

01:30:34:10 – 01:30:36:18
as well and dialects.

01:30:36:18 – 01:30:40:04
And so those are at risk for extinction

01:30:40:11 – 01:30:41:18
because they’re not written,

01:30:41:18 – 01:30:43:07
they’re not studied,

01:30:43:07 – 01:30:44:24
they’re not documented.

01:30:44:24 – 01:30:46:08
And in a few years,

01:30:46:08 – 01:30:47:13
the most common languages

01:30:47:13 – 01:30:50:02
that will be used are major languages.

01:30:50:02 – 01:30:51:06
In the minority languages

01:30:51:06 – 01:30:52:22
will have dissipated.

01:30:52:22 – 01:30:56:03
And so that is something that is a risk.

01:30:56:03 – 01:30:59:23
And we have to look at we can’t leave

01:30:59:23 – 01:31:00:21
those behind.

01:31:04:20 – 01:31:05:03
I’d like

01:31:05:03 – 01:31:06:15
to add to that comment

01:31:06:15 – 01:31:08:24
when we talk about data.

01:31:08:24 – 01:31:10:16
I made a comment recently about that

01:31:10:16 – 01:31:11:03
I was wrong.

01:31:11:03 – 01:31:13:12
I should have said that more data

01:31:13:12 – 01:31:15:08
is coming from

01:31:15:08 – 01:31:17:18
underrepresented communities

01:31:17:18 – 01:31:19:14
and that we need to identify

01:31:19:14 – 01:31:20:20
those communities

01:31:20:20 – 01:31:23:03
and make them aware

01:31:23:03 – 01:31:24:19
that we’d like them to collaborate

01:31:24:19 – 01:31:25:16
with us

01:31:25:16 – 01:31:26:12
to make sure

01:31:26:12 – 01:31:29:12
that they are represented in the data.

01:31:38:24 – 01:31:39:09
Okay.

01:31:39:09 – 01:31:40:20
Tim recently made a comment

01:31:40:20 – 01:31:42:14
about research,

01:31:42:14 – 01:31:45:17
and I’d like to piggyback

01:31:45:17 – 01:31:46:19
my question off of that.

01:31:46:19 – 01:31:49:19
Are there any publications, books,

01:31:49:24 – 01:31:51:14
resources, articles

01:31:51:14 – 01:31:53:21
related to the issue of A.I.

01:31:53:21 – 01:31:58:01
and the Deaf community in Conflict

01:31:59:17 – 01:32:02:10
and I’m sorry, together

01:32:02:10 – 01:32:05:04
intersection.

01:32:05:04 – 01:32:08:03
Can you spell that again?

01:32:08:03 – 01:32:09:20
I’m sorry.

01:32:09:20 – 01:32:10:16
Intersection.

01:32:10:16 – 01:32:13:16
Intersection.

01:32:13:18 – 01:32:16:13
Okay, so my focus in the research

01:32:16:13 – 01:32:19:19
is more on the technical side of things.

01:32:19:19 – 01:32:22:15
I’m not necessarily too heavy on the

01:32:23:14 – 01:32:24:23
on the other

01:32:24:23 – 01:32:26:20
area with the deaf community

01:32:26:20 – 01:32:28:19
and looking into the socio side of it.

01:32:28:19 – 01:32:29:18
But I do think it’s

01:32:29:18 – 01:32:32:21
very interesting research and

01:32:34:02 – 01:32:36:01
I think it’s a good way to do

01:32:36:01 – 01:32:39:08
some formal search of the literature.

01:32:39:14 – 01:32:40:21
So I would suggest

01:32:40:21 – 01:32:42:07
that if you’re interested in this,

01:32:42:07 – 01:32:43:17
that you look at things

01:32:43:17 – 01:32:45:02
like, for example,

01:32:45:02 – 01:32:48:20
Richard Dana or Danny Bragg

01:32:49:04 – 01:32:51:01
and they both have worked

01:32:51:01 – 01:32:54:02
focus on the ethical aspect

01:32:54:22 – 01:32:59:24
of these and the surrounding topics.

01:32:59:24 – 01:33:01:04
And so it’s a lot,

01:33:01:04 – 01:33:02:08
but I could probably

01:33:02:08 – 01:33:06:13
share a bibliography for you all

01:33:06:13 – 01:33:07:16
so that you could take a look

01:33:07:16 – 01:33:08:15
at those authors

01:33:08:15 – 01:33:11:15
and look more into their work.

01:33:13:21 – 01:33:15:14
I wish we could,

01:33:15:14 – 01:33:15:24
you know,

01:33:15:24 – 01:33:17:09
with the advisory group,

01:33:17:09 – 01:33:20:03
we touch on so many things

01:33:20:03 – 01:33:21:20
and I wish we could keep discussing

01:33:21:20 – 01:33:25:01
how the Germans are.

01:33:25:01 – 01:33:26:23
The shared study and research

01:33:26:23 – 01:33:28:04
really impacts everything.

01:33:30:00 – 01:33:30:19
Good idea.

01:33:30:19 – 01:33:33:19
Good idea.

01:33:40:01 – 01:33:40:16
Okay.

01:33:40:16 – 01:33:43:05
Any more questions?

01:33:43:05 – 01:33:45:07
Okay, I do have

01:33:45:07 – 01:33:47:09
I have more questions here,

01:33:47:09 – 01:33:50:09
so I will

01:33:50:23 – 01:33:53:08
copy exactly from what

01:33:53:08 – 01:33:54:11
I’m seeing here on the question,

01:33:54:11 – 01:33:55:15
this might be a good question

01:33:55:15 – 01:33:57:07
for Jeff to answer.

01:33:57:07 – 01:34:00:08
So, Jeff, we’re wondering

01:34:00:08 – 01:34:02:15
if the deaf community

01:34:02:15 – 01:34:05:15
is open to this system.

01:34:06:05 – 01:34:09:05
So for each individual user,

01:34:11:19 – 01:34:14:06
will they all be trained

01:34:14:06 – 01:34:17:19
and taught in their language of ASL?

01:34:17:19 – 01:34:18:14
For example,

01:34:18:14 – 01:34:20:07
if there’s a website,

01:34:20:07 – 01:34:23:07
well, that have American Sign language

01:34:23:09 – 01:34:26:09
and will it be copied and saved,

01:34:26:18 – 01:34:30:07
and will there be anything like

01:34:30:07 – 01:34:31:11
if this is saved,

01:34:31:11 – 01:34:32:16
will personal information

01:34:32:16 – 01:34:34:14
be saved in a server?

01:34:34:14 – 01:34:37:04
How will they ensure confidentiality?

01:34:37:04 – 01:34:37:16
While deaf

01:34:37:16 – 01:34:41:12
individuals have personal rights

01:34:41:12 – 01:34:43:06
to say yes, I’m

01:34:43:06 – 01:34:44:19
okay with releasing my information

01:34:44:19 – 01:34:46:14
at this company, How will that work?

01:34:48:14 – 01:34:50:10
Yeah, that’s a great question.

01:34:50:10 – 01:34:53:04
I think the formal term for

01:34:53:04 – 01:34:56:04
that is called fine tuning.

01:34:57:13 – 01:35:00:08
So with fine tuning you can go in

01:35:00:08 – 01:35:03:13
and make sure that the model is a fit

01:35:03:13 – 01:35:05:12
for your personal style,

01:35:05:12 – 01:35:08:11
your personal terms of choice.

01:35:08:11 – 01:35:11:15
So we can do that currently with English.

01:35:11:15 – 01:35:13:07
When it comes to Elm,

01:35:13:07 – 01:35:15:08
large language models like for example,

01:35:15:08 – 01:35:18:19
Chat, GPT, you can go in and personalize

01:35:18:19 – 01:35:20:01
and bind tune

01:35:20:01 – 01:35:21:23
the data that comes out of it.

01:35:21:23 – 01:35:23:15
And so I would imagine

01:35:23:15 – 01:35:24:09
that the same thing

01:35:24:09 – 01:35:25:09
will eventually happen

01:35:25:09 – 01:35:28:18
when it comes to AI by AI and also

01:35:28:18 – 01:35:31:18
in terms of sharing with others.

01:35:31:23 – 01:35:34:19
I don’t say I don’t see any reason why

01:35:34:19 – 01:35:36:01
it would take a long time

01:35:36:01 – 01:35:36:14
to be able

01:35:36:14 – 01:35:38:07
to for people

01:35:38:07 – 01:35:40:01
to give their informed consent.

01:35:40:01 – 01:35:41:15
So it is your data

01:35:41:15 – 01:35:42:20
and you’ll be able to do

01:35:42:20 – 01:35:44:22
whatever you want to do with it.

01:35:44:22 – 01:35:46:23
That would be the idea.

01:35:46:23 – 01:35:48:02
I don’t know if anyone has

01:35:48:02 – 01:35:49:08
anything else to add to that

01:35:51:14 – 01:35:52:16
Emery here, I

01:35:52:16 – 01:35:54:05
would like to add a comment.

01:35:54:05 – 01:35:55:19
I think that

01:35:55:19 – 01:35:58:19
the community feels empowered

01:35:59:14 – 01:36:02:11
to have these options available.

01:36:02:11 – 01:36:05:00
So I think whether or not

01:36:05:00 – 01:36:07:02
everyone agrees or not,

01:36:07:02 – 01:36:08:18
the idea of having options

01:36:08:18 – 01:36:11:10
available to personalize their data

01:36:11:10 – 01:36:12:23
I think would be optimal

01:36:12:23 – 01:36:15:23
and well received in general

01:36:17:16 – 01:36:19:01
to say, okay, you know,

01:36:19:01 – 01:36:20:15
I see that this is happening,

01:36:20:15 – 01:36:22:10
but I’m just one person.

01:36:22:10 – 01:36:25:01
But that would be my my guess.

01:36:25:01 – 01:36:26:17
This is Tim.

01:36:26:17 – 01:36:28:22
I remember back in what was it, 2001

01:36:28:22 – 01:36:30:11
when I was in college,

01:36:30:11 – 01:36:33:02
we had speech recognition software

01:36:33:02 – 01:36:35:05
and it was called Dragon

01:36:35:05 – 01:36:37:04
Natural Speaking.

01:36:37:04 – 01:36:41:04
And so that required a lot of training,

01:36:41:12 – 01:36:44:24
a lot of feeding into the technology to

01:36:44:24 – 01:36:46:00
develop it.

01:36:46:00 – 01:36:47:22
But there was some control

01:36:47:22 – 01:36:49:18
of sound and clarity.

01:36:49:18 – 01:36:51:24
But I think today the technology

01:36:51:24 – 01:36:54:01
seems to have gotten even better,

01:36:54:01 – 01:36:57:06
and so there’s less of a curve there.

01:36:57:06 – 01:36:59:09
But I think it would be possible

01:36:59:09 – 01:37:01:16
for bias to still be involved.

01:37:01:16 – 01:37:03:09
And so, of course, we see

01:37:03:09 – 01:37:04:16
that it’s quite standard English,

01:37:04:16 – 01:37:06:05
especially in particular tool

01:37:06:05 – 01:37:07:03
I was just discussing.

01:37:07:03 – 01:37:10:08
So when it comes to dialect and accents

01:37:10:08 – 01:37:11:07
and that kind of thing,

01:37:11:07 – 01:37:12:13
for my understanding,

01:37:12:13 – 01:37:14:17
the bigger concern is coming from

01:37:14:17 – 01:37:15:20
the greater community

01:37:15:20 – 01:37:16:14
based on what we see

01:37:16:14 – 01:37:18:03
on speech recognition.

01:37:18:03 – 01:37:20:02
So we can only imagine

01:37:20:02 – 01:37:20:21
what it might look like

01:37:20:21 – 01:37:21:19
with sign language.

01:37:21:19 – 01:37:23:08
So with over 20 years

01:37:23:08 – 01:37:25:11
invested in these types of tools

01:37:25:11 – 01:37:27:10
and and seeing how that goes,

01:37:27:10 – 01:37:28:04
I think that

01:37:28:04 – 01:37:29:23
we need to take into consideration

01:37:29:23 – 01:37:32:04
that we would need better technology

01:37:32:04 – 01:37:35:04
and we would need, you know, that,

01:37:35:14 – 01:37:37:13
for example, leapfrog technology

01:37:37:13 – 01:37:40:13
where we would come in and

01:37:40:20 – 01:37:41:24
be able to say,

01:37:41:24 – 01:37:43:04
we’ve already seen these things

01:37:43:04 – 01:37:45:05
develop, we’ve seen how this has gone.

01:37:45:05 – 01:37:46:22
So maybe we wouldn’t

01:37:46:22 – 01:37:48:23
need as much time to catch up.

01:37:48:23 – 01:37:49:24
I would hope,

01:37:49:24 – 01:37:50:22
like I mentioned, that

01:37:50:22 – 01:37:53:22
that dragon technology’s like 20 years in

01:37:53:22 – 01:37:57:22
so I think that I’m not necessarily

01:37:57:22 – 01:38:00:12
into the technical aspect as much as,

01:38:00:12 – 01:38:01:02
but that’s just

01:38:01:02 – 01:38:02:17
based on my experience time.

01:38:02:17 – 01:38:04:06
I predict it could go.

01:38:07:12 – 01:38:08:24
Holly says the person that asked

01:38:08:24 – 01:38:10:08
that question added a comment

01:38:10:08 – 01:38:12:11
and said, Yes,

01:38:12:11 – 01:38:14:09
I know exactly what you’re talking about.

01:38:14:09 – 01:38:15:09
The dragon, naturally

01:38:15:09 – 01:38:16:10
speaking technology.

01:38:16:10 – 01:38:18:20
I’m familiar with that.

01:38:18:20 – 01:38:20:14
Okay.

01:38:20:14 – 01:38:23:14
Another question says,

01:38:23:23 – 01:38:26:23
I am a deaf leader in my community.

01:38:28:13 – 01:38:32:19
How can we build more of a

01:38:33:04 – 01:38:34:17
bond,

01:38:34:17 – 01:38:37:18
a stronger bond with the interpreters?

01:38:38:03 – 01:38:40:14
Because many of them are afraid

01:38:40:14 – 01:38:43:14
to reach out to the deaf?

01:38:45:01 – 01:38:47:08
And said General question.

01:38:47:08 – 01:38:49:10
Emery says, Can you repeat the question,

01:38:49:10 – 01:38:52:10
please?

01:38:53:08 – 01:38:54:13
It’s a general question.

01:38:54:13 – 01:38:55:16
Holly says,

01:38:55:16 – 01:38:58:16
I am a deaf leader in my community

01:38:59:10 – 01:39:02:06
and I want to know how we as the deaf

01:39:02:06 – 01:39:05:07
community, can build stronger bonds

01:39:06:19 – 01:39:09:18
with interpreters.

01:39:09:18 – 01:39:12:17
Many interpreters afraid,

01:39:12:17 – 01:39:15:17
and they don’t reach out to us and from

01:39:15:17 – 01:39:16:11
the deaf community,

01:39:19:01 – 01:39:21:14
Tim says.

01:39:21:14 – 01:39:24:08
I think that

01:39:24:08 – 01:39:26:18
it’s all comes back to trust

01:39:26:18 – 01:39:28:10
trust issues.

01:39:28:10 – 01:39:29:23
So from my understanding,

01:39:29:23 – 01:39:32:06
many deaf people are afraid

01:39:32:06 – 01:39:34:03
and resistant to technology

01:39:34:03 – 01:39:36:17
because they’re afraid that

01:39:36:17 – 01:39:38:03
this technology

01:39:38:03 – 01:39:39:10
would take away their ability

01:39:39:10 – 01:39:40:20
to have an informed choice

01:39:40:20 – 01:39:42:00
to make decisions.

01:39:42:00 – 01:39:44:03
Same things with same thing with video

01:39:44:03 – 01:39:45:05
remote interpreting.

01:39:45:05 – 01:39:47:04
A lot of deaf people didn’t want that

01:39:47:04 – 01:39:48:00
because,

01:39:48:00 – 01:39:48:05
you know,

01:39:48:05 – 01:39:49:23
they have to fight the system

01:39:49:23 – 01:39:51:22
to get an in-person interpreter.

01:39:51:22 – 01:39:53:11
So based on that experience

01:39:53:11 – 01:39:54:16
and, those challenges,

01:39:54:16 – 01:39:57:16
it’s caused a lot of issues with trust.

01:39:57:19 – 01:40:00:01
And

01:40:00:01 – 01:40:00:16
I know

01:40:00:16 – 01:40:03:03
especially when it comes to health care,

01:40:03:03 – 01:40:04:12
it’s already challenging

01:40:04:12 – 01:40:05:14
to make appointments

01:40:05:14 – 01:40:06:07
to come

01:40:06:07 – 01:40:09:07
in, to have access and all of that.

01:40:09:10 – 01:40:12:20
So it can be very exhausting and cause

01:40:12:20 – 01:40:13:15
deaf individuals

01:40:13:15 – 01:40:14:04
to feel like

01:40:14:04 – 01:40:15:08
they don’t want to go to the doctor

01:40:15:08 – 01:40:16:09
because they don’t want to deal

01:40:16:09 – 01:40:17:14
with all of that.

01:40:17:14 – 01:40:19:17
Now, when it comes to interpreters,

01:40:19:17 – 01:40:23:10
of course, there are some things

01:40:23:10 – 01:40:24:00
to consider,

01:40:24:00 – 01:40:25:21
like the code of ethics, code of coverage

01:40:25:21 – 01:40:26:18
reality.

01:40:26:18 – 01:40:28:13
And I think deaf people may feel,

01:40:28:13 – 01:40:29:09
you know what,

01:40:29:09 – 01:40:32:09
I would prefer to have a I, because

01:40:32:23 – 01:40:35:12
in this case, there’s no baggage.

01:40:35:12 – 01:40:36:15
I don’t have to deal with

01:40:36:15 – 01:40:39:15
the human aspect of trust issues.

01:40:39:16 – 01:40:42:11
I know that with AI

01:40:42:11 – 01:40:45:08
and that kind of collaboration and dialog

01:40:45:08 – 01:40:47:12
also, it can lead to the discussion

01:40:47:12 – 01:40:48:18
of what the meaning

01:40:48:18 – 01:40:50:12
of trust is, what confidentiality

01:40:50:12 – 01:40:51:14
should look like,

01:40:51:14 – 01:40:53:24
the code of ethics, how that applies,

01:40:53:24 – 01:40:55:04
and just to make sure

01:40:55:04 – 01:40:58:02
that if our expectations of AI

01:40:58:02 – 01:41:00:02
are high and

01:41:01:14 – 01:41:02:15
we need to know

01:41:02:15 – 01:41:03:09
those accurate

01:41:03:09 – 01:41:04:20
or what could we expect something

01:41:04:20 – 01:41:06:08
that we’ve had with the human experience.

01:41:06:08 – 01:41:09:08
So that’s another discussion to have.

01:41:10:15 – 01:41:12:03
Theresa I don’t know if you want to add

01:41:12:03 – 01:41:13:01
to that.

01:41:13:01 – 01:41:15:02
Theresa says Yes, I’m just thinking.

01:41:15:02 – 01:41:18:23
I think that maybe the first step

01:41:19:02 – 01:41:21:18
would be to start these discussions

01:41:21:18 – 01:41:24:15
and to have some time in smaller

01:41:24:15 – 01:41:25:16
communities

01:41:25:16 – 01:41:28:08
where everyone knows each other, right?

01:41:28:08 – 01:41:29:11
We are all familiar

01:41:29:11 – 01:41:30:23
with those types of situations

01:41:30:23 – 01:41:32:07
where the interpreters and all the deaf

01:41:32:07 – 01:41:33:04
people and the deaf people,

01:41:33:04 – 01:41:34:07
not the interpreters,

01:41:34:07 – 01:41:37:22
but maybe we would start with some kind

01:41:37:22 – 01:41:39:12
of, let’s say, for example,

01:41:39:12 – 01:41:43:07
have your local deaf

01:41:43:07 – 01:41:47:17
community groups and local or I.D.

01:41:47:19 – 01:41:49:10
or interpreting organizations

01:41:49:10 – 01:41:50:20
come together.

01:41:50:20 – 01:41:51:13
So, for example,

01:41:51:13 – 01:41:52:13
maybe they come together

01:41:52:13 – 01:41:53:18
and watch this video

01:41:53:18 – 01:41:55:17
and then they host a discussion

01:41:55:17 – 01:41:57:02
and ask questions.

01:41:57:02 – 01:41:59:22
But I’m just thinking, how can we start

01:41:59:22 – 01:42:01:17
to develop this discussion

01:42:01:17 – 01:42:03:02
and this dialog?

01:42:03:02 – 01:42:05:05
Because without the dialog,

01:42:05:05 – 01:42:07:08
there’s so many misunderstandings.

01:42:07:08 – 01:42:08:19
And I think that

01:42:08:19 – 01:42:10:19
with the dialog, misunderstandings

01:42:10:19 – 01:42:12:08
will still happen as well,

01:42:12:08 – 01:42:14:06
but is definitely an opportunity

01:42:14:06 – 01:42:14:23
to have

01:42:14:23 – 01:42:16:00
more understanding

01:42:16:00 – 01:42:17:03
and more of an opportunity

01:42:17:03 – 01:42:18:15
to listen to each other

01:42:18:15 – 01:42:20:20
and figure out how we can discuss

01:42:20:20 – 01:42:23:09
this together is huge.

01:42:23:09 – 01:42:24:13
It’s coming

01:42:24:13 – 01:42:26:08
and we need to be sure

01:42:26:08 – 01:42:28:18
that we know how to respond to this.

01:42:28:18 – 01:42:30:08
We need to start with

01:42:30:08 – 01:42:32:24
the grassroots community

01:42:32:24 – 01:42:34:08
and go from there.

01:42:34:08 – 01:42:35:22
So the grassroots

01:42:35:22 – 01:42:36:16
community is the heart

01:42:36:16 – 01:42:37:16
of our deaf community.

01:42:43:04 – 01:42:44:04
Thank you, Anne Marie.

01:42:44:04 – 01:42:44:24
And Jeff, would you

01:42:44:24 – 01:42:46:13
do you have anything you want to add?

01:42:46:13 – 01:42:49:13
Just saying I agree wholeheartedly.

01:42:50:04 – 01:42:51:20
Emery Here,

01:42:51:20 – 01:42:55:18
this one topic itself is just it.

01:42:56:03 – 01:42:57:09
It’s huge.

01:42:57:09 – 01:42:59:09
There’s no way to describe it.

01:42:59:09 – 01:43:00:05
Otherwise,

01:43:00:05 – 01:43:01:21
there are so many things

01:43:01:21 – 01:43:04:17
to look at the process itself,

01:43:04:17 – 01:43:08:02
the trust in the process, the transport

01:43:08:03 – 01:43:11:03
tenancy, the data collection, all of it

01:43:11:06 – 01:43:12:17
together.

01:43:12:17 – 01:43:13:17
But you know,

01:43:13:17 – 01:43:16:06
and how everything moves in tandem.

01:43:16:06 – 01:43:17:06
It’s an opportunity

01:43:17:06 – 01:43:20:06
to always create a safe space

01:43:20:23 – 01:43:23:06
for the community to come together

01:43:23:06 – 01:43:24:17
and to discuss.

01:43:24:17 – 01:43:25:19
And it’s important

01:43:25:19 – 01:43:26:08
for us

01:43:26:08 – 01:43:29:08
to look at that process as a whole.

01:43:31:13 – 01:43:33:04
Very good discussion.

01:43:33:04 – 01:43:35:11
Very good discussion,

01:43:35:11 – 01:43:35:24
Ali, saying,

01:43:35:24 – 01:43:38:24
okay, next question is about the research

01:43:38:24 – 01:43:42:14
process from last October.

01:43:42:14 – 01:43:45:14
You had three different webinar sessions,

01:43:45:18 – 01:43:48:15
the deaf participants in those sessions.

01:43:48:15 – 01:43:49:24
What were their backgrounds,

01:43:49:24 – 01:43:52:19
where were they from geographically?

01:43:52:19 – 01:43:54:07
And

01:43:55:22 – 01:43:58:22
demographically?

01:43:59:03 – 01:44:00:20
Just saying, go ahead.

01:44:00:20 – 01:44:01:07
Emery

01:44:01:07 – 01:44:03:21
So the backgrounds were very diverse.

01:44:03:21 – 01:44:05:18
We had some that were deaf interpreters,

01:44:05:18 – 01:44:07:13
deaf consumers,

01:44:07:13 – 01:44:11:05
deaf individuals who are professionals

01:44:11:10 – 01:44:14:08
working in the field of education,

01:44:14:08 – 01:44:16:07
working in the field of interpreting

01:44:16:07 – 01:44:17:21
very different varieties

01:44:17:21 – 01:44:20:21
of backgrounds and

01:44:21:12 – 01:44:23:04
areas as well.

01:44:23:04 – 01:44:24:22
Jeff Did you want to add some more?

01:44:24:22 – 01:44:25:20
Jeff Yes,

01:44:25:20 – 01:44:26:22
I think the next step

01:44:26:22 – 01:44:28:22
is to decide how we expand

01:44:28:22 – 01:44:30:11
and how we grow our audience

01:44:30:11 – 01:44:32:05
from those webinars

01:44:32:05 – 01:44:34:00
and to really include

01:44:34:00 – 01:44:35:21
even more of the community at large,

01:44:35:21 – 01:44:38:21
to have all those perspectives as well

01:44:40:06 – 01:44:41:09
in the saying yes,

01:44:41:09 – 01:44:42:23
the webinars were really

01:44:42:23 – 01:44:47:15
our first step into this

01:44:47:15 – 01:44:49:19
realm of testing out this,

01:44:49:19 – 01:44:50:21
looking at different things,

01:44:50:21 – 01:44:52:02
and this symposium

01:44:52:02 – 01:44:53:04
will just continue

01:44:53:04 – 01:44:55:21
to be a springboard into the future

01:44:55:21 – 01:44:57:17
and as long as we have a

01:44:57:17 – 01:44:59:13
I will be having these discussions

01:45:00:24 – 01:45:01:16
in raising.

01:45:01:16 – 01:45:02:11
I’d like to add

01:45:02:11 – 01:45:06:05
also that all of the individuals,

01:45:06:13 – 01:45:08:03
the participants here today,

01:45:08:03 – 01:45:09:16
you guys are critical

01:45:09:16 – 01:45:11:16
for this process as well.

01:45:11:16 – 01:45:13:01
We are not finished now

01:45:13:01 – 01:45:14:15
that this report is published.

01:45:14:15 – 01:45:16:05
This is just the beginning.

01:45:16:05 – 01:45:18:12
US and even all of your questions

01:45:18:12 – 01:45:20:04
have really spurred our thoughts

01:45:20:04 – 01:45:22:04
into how we move this forward

01:45:22:04 – 01:45:23:04
and what the next steps

01:45:23:04 – 01:45:24:06
are in this process.

01:45:24:06 – 01:45:27:06
We’ve just scratched the surface.

01:45:34:04 – 01:45:35:21
Okay, I’ll help you think.

01:45:35:21 – 01:45:36:23
We have navigated

01:45:36:23 – 01:45:38:06
through all of the questions

01:45:38:06 – 01:45:39:22
that we have for the day.

01:45:39:22 – 01:45:45:02
We do have one more, but it is

01:45:45:07 – 01:45:46:17
with this

01:45:46:17 – 01:45:49:17
webinar, complete this report, complete

01:45:49:21 – 01:45:52:01
the sharing of the recording

01:45:52:01 – 01:45:53:10
When will that be done?

01:45:53:10 – 01:45:57:08
The Deaf Safe

01:45:57:16 – 01:46:01:10
Advisory website, the report,

01:46:01:16 – 01:46:03:05
if that will be shared,

01:46:03:05 – 01:46:06:09
and how to register

01:46:06:09 – 01:46:09:09
for the Brown University Symposium.

01:46:09:16 – 01:46:10:14
Several people have asked

01:46:10:14 – 01:46:12:06
for that information as well.

01:46:14:18 – 01:46:17:11
Tim here, so I will answer that.

01:46:17:11 – 01:46:21:10
The report we have both the safe

01:46:21:18 – 01:46:25:19
I their report as well as ours,

01:46:25:19 – 01:46:27:00
and we’ve been working

01:46:27:00 – 01:46:29:08
with Katha research.

01:46:29:08 – 01:46:31:10
They have spent so much time

01:46:31:10 – 01:46:32:21
going through this project.

01:46:32:21 – 01:46:36:04
The surveys, volunteers, so much work

01:46:36:04 – 01:46:37:13
has gone into that report

01:46:37:13 – 01:46:38:04
and that will

01:46:38:04 – 01:46:40:00
and will be published as well.

01:46:40:00 – 01:46:41:15
We had a tech

01:46:41:15 – 01:46:43:12
a presentation scheduled yesterday,

01:46:43:12 – 01:46:45:11
but there was technological problems

01:46:45:11 – 01:46:47:00
and so we’re going to be rescheduling

01:46:47:00 – 01:46:47:24
that for Wednesday morning,

01:46:47:24 – 01:46:50:24
I believe, at 11 Eastern time.

01:46:51:00 – 01:46:54:02
And so with that being said,

01:46:54:02 – 01:46:55:13
I really encourage you all

01:46:55:13 – 01:46:57:12
to watch that presentation.

01:46:57:12 – 01:46:59:08
It’s about languages in general,

01:46:59:08 – 01:47:00:14
not just sign languages,

01:47:00:14 – 01:47:01:23
it’s languages in general.

01:47:01:23 – 01:47:03:17
And so that will be made

01:47:03:17 – 01:47:05:10
available to the public as well.

01:47:05:10 – 01:47:06:06
This one,

01:47:06:06 – 01:47:09:17
I believe we have some editing to do

01:47:09:23 – 01:47:12:10
and we will make it available here soon.

01:47:12:10 – 01:47:13:10
The presentation

01:47:13:10 – 01:47:14:01
next Wednesday

01:47:14:01 – 01:47:16:07
will also be available online.

01:47:16:07 – 01:47:18:22
Our website does have links

01:47:18:22 – 01:47:21:22
to Safe A.I.

01:47:22:01 – 01:47:23:11
Advisory Group,

01:47:23:11 – 01:47:25:04
and then we have our own Death

01:47:25:04 – 01:47:27:04
Advisory Group page

01:47:27:04 – 01:47:29:02
and the links to those.

01:47:29:02 – 01:47:30:08
I will thin them out

01:47:30:08 – 01:47:32:07
and make it available there

01:47:32:07 – 01:47:34:16
also the symposium

01:47:34:16 – 01:47:37:16
we are currently working on that platform

01:47:37:20 – 01:47:38:22
and the save

01:47:38:22 – 01:47:40:12
the date is just announced today.

01:47:40:12 – 01:47:42:16
This is our first announcement for that

01:47:42:16 – 01:47:44:19
I will send out a more formal

01:47:44:19 – 01:47:45:14
save the date

01:47:45:14 – 01:47:47:22
with information on registration

01:47:47:22 – 01:47:49:02
and the like.

01:47:49:02 – 01:47:50:05
It will be sent out.

01:47:50:05 – 01:47:52:08
That is a currently in process.

01:47:52:08 – 01:47:53:19
So Look forward to that.

01:47:55:06 – 01:47:55:21
I do.

01:47:55:21 – 01:47:56:20
Any of the other

01:47:56:20 – 01:47:58:06
advisory council members

01:47:58:06 – 01:47:58:21
have something

01:47:58:21 – 01:48:01:10
that they would like to add.

01:48:01:10 – 01:48:02:05
Theresa Thing

01:48:02:05 – 01:48:03:23
I just want to thank all of you

01:48:03:23 – 01:48:06:12
and Mary saying, yes, I agree. Thank you.

01:48:06:12 – 01:48:08:09
Thank you for your interest

01:48:08:09 – 01:48:09:04
in this topic.

01:48:09:04 – 01:48:10:04
Thank you for coming

01:48:10:04 – 01:48:12:03
and listening to our presentation.

01:48:12:03 – 01:48:13:15
We Appreciate it.

01:48:13:15 – 01:48:15:09
We we think that it’s wonderful

01:48:15:09 – 01:48:16:19
that all of you were involved

01:48:16:19 – 01:48:18:22
and we really appreciate everyone

01:48:18:22 – 01:48:20:12
that was involved in the study

01:48:20:12 – 01:48:21:04
just said yes.

01:48:21:04 – 01:48:24:04
Thank you so much.

01:48:24:11 – 01:48:25:17
Thank you, Holly, as well

01:48:25:17 – 01:48:26:21
for your time today.

01:48:26:21 – 01:48:29:06
We appreciate you joining us

01:48:29:06 – 01:48:32:06
in this presentation.

01:48:32:07 – 01:48:35:07
Thank you very much,

01:48:37:06 – 01:48:37:12
Holly.

01:48:37:12 – 01:48:37:19
Same.

01:48:37:19 – 01:48:39:09
I want to make sure that we have a clear

01:48:39:09 – 01:48:40:13
answer about

01:48:40:13 – 01:48:40:22

01:48:40:22 – 01:48:43:10
if this recording of the webinar

01:48:43:10 – 01:48:44:22
will be broadcast

01:48:44:22 – 01:48:46:09
and then

01:48:46:09 – 01:48:47:17
we will be sending up

01:48:47:17 – 01:48:49:06
a follow up email as well.

01:48:49:06 – 01:48:49:19
For everyone

01:48:49:19 – 01:48:50:16
who registered

01:48:50:16 – 01:48:53:16
with the website information

01:48:53:16 – 01:48:56:24
and Brown University Symposium

01:48:58:07 – 01:48:59:23
information

01:48:59:23 – 01:49:03:02
and what else was there.

01:49:03:02 – 01:49:03:24
I think that’s it.

01:49:03:24 – 01:49:06:24
So we will correct?

01:49:07:15 – 01:49:08:21
Yes, simply yes,

01:49:08:21 – 01:49:09:24
we will all just say yes,

01:49:09:24 – 01:49:12:24
it will be all available.

01:49:15:22 – 01:49:18:15
The webinar also that we have,

01:49:18:15 – 01:49:19:23
we have the video

01:49:19:23 – 01:49:21:03
recordings of the webinars,

01:49:21:03 – 01:49:22:13
if you’d like to see those as well.

01:49:22:13 – 01:49:25:13
We those available.

01:49:29:23 – 01:49:31:24
All right,

01:49:31:24 – 01:49:33:18
Tim thing, I believe this concludes

01:49:33:18 – 01:49:36:18
our meeting.

01:49:38:24 – 01:49:40:10
Just saying Thank you, everyone.

01:49:40:10 – 01:49:42:06
We appreciate your time.

01:49:42:06 – 01:49:43:18
Thank you so much. Bye bye.