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Speaker 1: Hey, there, everybody, this is Jonathan Strickland, host of Tax Stuff. Today,

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Speaker 1: we have something a little different for you. We have

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Speaker 1: a series called First Contact with Laurie Siegel uh, former

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Speaker 1: correspondent with CNN, and if you have not listened to

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Speaker 1: that show, you definitely need to check it out. She's

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Speaker 1: doing amazing work having incredible conversations with thought leaders in

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Speaker 1: the tech space and the social space, and this particular

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Speaker 1: episode I think is really important and one that really

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Speaker 1: needs to be heard by as many folks as possible.

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Speaker 1: She sits down with a CLU president, Susan Herman, and

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Speaker 1: together they talk about the issue of racially biased face

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Speaker 1: recognition technology. And you've heard me speak on episodes of

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Speaker 1: Tech Stuff in the past about bias artificial intelligence, facial recognition,

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Speaker 1: you know, image recognition, that sort of stuff, and how

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Speaker 1: that is a real problem in tech and it's something

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Speaker 1: that we absolutely have to get our heads wrapped around

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Speaker 1: and address. So, without further ado, I'm going to have

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Speaker 1: that episode play in lieu of a Tech Stuff episode

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Speaker 1: for today, and I highly recommend you check out First Contact.

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Speaker 1: It's a great show and one that I think really

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Speaker 1: dove tails nicely with Tech Stuff. So if you enjoy

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Speaker 1: tech stuff, I think you're really gonna like First Contact

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Speaker 1: as well, and we will catch you next week with

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Speaker 1: new episodes of tech Stuff. Thanks. First Contact with Lori

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Speaker 1: Siegel is a production of Dot Dot Dot Media and

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Speaker 1: I Heart Radio. There's a great quote on the A

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Speaker 1: c l U website. The fact that technology now allows

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Speaker 1: an individual to carry such information in his hand does

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Speaker 1: not make the information any less worthy of the protection

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Speaker 1: for which the founders fought. Exactly. I like to talk about,

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Speaker 1: you know, one of the whole points of the Constitution

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Speaker 1: adding the fourth Amendment, which is the protection of privacy,

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Speaker 1: is they wanted to protect what was in Benjamin Franklin's disk.

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Speaker 1: Nobody should know if he was writing some things that

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Speaker 1: were anti government, and we now have that on our

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Speaker 1: cell phone, so of course, but that's where I think

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Speaker 1: that a lot of the protection of civil liberties is

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Speaker 1: applying our fundamental principles in different circumstances. We are in

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Speaker 1: a moment of reckoning as we enter an age of

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Speaker 1: ubiquitous surveillance, questionable data collection practices, even algorithms that discriminate.

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Speaker 1: It's minorities, especially black and brown communities that are disproportionately affected.

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Speaker 1: Over the last months, as the nation has grappled with

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Speaker 1: a conversation around police brutality, we've seen predator drones used

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Speaker 1: for aerial surveillance at protests, facial recognition technology that wrongfully

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Speaker 1: accused a black man of a crime he didn't commit,

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Speaker 1: and it wasn't a coincidence. Reports say the tech is

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Speaker 1: a hundred times more likely to misidentify African, American and

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Speaker 1: Asian people, and as COVID nineteen continues to spread, there

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Speaker 1: are serious questions being raised about contact tracing apps and

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Speaker 1: how that data collected could be misused. These issues raise

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Speaker 1: ethical questions about technology and its impact on our civil liberties, equality,

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Speaker 1: and the future of our country. For Susan Herman, it

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Speaker 1: is an extraordinary time to be sitting in her seat

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Speaker 1: as president of the a s l U. Over the years,

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Speaker 1: the American Civil Liberties Union has filed lawsuits fighting for

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Speaker 1: free speech, reproductive rights, and privacy. But as technology continues

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Speaker 1: to muddy the waters, the tradeoffs become more complicated. Where

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Speaker 1: do we draw the line between security and privacy and

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Speaker 1: how do we prevent technological innovation from outpacing the law.

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Speaker 1: I'm Laurie Siegel, and this is first contact, Susan, thank

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Speaker 1: you for being virtually with me today. Thank you for

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Speaker 1: inviting me, Laurie. Yeah, you know, I I always start

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Speaker 1: out these interviews with our first contact. I talked to

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Speaker 1: guests about how we met, and we don't really have

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Speaker 1: a first contact. We've never met in person, but we

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Speaker 1: met on an email chain because we were going to

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Speaker 1: do an interview together for something else and it and

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Speaker 1: it fell through. So I said, you've got to come

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Speaker 1: on the podcast because you are just sitting in such

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Speaker 1: an extraordinary seat at such an extraordinary moment in time.

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Speaker 1: So that's our first contact. Well, thanks, It just seems

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Speaker 1: to me like our first contact was total serendipity. Yeah, exactly,

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Speaker 1: so you know, to get started. You've been the president

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Speaker 1: of the a c l U since two thousand and eight,

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Speaker 1: and I said this before, but you know, what an

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Speaker 1: extraordinary time to be sitting in your seat. You know,

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Speaker 1: how are you feeling? Oh my, it's just sort of overwhelming.

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Speaker 1: You know. As president, I'm go the chair of the board,

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Speaker 1: so you know, I'm not the one doing the day

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Speaker 1: to day work as all of the members of our

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Speaker 1: staff are. But to be a member of the a

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Speaker 1: c l U staff right now is just it's mind

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Speaker 1: boggling because we had, you know, a lot of work

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Speaker 1: that we were already doing before two thousand and sixteen,

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Speaker 1: with all of the states making worse and worse laws

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Speaker 1: about reproductive freedom and voting rights and immigrants rights, and

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Speaker 1: you know, all sorts of other things. Then came the election,

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Speaker 1: and since that we have brought a hundreds and seventy

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Speaker 1: three legal actions against the Trump administration for things like

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Speaker 1: family separations and the travel BAM and prohibiting trans in

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Speaker 1: the military. Then in March, COVID hit, and at that point,

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Speaker 1: you know, since then, we've also brought over a hundred lawsuits,

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Speaker 1: including with a hundred lawsuits just about people who are

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Speaker 1: incarcerated in jails and prisons and ice attention and who

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Speaker 1: are just in a hotspot. You know, they have no

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Speaker 1: control over whether they can social distance, and so we've

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Speaker 1: been working very hard to get vulnerable people out of

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Speaker 1: those terrible situations, out of basically death traps. Plus, the

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Speaker 1: COVID also led to a number of states opportunistically restricting

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Speaker 1: things like freedom of abortion, declaring abortion to be a

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Speaker 1: non essential procedure so people could just wait until the

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Speaker 1: pandemic is over to get an abortion right, and voting

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Speaker 1: rights has also just been a really braught area right

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Speaker 1: now because all the restrictions on voting and the ways

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Speaker 1: in which the vote was becoming distorted. Have you just

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Speaker 1: been magnified by all the difficulties of So there's a

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Speaker 1: lot to talk about. So I was a gonna say,

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Speaker 1: what what what I'm hearing from is you're sleeping really

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Speaker 1: well at night. You know, there's no work to do,

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Speaker 1: almost nothing to do this stuff that they're just sitting

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Speaker 1: around polishing their nails. Yeah, I mean, like, take me

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Speaker 1: to March, like coronavirus hits. You have been involved in

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Speaker 1: some of these monumental cases that have just shaped society

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Speaker 1: in our civil liberties, like coronavirus hits. And now you know,

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Speaker 1: we have a little bit I don't even think we

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Speaker 1: have the luxury of perspective at this point, but we

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Speaker 1: have a little bit more perspective. But let's take me

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Speaker 1: to March, Like in your role at this extraordinary moment,

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Speaker 1: like what was going through your head? What were you

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Speaker 1: concerned about at the time. Well, you know, one of

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Speaker 1: the first concerns is just you have to close the office.

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Speaker 1: So the first concern is how can people do all

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Speaker 1: this umcity It increases the work and makes it more

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Speaker 1: difficult to do the work. So we just had to

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Speaker 1: really make sure that our technology was up to doing things.

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Speaker 1: So one thing that the a c l YOU did

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Speaker 1: was to buy new laptops for some stuff. People who

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Speaker 1: are going to be working and you have to worry

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Speaker 1: about how the technology is working um which has been

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Speaker 1: a question for us every time there's something really big hits.

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Speaker 1: When the travel band hit, there were so many people

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Speaker 1: wanting to donate to the a c l U. There

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Speaker 1: are website crash so even things like that, you know,

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Speaker 1: that's you know, like number one of how do you

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Speaker 1: handle this? We have been fortunate so far that the

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Speaker 1: a c l U is so well managed and we

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Speaker 1: had not spent every penny that all of our donors

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Speaker 1: had given us up until that point, so have not

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Speaker 1: had to laid people off, which is very fortunate because

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Speaker 1: as you know, saying, there's more than enough work to do.

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Speaker 1: But yeah, that's the first concern of just you know,

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Speaker 1: how do you keep the organization up to speed and

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Speaker 1: ready to do you know what. Staff members now need

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Speaker 1: to be doing an incredible amount more work. But for

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Speaker 1: some of them, it's well, they're juggling a toddler and

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Speaker 1: a dog. Yeah. Can you give me a run through

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Speaker 1: of some of the cases that you've been involved in

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Speaker 1: that correct me if I'm wrong. You started out as

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Speaker 1: an intern, right, and really just worked your way up.

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Speaker 1: I mean, I can imagine you've been involved, and I

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Speaker 1: know you've been involved in some pretty extraordinary cases. To

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Speaker 1: give listeners some context, can you explain some of the

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Speaker 1: cases that kind of stick out to you. Well, I

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Speaker 1: wasn't intern for the a c l U back, you know,

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Speaker 1: in the nineties seventies, you know, around the time when

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Speaker 1: I was in law school. And just to make sure

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Speaker 1: that everybody understands, I don't actually work at the a

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Speaker 1: c l U. Day job is I'm a law professor,

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Speaker 1: and I don't generally work on the cases. What I'm

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Speaker 1: generally doing is we run the organization. But I'll tell

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Speaker 1: you I think it would be interesting start um. But

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Speaker 1: the first a c l U case that I actually

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Speaker 1: did work on, which was while I was a law student,

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Speaker 1: and this was the case. One of my connections with

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Speaker 1: the a c l U originally was that one of

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Speaker 1: my law professors in the first year was connected with

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Speaker 1: the New York Civil Liberties Union, and he had some

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Speaker 1: clients who came to him who were graduate students at

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Speaker 1: Stony Brook on Allowland, and they had just discovered they

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Speaker 1: were not allowed to live together. They had rented a

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Speaker 1: house together, there were six of them, and they had

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Speaker 1: just discovered they weren't allowed to live together because there

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Speaker 1: was an ordinance in their village village called belt Hair,

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Speaker 1: that prohibited more than two persons unrelated by blood, marriage

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Speaker 1: or adoption from living together. So, you know, they were

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Speaker 1: pretty shocked. And it turned out that under the laws

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Speaker 1: it was at the time, by the time they were

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Speaker 1: talking about this, they were liable for all sorts of

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Speaker 1: criminal fines and punishment. It was really a very heavy stuff.

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Speaker 1: So I started working on that case with my law

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Speaker 1: professor and um we went to a federal job to

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Speaker 1: ask for a temporary restraining order, which means to just

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Speaker 1: until we had litigated whether or not that was a

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Speaker 1: constitutional thing to do, to tell people who they couldn't

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Speaker 1: couldn't live with that the village should not be allowed

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Speaker 1: to either kick them out of their their house or

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Speaker 1: to you know, start walking them up because you know,

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Speaker 1: they owned too many fines for having been illegal residents.

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Speaker 1: So um, the judge ended up signing the order, and

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Speaker 1: he was signing the order about that, and then one

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Speaker 1: of the ways they which actually the original way in

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Speaker 1: which our clients had discovered that they were illegal residents,

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Speaker 1: was that they had applied for residents only beach permit

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Speaker 1: and they were told they couldn't have one because they

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Speaker 1: were illegal residents. So the judge who we had the

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Speaker 1: district judge, who was a very nice man, looked at

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Speaker 1: the order we had written out and he said, well,

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Speaker 1: you know, it's the summer. Don't your clients want to

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Speaker 1: go to the beach while the litigation is pending? Do

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Speaker 1: you mind if I write that in that they have

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Speaker 1: to be allowed to park in the parking lot of

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Speaker 1: the beach. So we said sure, you know, that's very nice,

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Speaker 1: so he wrote that in. Then, as the junior member

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Speaker 1: of the team, I was sent out to explain to

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Speaker 1: our clients, to show them the order and explained to

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Speaker 1: them what was going on, and they gave me a

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Speaker 1: tour of you what the village looked like in the

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Speaker 1: residents only beach and the minute the wheels of their

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Speaker 1: car hit the parking lot is very large. Your fierce

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Speaker 1: looking man comes striding across and says, what are you

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Speaker 1: doing here? You're not allowed to be in this parking lot.

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Speaker 1: And they all look at me, and I'm thinking, what

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Speaker 1: am I? I'm like, you know, twenty something, I'm not

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Speaker 1: very tall, but what am I supposed to do with

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Speaker 1: this large man who doesn't want us in there in

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Speaker 1: his parking lot? And then I remembered that I had

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Speaker 1: a federal court order right on my person, so I

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Speaker 1: New York Civil Liberties Union kind of, and I have

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Speaker 1: a federal court order saying that these people are allowed

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Speaker 1: to be in this parking lot and go to the beach.

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Speaker 1: And he melted. And that that was, I think, in

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Speaker 1: one of the points at which I thought, Wow, you

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Speaker 1: know this, this is really powerful stuff. Yeah you saw

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Speaker 1: that there was my first day case. Yeah, exactly, that's great.

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Speaker 1: And I saw I read that. So maybe your your

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Speaker 1: earliest memories, um of speaking up to authority involved I

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Speaker 1: think a dispute over a book at your school library. Yeah,

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Speaker 1: that's right, even before the Belchair case. My first civil

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Speaker 1: liberties hero was my mother. So when I was in

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Speaker 1: third grade, we were doing a school play about a

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Speaker 1: story called Johnny Tremaine, about a boy in the American Revolution,

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Speaker 1: don't have that many words, and we were told that

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Speaker 1: this was based on the book. So I went to

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Speaker 1: to take out the book, and the librarian said, oh,

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Speaker 1: boys section. And I was I was surprised to find

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Speaker 1: which were all collections of fairy tales and biographies of

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Speaker 1: president's wives, but it had never occurred to me that

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Speaker 1: boys section. So I went home and I told my

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Speaker 1: mother about this. You're just thinking, you know, that's the

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Speaker 1: way things are, and she just exploded and she called

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Speaker 1: the librarian the next day and say, how dare you

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Speaker 1: told my daughter you know what she's not allowed to read.

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Speaker 1: So the librarian told me that from then on, I

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Speaker 1: could take out any book I wanted, and you know,

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Speaker 1: not long after that, they changed the policy for everyone.

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Speaker 1: So you know, there was another example of how you

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Speaker 1: you from making your own choices. Were you always like that? Well,

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Speaker 1: you know, that's third grade and I feel like yes,

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Speaker 1: I think for most of us are values of form

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Speaker 1: when we're pretty young. Yeah, so you know, seeing my

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Speaker 1: mother do that, I'm sure you would have had an

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Speaker 1: impact on me. Yeah, that's such a good story. And

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Speaker 1: did you I mean, did you always know you wanted

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Speaker 1: to go into law? No? I actually really didn't, because

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Speaker 1: having grown up as a woman during that era, my

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Speaker 1: profession for women. Why would you want to do that

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Speaker 1: if you could be an English teacher and have the

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Speaker 1: summer off, you take care of your children, so you

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Speaker 1: have to be a while. I graduated from college and

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Speaker 1: then spent a few years doing other things and then

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Speaker 1: decided to go to law school. Well, I mean, it's

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Speaker 1: it's so interesting and now to seeing where you're at

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Speaker 1: um and seeing this moment, it does feel like a moment.

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Speaker 1: And I was looking at something you said about you know,

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Speaker 1: this feels like a moment we can be optimistic because

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Speaker 1: so many Americans are beginning to really understand the scope

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Speaker 1: and the depth of structural racism. It certainly feels, you know,

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Speaker 1: I'm based in New York City. You can just feel

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Speaker 1: it right on the streets with the protests, and they

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Speaker 1: hear the sirens and the helicopters, you know, as we

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Speaker 1: sit here, um and we hear you know, your rich

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Speaker 1: history and covering and caring about these issues. What is

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Speaker 1: the challenge for you guys ahead, Well, you know, the

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Speaker 1: challenge on that particular subject is that this is work

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Speaker 1: that we had already been doing. One of our top

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Speaker 1: priorities for the past several years has been trying to

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Speaker 1: break our addiction to mass incarceration, which, as everybody is

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Speaker 1: now really coming to terms with, has been really it's

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Speaker 1: a system that has disproportionately affected people on the basis

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Speaker 1: of race and income and disability of co to the

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Speaker 1: people who are arrested or people who are mentally ill,

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Speaker 1: And our feeling is that the system has been fundamentally

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Speaker 1: broken and misguided for a long time. So part of

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Speaker 1: what we're trying to do with this moment is to

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Speaker 1: capitalize on the fact that people want to look at

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Speaker 1: what the police do. We're trying to encourage people to

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Speaker 1: look beyond the police. It's not just you. Who are

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Speaker 1: the police arresting and how are they treating the people

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Speaker 1: they arrest? I think behind that is the question of

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Speaker 1: what do we really want to treat as a crime.

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Speaker 1: So when you treat all sorts of very minor misconduct

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Speaker 1: as a crime, you're really setting up a situation where

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Speaker 1: they're going to be more contacts and therefore potentially more

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Speaker 1: arbitrary and discriminatory context. So, if you think about it,

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Speaker 1: Eric Garner ended up dying because he was selling single

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Speaker 1: cigarettes on which the tax had not been paid. George Floyd.

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Speaker 1: The basis for that encounter was that they thought he

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Speaker 1: might be passing a counterfeit twenty dollar bill. So I

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Speaker 1: think that if you look at why are we criminalizing

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Speaker 1: some of the things we criminalize, especially if you're talking

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Speaker 1: about people who are mentally ill and are having problems.

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Speaker 1: Do we really want the police to be the people

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Speaker 1: who are the first responders to people who are having

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Speaker 1: a mental health crisis or is there some more effective

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Speaker 1: way to deal with that that would avoid putting those

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Speaker 1: people into the criminal justice system, which isn't really good

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Speaker 1: for anyone, and to maybe recommit, reallocate some of the

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Speaker 1: resources we're using on arresting people and locking them up

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Speaker 1: to actually dealing with the mental health crises. You have

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Speaker 1: mental health treatment. So instead of viewing everything as well

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Speaker 1: dysfunction as a matter of policing, why don't we spend

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Speaker 1: more time reinvesting and to try to prevent more dysfunction.

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Speaker 1: Instort of like the old saying, you know, if you're

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Speaker 1: a hammer, everything looks like a nail. Well, you know,

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Speaker 1: not every problem in our society is a problem for

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Speaker 1: the criminal justice system, and an occasion to arrest people

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Speaker 1: and lock them up a lot of them really should

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Speaker 1: be an occasion for thinking about public health treatments. I'm

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Speaker 1: thinking about how we want to approach homelessness, and you

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Speaker 1: have a lot of much deeper thoughts about how you

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Speaker 1: prevent dysfunction. Rather than answering everything with you we're going

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Speaker 1: to send in the police. It certainly seems also like

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Speaker 1: this moment, even coming out of the pandemic, I can

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Speaker 1: only imagine the mental health crisis is going to be

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Speaker 1: even worse. Yeah, that could well be um and I

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Speaker 1: think the pandemic is also showing us. Somebody asked me

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Speaker 1: the other day whether the protests over policing and police

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Speaker 1: brutality are related to the pandemic, and I was in

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Speaker 1: a webinar and one of the smart people in the

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Speaker 1: room said, oh, no, no, they're two entirely different things.

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Speaker 1: But I said, what do you mean. The same people

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Speaker 1: who are being disproportionately affected by policing and police brutality

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Speaker 1: are the people who are being disproportionately affected by COVID.

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Speaker 1: The statistics is that people of color are much more

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Speaker 1: likely to die, and there are a lot of reasons

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Speaker 1: for that. You're having to do with your underlying health

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Speaker 1: and having to do with the fact that minorities and

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Speaker 1: people who are not affluent don't get to work from home,

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Speaker 1: they don't get to work through zoom. There are the

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Speaker 1: people who are out there on the streets being the

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Speaker 1: first risk wanders, being the people who are picking up

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Speaker 1: the garbage, being the people who was talking the supermarket shills.

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Speaker 1: And I feel like the virus is really amplifying so

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Speaker 1: many of the inequities we've had in our society. And

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Speaker 1: I think especially you know, I don't know what it's

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Speaker 1: like for everyone else. But I live in Brooklyn and

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Speaker 1: in New York City. It really felt like a lot

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Speaker 1: of the people who were out on the street, they

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Speaker 1: were out on the street because they were upset about

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Speaker 1: George Floyd. But I think it was more that they

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Speaker 1: recognized that George Floyd was the chip of the iceberg

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Speaker 1: and that there were just a lot going on that

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Speaker 1: they really just you could not tolerate any longer. More

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Speaker 1: from Susan after the break, and make sure to subscribe

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Speaker 1: to First Contact in Apple podcasts or wherever you listen

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Speaker 1: so you don't miss an episode putting on the tech hat.

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Speaker 1: You know, I think most people probably don't think of

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Speaker 1: tech when they think of the a c l U,

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Speaker 1: But there's quite a bit of litigation in regards to

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Speaker 1: security and privacy issues around contact tracing, surveillance, algorithmic bias,

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Speaker 1: and obviously the a c l U has a hand

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Speaker 1: in checks and balances and a lot of the issues

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Speaker 1: that are merging from the pandemic. You know, what are

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Speaker 1: some of the tech developments that you guys are most

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Speaker 1: concerned about. Well, since you were mentioning the COVID and

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Speaker 1: the the contact tracking and tracing, I'll start with that.

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Speaker 1: So the upshot is that we are neither for nor

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Speaker 1: against contact racing. Contact tracing is something that really will

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Speaker 1: contribute to public health. Our concern is not to say no,

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Speaker 1: you can't do it, or you just go right ahead

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Speaker 1: and do whatever you want. What we're concerned about is

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Speaker 1: to minimize the damage to privacy, the damage to equity. Again, Uh,

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Speaker 1: there are a lot of concerns that we have. The

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Speaker 1: other thing that we're concerned about is discrimination. Again, because

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Speaker 1: there are ways in which the technology could also increase

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Speaker 1: pre existing social inequities. We think that people should not

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Speaker 1: be coerced into participating and in testing. We think it

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Speaker 1: should be voluntary, and we also think that it should

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Speaker 1: be nonpunitive, because if you start having the criminal justice

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Speaker 1: system enforcing whether or not people are willing to use

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Speaker 1: their phone to take a test or whatever it is,

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Speaker 1: you're just creating more opportunities for police interactions that will

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Speaker 1: at some point be arbitrary or discriminatory. So we don't

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Speaker 1: want to see rules and regulations that are good to

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Speaker 1: public health rules. Even if they really are good public

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Speaker 1: health rules, we don't want to see those become occasions

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Speaker 1: for filling up the jails with the people who aren't complying,

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Speaker 1: because we've already seen there were some statistics in New

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Speaker 1: York that when you ask the police to start enforcing

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Speaker 1: who's wearing a mask and who's not wearing a mask,

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Speaker 1: that right away, excuse excuse, racially truly disproportionate in terms

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Speaker 1: of who they were questioning and who they weren't questioning.

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Speaker 1: So I think there's just a lot of issues they

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Speaker 1: were which is very much prop your reality, because they're

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Speaker 1: very much ethical issues. Yeah, you know, UM one of

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Speaker 1: the one of the cases that I'm fascinated by. UM

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Speaker 1: and I you know, I honestly I felt like it

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Speaker 1: was just it was only a matter of time until

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Speaker 1: we saw this headline. And then we saw the headline,

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Speaker 1: you know, a man was arrested after an algorithm wrongfully

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Speaker 1: identified him. You know, I've been covering for so many years.

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Speaker 1: AI is biased. AI is trained on you know, on

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Speaker 1: data online, which can be very racist, you know, And

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Speaker 1: I think for so many years we've been having this conversation.

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Speaker 1: But the question of okay, well, what happens when it

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Speaker 1: gets into the hands of the police, what happens, you know,

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Speaker 1: if if it could go for policing, And so I

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Speaker 1: think it's such a fascinating case. And and you guys

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Speaker 1: that a cl you filed an administrative complaint with Detroit's

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Speaker 1: police department over what you guys are calling the country's

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Speaker 1: first known wrongful arrest involving facial recognition technology. I mean,

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Speaker 1: for context, a man was arrested because he was wrongfully

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Speaker 1: identified by an algorithm. The police department thought he had

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Speaker 1: robbed I believe, like stolen watches, and he was arrested.

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Speaker 1: I mean, can you talk to me about the significance

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Speaker 1: of this case. I can't help put put on my

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Speaker 1: tech hat and scream. You guys, this is a really

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Speaker 1: big deal. Yeah, it is a really big deal. And

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Speaker 1: as you're saying, Laurie, we were aware of this problem

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Speaker 1: for a long time and we've been complaining. So going

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Speaker 1: back for a minute before getting to the case you're

433
00:22:32,920 --> 00:22:37,600
Speaker 1: talking about, Robert Williams UH the National Institute of Science

434
00:22:37,600 --> 00:22:40,679
Speaker 1: and Technology says that African American and Asian people are

435
00:22:40,760 --> 00:22:43,679
Speaker 1: up to a hundred times is likely to be disidentified

436
00:22:43,800 --> 00:22:47,359
Speaker 1: by official recognition. So yeah, that's the background problem. And

437
00:22:47,400 --> 00:22:49,560
Speaker 1: so we knew that, right, you know, we knew that

438
00:22:49,640 --> 00:22:53,120
Speaker 1: before the case came up in Michigan. Um, and it's

439
00:22:53,119 --> 00:22:55,800
Speaker 1: not the algorithm's fault. Obviously, there's something that's being put

440
00:22:55,800 --> 00:22:58,600
Speaker 1: into the algorithm that that is, you know, that has

441
00:22:58,640 --> 00:23:01,840
Speaker 1: a bias. And I people tend to think that algorithms

442
00:23:01,840 --> 00:23:03,600
Speaker 1: are you know, are so neutral and that we can

443
00:23:03,680 --> 00:23:05,960
Speaker 1: rely on algorithms. That's what I was saying about the

444
00:23:06,000 --> 00:23:09,439
Speaker 1: contact tracking and tracing, that you you start relying on

445
00:23:09,480 --> 00:23:11,840
Speaker 1: algorithms or apps that you think are neutral, and you

446
00:23:11,880 --> 00:23:15,399
Speaker 1: really have to be very wary of that. So again,

447
00:23:15,440 --> 00:23:18,520
Speaker 1: before getting to the Robert Williams case, uh An, a

448
00:23:18,600 --> 00:23:21,520
Speaker 1: c l U staffer at the ah LU of Northern California,

449
00:23:21,720 --> 00:23:24,919
Speaker 1: had the really interesting idea of trying out Amazon's facial

450
00:23:24,960 --> 00:23:29,400
Speaker 1: recognition program Recognition with the K because yeah, they were

451
00:23:29,400 --> 00:23:31,800
Speaker 1: just offering this to the police or whatever. This is great,

452
00:23:31,800 --> 00:23:33,919
Speaker 1: it will help you identify and see if you have

453
00:23:34,040 --> 00:23:36,880
Speaker 1: somebody who matches a mug shot. Well, what they tried

454
00:23:36,920 --> 00:23:39,040
Speaker 1: to do, which I thought was very clever, was they

455
00:23:39,160 --> 00:23:42,600
Speaker 1: tried to match mug shots against the members of Congress.

456
00:23:43,080 --> 00:23:46,080
Speaker 1: They got the facial pictures of all the members of Congress.

457
00:23:46,400 --> 00:23:51,240
Speaker 1: This was in July and there were twenty eight members

458
00:23:51,240 --> 00:23:54,280
Speaker 1: of Congress who were misidentified as matching the mug shots.

459
00:23:54,920 --> 00:23:57,560
Speaker 1: There were twenty in mistakes out of that, and not

460
00:23:57,640 --> 00:24:00,640
Speaker 1: only that, but that the false matches were disc apportionately

461
00:24:00,720 --> 00:24:03,159
Speaker 1: people of color. And one of the people who was

462
00:24:03,240 --> 00:24:06,000
Speaker 1: identified as matching a mug shot, and therefore, you know,

463
00:24:06,040 --> 00:24:09,679
Speaker 1: probably you know this criminal was civil rights legend John Lewis.

464
00:24:10,119 --> 00:24:11,760
Speaker 1: You're the guy who was beat up on the bridge

465
00:24:11,800 --> 00:24:14,840
Speaker 1: in Salma to know, to get us all voting rights.

466
00:24:14,880 --> 00:24:19,240
Speaker 1: So yeah, we know that almost of the false matches

467
00:24:19,280 --> 00:24:22,879
Speaker 1: there were of people of color, even though people of

468
00:24:22,880 --> 00:24:26,600
Speaker 1: color made up only twenty of the members of Congress.

469
00:24:26,640 --> 00:24:28,639
Speaker 1: So in some ways, you know, the Robert Williams case

470
00:24:28,760 --> 00:24:32,399
Speaker 1: is completely predictable. We knew that we allowed for that

471
00:24:32,480 --> 00:24:35,359
Speaker 1: to happen. It might have already happened elsewhere, but you know,

472
00:24:35,400 --> 00:24:37,600
Speaker 1: subterranean lee in a way that we don't really we

473
00:24:37,640 --> 00:24:40,399
Speaker 1: didn't see the case. But what's amazing about the Robert

474
00:24:40,400 --> 00:24:43,080
Speaker 1: Williams cases that it happened right there, you know, visible

475
00:24:43,119 --> 00:24:45,679
Speaker 1: to everybody where you can just see it. So what

476
00:24:45,760 --> 00:24:47,960
Speaker 1: happened was that they told him that he was being

477
00:24:48,040 --> 00:24:51,600
Speaker 1: arrested because they believed that he was that the algorithm

478
00:24:51,600 --> 00:24:54,959
Speaker 1: has said that that that he was a match for

479
00:24:55,000 --> 00:24:57,280
Speaker 1: this mug shot, and they showed him in the mug shot,

480
00:24:57,600 --> 00:24:59,560
Speaker 1: and he said to them, do you guys think all

481
00:24:59,600 --> 00:25:03,199
Speaker 1: black people look alike? That looks nothing like me. So,

482
00:25:03,280 --> 00:25:05,000
Speaker 1: you know, it was pretty clearing that if you used

483
00:25:05,000 --> 00:25:07,600
Speaker 1: your eyes and looked at the picture yourself, if you

484
00:25:07,640 --> 00:25:10,080
Speaker 1: didn't trust the algorithm, and if you looked at the

485
00:25:10,080 --> 00:25:13,919
Speaker 1: picture in this man's face, they didn't look alike. But nevertheless,

486
00:25:13,960 --> 00:25:16,920
Speaker 1: he spent thirty hours in jail under some pretty miserable

487
00:25:16,920 --> 00:25:20,399
Speaker 1: conditions because the algorithm said it was a match. So

488
00:25:20,440 --> 00:25:22,760
Speaker 1: I think that's really important. In some ways, the fact

489
00:25:22,840 --> 00:25:27,480
Speaker 1: that you know a problem exists is not as inspiring

490
00:25:27,720 --> 00:25:30,240
Speaker 1: to make people want to do something about it as

491
00:25:30,240 --> 00:25:32,879
Speaker 1: when you see it. So that's what happened with all

492
00:25:32,920 --> 00:25:35,560
Speaker 1: the protests about George Floyd. You people could watch that

493
00:25:35,640 --> 00:25:38,800
Speaker 1: horrible video. They could see it. It was recorded on

494
00:25:38,800 --> 00:25:42,040
Speaker 1: the video, And here we have an actual person, not

495
00:25:42,160 --> 00:25:46,040
Speaker 1: just hypothetically statistics are showing, but an actual person who

496
00:25:46,080 --> 00:25:48,720
Speaker 1: did get arrested and did have a miserable time. He

497
00:25:48,760 --> 00:25:51,400
Speaker 1: was arrested in front of his family. It was really

498
00:25:51,440 --> 00:25:56,800
Speaker 1: traumatizing and based on again, the officers involved were trusting

499
00:25:56,840 --> 00:25:59,840
Speaker 1: the science more than they were trusting their their own eyes.

500
00:26:00,320 --> 00:26:04,160
Speaker 1: When anybody cook he didn't look like the picture right.

501
00:26:04,440 --> 00:26:06,480
Speaker 1: And you know, he wrote an offed in the Washington

502
00:26:06,520 --> 00:26:08,440
Speaker 1: Post and he he asked the question, He said, why

503
00:26:08,480 --> 00:26:10,800
Speaker 1: is law enforcement even allowed to use this technology when

504
00:26:10,800 --> 00:26:14,960
Speaker 1: it obviously doesn't work? So, I guess asking a legal

505
00:26:15,000 --> 00:26:19,040
Speaker 1: scholar the question. You know, police departments all around the

506
00:26:19,040 --> 00:26:22,960
Speaker 1: country are using different variations of facial recognition software. So

507
00:26:23,520 --> 00:26:27,200
Speaker 1: you know, what regulations should we see as we enter

508
00:26:27,320 --> 00:26:31,560
Speaker 1: this era of algorithmic discrimination. Yeah, that's a great question.

509
00:26:31,600 --> 00:26:33,800
Speaker 1: And again we've been urging, you know, long before Robert

510
00:26:33,840 --> 00:26:36,960
Speaker 1: Williams turned up, we've been urging police departments not to

511
00:26:37,040 --> 00:26:40,240
Speaker 1: rely on the facial recognition technology that it was just

512
00:26:40,320 --> 00:26:42,840
Speaker 1: it was not reliable enough to you know, to hold

513
00:26:42,840 --> 00:26:46,600
Speaker 1: people's face in the hands of the algorithms don't have hands,

514
00:26:46,840 --> 00:26:49,880
Speaker 1: but for people's face to be dependent on the spatial

515
00:26:49,920 --> 00:26:53,280
Speaker 1: recognition technology which was being touted. And again, it's great

516
00:26:53,280 --> 00:26:55,760
Speaker 1: if a company is doing something to make money, but

517
00:26:55,800 --> 00:26:58,679
Speaker 1: if wanting to make money is your only consideration, and

518
00:26:58,680 --> 00:27:02,240
Speaker 1: if you're not considering whether you are unleashing something that

519
00:27:02,359 --> 00:27:05,919
Speaker 1: is really going to be disruptive of people's lives unfairly,

520
00:27:06,680 --> 00:27:08,879
Speaker 1: either because it's just going to be wrong or because

521
00:27:08,880 --> 00:27:11,240
Speaker 1: it's going to be wrong in a racially skewed way.

522
00:27:11,880 --> 00:27:15,000
Speaker 1: I think that's just really a problem. So um, we've

523
00:27:15,000 --> 00:27:18,840
Speaker 1: been urging police departments not to buy and use the technology.

524
00:27:19,040 --> 00:27:21,439
Speaker 1: And I'm sure you know Amazon has withdrawn the facial

525
00:27:21,480 --> 00:27:24,840
Speaker 1: recognition technology temporarily and they're not sure whether or not

526
00:27:24,880 --> 00:27:28,680
Speaker 1: they'll bring it back. So the probability of a wrongful

527
00:27:28,760 --> 00:27:31,600
Speaker 1: arrest is one thing, but when you draw the camera

528
00:27:31,680 --> 00:27:34,639
Speaker 1: back and look at all the technology in the bigger picture.

529
00:27:35,080 --> 00:27:38,440
Speaker 1: In addition to facial recognition, one thing that police departments

530
00:27:38,440 --> 00:27:42,160
Speaker 1: have been doing with facial recognition and different law enforcement

531
00:27:42,200 --> 00:27:45,760
Speaker 1: agencies is to try to see who attends a demonstration

532
00:27:45,960 --> 00:27:49,560
Speaker 1: or see who's in the crowd. So it ties not

533
00:27:49,680 --> 00:27:52,440
Speaker 1: into are you, like, is somebody likely to be wrongly

534
00:27:52,520 --> 00:27:55,520
Speaker 1: arrested like Robert Williams because they just there was a

535
00:27:55,560 --> 00:28:00,199
Speaker 1: false match. But it starts becoming big surveillance too that

536
00:28:00,480 --> 00:28:03,399
Speaker 1: an agency has the cameras on and then they have

537
00:28:03,480 --> 00:28:07,919
Speaker 1: the facial recognition and they're purporting to identify all the

538
00:28:07,960 --> 00:28:11,119
Speaker 1: people in that crowd, so that then they contract those people.

539
00:28:11,160 --> 00:28:13,880
Speaker 1: They now know that you were at the George Floyd demonstration,

540
00:28:14,200 --> 00:28:18,440
Speaker 1: and that person was in the the the anti war demonstration,

541
00:28:18,960 --> 00:28:21,199
Speaker 1: And at that point, the government starts having more and

542
00:28:21,280 --> 00:28:24,440
Speaker 1: more information about all of us, to the point where

543
00:28:24,480 --> 00:28:27,720
Speaker 1: it feels like instead of we're controlling the government, it's

544
00:28:27,760 --> 00:28:30,840
Speaker 1: like the government controls us. So I think the facial

545
00:28:30,920 --> 00:28:37,399
Speaker 1: recognition is only one part of the whole tendency of technology.

546
00:28:37,600 --> 00:28:43,680
Speaker 1: Two amplify government power to be kind of watching, watching

547
00:28:43,680 --> 00:28:47,240
Speaker 1: what we do. Yeah, I mean, it's it's interesting to

548
00:28:47,280 --> 00:28:50,800
Speaker 1: hear you say that. Um. You know, that type of

549
00:28:51,120 --> 00:28:53,280
Speaker 1: technology is just a part of it, especially when it

550
00:28:53,280 --> 00:28:56,360
Speaker 1: comes to this moment where people are out protesting police brutality,

551
00:28:56,400 --> 00:28:59,400
Speaker 1: when people are out fighting for their civil liberties. You know,

552
00:28:59,560 --> 00:29:04,240
Speaker 1: there's all sorts of technology that's being built. Their cameras

553
00:29:04,280 --> 00:29:07,200
Speaker 1: that are are being built that can recognize people in

554
00:29:07,240 --> 00:29:09,640
Speaker 1: real time that are police, police are wearing. There's all

555
00:29:09,720 --> 00:29:13,120
Speaker 1: sorts of technology. This is just the beginning of it. Um.

556
00:29:13,520 --> 00:29:15,480
Speaker 1: I I know you mentioned Amazon put a hold on

557
00:29:15,520 --> 00:29:18,240
Speaker 1: their sales of recognition software. Microsoft said it's not going

558
00:29:18,280 --> 00:29:22,320
Speaker 1: to sell face recognition software to police departments until their

559
00:29:22,360 --> 00:29:25,120
Speaker 1: federal regulations. I know. IBM said that it was going

560
00:29:25,120 --> 00:29:28,920
Speaker 1: to announce a ban on general purpose facial recognition? Is

561
00:29:29,000 --> 00:29:32,440
Speaker 1: that enough? Like? What is I guess, you know, what

562
00:29:32,680 --> 00:29:34,960
Speaker 1: is the government's role here? Like? What do you think

563
00:29:34,960 --> 00:29:38,200
Speaker 1: should happen? Especially since this is just, as you say,

564
00:29:38,440 --> 00:29:41,440
Speaker 1: one small part of a larger issue that we're facing

565
00:29:41,440 --> 00:29:43,960
Speaker 1: as a society. I think that's right, and I think

566
00:29:44,000 --> 00:29:46,360
Speaker 1: that there could be, you know, government regulation, but that's

567
00:29:46,360 --> 00:29:49,320
Speaker 1: not going to happen unless the public wants to urge

568
00:29:49,360 --> 00:29:52,720
Speaker 1: their representatives to start controlling this. And what we've seen

569
00:29:52,840 --> 00:29:56,200
Speaker 1: is that an enlightened public can make something happen even

570
00:29:56,240 --> 00:29:59,600
Speaker 1: without regulation, right, So, you know, it was that the

571
00:29:59,600 --> 00:30:02,520
Speaker 1: public is becoming concerned and that's the reason why Amazon

572
00:30:02,640 --> 00:30:06,280
Speaker 1: acted to withdraw this. They started being concerned that their

573
00:30:06,320 --> 00:30:09,320
Speaker 1: customers were not going to be happy with them. And

574
00:30:09,560 --> 00:30:12,800
Speaker 1: I think at this point that's almost more effective than

575
00:30:12,880 --> 00:30:16,480
Speaker 1: government regulation. And once you have that wake up call,

576
00:30:16,920 --> 00:30:19,600
Speaker 1: then you can start having serious debates. And I think

577
00:30:19,600 --> 00:30:22,440
Speaker 1: those debates have to take place in many places. They

578
00:30:22,440 --> 00:30:25,520
Speaker 1: should be taking place in legislatures where people can talk

579
00:30:25,560 --> 00:30:28,920
Speaker 1: about the trade off between privacy and mass surveillance and

580
00:30:28,960 --> 00:30:32,520
Speaker 1: whatever the government is trying to accomplish, why do they

581
00:30:32,560 --> 00:30:34,840
Speaker 1: need this technology? Is it really worth it? You are

582
00:30:34,880 --> 00:30:37,800
Speaker 1: their crimes that they wouldn't be solving without it, and

583
00:30:37,840 --> 00:30:41,240
Speaker 1: are they crimes that we're concerned about solving or do

584
00:30:41,320 --> 00:30:44,560
Speaker 1: they fall into the category of, you know, is that

585
00:30:44,680 --> 00:30:46,640
Speaker 1: something that we don't think should be a crime at all.

586
00:30:46,920 --> 00:30:49,200
Speaker 1: People are generally unaware in terms of what the police

587
00:30:49,200 --> 00:30:52,280
Speaker 1: do that only four to five percent of all arrests

588
00:30:52,280 --> 00:30:55,840
Speaker 1: involved crimes of violence. So when people think about we

589
00:30:55,880 --> 00:30:59,200
Speaker 1: want to enable law enforcement to be at catching criminals,

590
00:30:59,400 --> 00:31:02,720
Speaker 1: where we're concerned about divesting or defunding the police because

591
00:31:02,720 --> 00:31:06,000
Speaker 1: who's going to protect us from physical harm? Almost none

592
00:31:06,120 --> 00:31:08,160
Speaker 1: of what the police and law enforcement do is about

593
00:31:08,160 --> 00:31:12,040
Speaker 1: physical harm. It's a tiny percentage. Everything else that they're

594
00:31:12,040 --> 00:31:14,840
Speaker 1: doing is about this whole array of all sorts of

595
00:31:14,840 --> 00:31:17,600
Speaker 1: other things that we criminalize. And I think that in

596
00:31:17,640 --> 00:31:22,360
Speaker 1: addition to having better conversations about is there a potential

597
00:31:22,480 --> 00:31:25,160
Speaker 1: for some of these technologies that the government is using

598
00:31:25,880 --> 00:31:30,200
Speaker 1: to create arbitrary or discriminatory enforcement, I think we need

599
00:31:30,240 --> 00:31:32,920
Speaker 1: to dig deeper behind that question, in the same way

600
00:31:32,960 --> 00:31:35,440
Speaker 1: that you need to dig deeper beyond the George Floyd

601
00:31:35,760 --> 00:31:39,040
Speaker 1: murder and to ask if there's something systemically wrong here.

602
00:31:39,040 --> 00:31:41,560
Speaker 1: Do you need to rethink the whole question. So when

603
00:31:41,600 --> 00:31:43,440
Speaker 1: people say, well, you know, but we need the facial

604
00:31:43,480 --> 00:31:47,120
Speaker 1: recognition technology because it helps the police solve crimes, well, okay,

605
00:31:47,160 --> 00:31:49,640
Speaker 1: but you know what crimes and what are the costs?

606
00:31:50,320 --> 00:31:52,760
Speaker 1: So I think once people are educated enough and once

607
00:31:52,760 --> 00:31:56,360
Speaker 1: they realize what the nature of the problem is kind

608
00:31:56,360 --> 00:31:59,920
Speaker 1: of what's being unleashed, they can start really being read

609
00:32:00,160 --> 00:32:02,560
Speaker 1: to have that broader conversation. And I think it should

610
00:32:02,560 --> 00:32:04,960
Speaker 1: take place in legislatures, but I think it also should

611
00:32:04,960 --> 00:32:10,360
Speaker 1: take place and evidently is taking place in boardrooms at Amazon, Facebook,

612
00:32:10,400 --> 00:32:13,600
Speaker 1: and Google and Microsoft. They should be talking and they

613
00:32:13,640 --> 00:32:16,640
Speaker 1: do sometimes if the people demand it. And it also

614
00:32:16,720 --> 00:32:19,400
Speaker 1: has to take part just among people, you know, among

615
00:32:19,520 --> 00:32:22,760
Speaker 1: you know, tech communities and people just beginning to talk

616
00:32:22,760 --> 00:32:25,600
Speaker 1: about what are our responsibilities here? Is it okay for

617
00:32:25,720 --> 00:32:29,360
Speaker 1: us to create products just to make money if we

618
00:32:29,440 --> 00:32:31,719
Speaker 1: know that there are dangers, that the products are going

619
00:32:31,720 --> 00:32:34,920
Speaker 1: to be misused, or maybe aren't reliable enough, or that

620
00:32:35,000 --> 00:32:38,960
Speaker 1: they just feed into this enormous surveillance date. So let

621
00:32:38,960 --> 00:32:41,640
Speaker 1: me compare this to an earlier moment. After nine eleven,

622
00:32:42,360 --> 00:32:44,560
Speaker 1: we had a kind of similar phenomenon that in order

623
00:32:44,600 --> 00:32:48,360
Speaker 1: to deal with catching terrorists. We changed a lot of

624
00:32:48,440 --> 00:32:51,960
Speaker 1: laws that ended up really sacrificing a lot of privacy

625
00:32:51,960 --> 00:32:55,080
Speaker 1: and allowing a lot more government surveillance. And for a

626
00:32:55,160 --> 00:32:58,240
Speaker 1: number of years that went unchallenged, and people kept saying, oh, well,

627
00:32:58,280 --> 00:33:00,000
Speaker 1: you know, if if that's what we need in order

628
00:33:00,000 --> 00:33:04,120
Speaker 1: to be safe, we're willing to give up a little privacy. So,

629
00:33:04,240 --> 00:33:06,240
Speaker 1: first of all, I think people didn't think about the

630
00:33:06,280 --> 00:33:08,440
Speaker 1: fact that they weren't giving up their own privacy, they

631
00:33:08,440 --> 00:33:11,280
Speaker 1: were giving up somebody else's. And second of all, people

632
00:33:11,320 --> 00:33:15,320
Speaker 1: didn't realize how extensive the surveillance really was until Edward Snowden.

633
00:33:16,080 --> 00:33:19,240
Speaker 1: So then after Edward Snowden came along and people realized

634
00:33:19,280 --> 00:33:22,120
Speaker 1: how the government was just scooping up tons of information

635
00:33:22,160 --> 00:33:24,880
Speaker 1: about people and just keeping it in government databases and

636
00:33:24,920 --> 00:33:30,160
Speaker 1: started realizing the horrifying potential of all that. What happened

637
00:33:30,200 --> 00:33:32,760
Speaker 1: was that Congress made a couple of little changes to

638
00:33:32,800 --> 00:33:36,080
Speaker 1: the law. But more important, Microsoft and Google and other

639
00:33:36,080 --> 00:33:39,880
Speaker 1: places started to realize that their customers were concerned, and

640
00:33:39,920 --> 00:33:42,560
Speaker 1: they started being a little less cooperative. At the beginning,

641
00:33:42,640 --> 00:33:45,520
Speaker 1: right after nine eleven, all of the telecoms, all these

642
00:33:45,560 --> 00:33:48,120
Speaker 1: companies were just saying to the government, you want information here.

643
00:33:48,200 --> 00:33:50,440
Speaker 1: Take it all your verizons are sure, you know, hear

644
00:33:50,480 --> 00:33:52,360
Speaker 1: all the records of all our customers. Take it all.

645
00:33:52,400 --> 00:33:56,600
Speaker 1: You're keeping us safe. And I think that to me,

646
00:33:56,760 --> 00:33:59,719
Speaker 1: the most important thing is an informed public. That if

647
00:33:59,760 --> 00:34:02,680
Speaker 1: people can examine for themselves, but that they really think

648
00:34:02,720 --> 00:34:05,400
Speaker 1: that we're being kept safe by all of this, and

649
00:34:05,480 --> 00:34:08,440
Speaker 1: really examine you both the costs and the benefits in

650
00:34:08,480 --> 00:34:11,719
Speaker 1: an educated way, I think we get much better discussions.

651
00:34:11,760 --> 00:34:14,279
Speaker 1: And I think not only do you have the possibility

652
00:34:14,280 --> 00:34:17,360
Speaker 1: of getting better legislation or regulation, you also have the

653
00:34:17,400 --> 00:34:20,960
Speaker 1: possibility that private companies and you know, the tech the

654
00:34:21,000 --> 00:34:22,759
Speaker 1: tech companies are not going to want to do it

655
00:34:22,800 --> 00:34:25,960
Speaker 1: anymore because their customers don't want them to. Yeah. I

656
00:34:26,000 --> 00:34:28,680
Speaker 1: mean it's hard to have an informed public and to

657
00:34:28,800 --> 00:34:31,960
Speaker 1: have these discussions, even in this current environment. To some degree.

658
00:34:32,040 --> 00:34:34,960
Speaker 1: I mean, people I think are struggling with the idea

659
00:34:35,000 --> 00:34:38,160
Speaker 1: of truth. People are um, you know. And I remember,

660
00:34:38,239 --> 00:34:40,160
Speaker 1: by the way, I remember this note in leaks, like

661
00:34:40,160 --> 00:34:43,000
Speaker 1: I remember being in the news room covering technology and

662
00:34:43,040 --> 00:34:46,160
Speaker 1: thinking to myself because I wrote the tech bubble all

663
00:34:46,200 --> 00:34:49,840
Speaker 1: the way up right, and thinking, this is an extraordinary moment,

664
00:34:49,920 --> 00:34:52,960
Speaker 1: because we saw that we've been sharing all our data.

665
00:34:53,040 --> 00:34:55,319
Speaker 1: But we saw for the first time that, you know,

666
00:34:55,360 --> 00:34:58,080
Speaker 1: the government had a lot of access to things that

667
00:34:58,160 --> 00:35:01,040
Speaker 1: we had no idea they had access to. And I

668
00:35:01,040 --> 00:35:04,000
Speaker 1: think it was a fundamental shift, and the lens on

669
00:35:04,040 --> 00:35:08,240
Speaker 1: tech companies changed at that moment, and tech companies behavior

670
00:35:08,280 --> 00:35:11,200
Speaker 1: has changed quite a bit after that. You know, I

671
00:35:11,280 --> 00:35:13,480
Speaker 1: wonder this moment we're sitting in where we're having these

672
00:35:13,480 --> 00:35:17,400
Speaker 1: debates about surveillance and privacy and whatnot. These are sticky

673
00:35:17,440 --> 00:35:20,600
Speaker 1: debates and they're very politicized as we're heading into an election,

674
00:35:20,880 --> 00:35:24,360
Speaker 1: as we have misinformation spreading online, as a lot of

675
00:35:24,360 --> 00:35:26,600
Speaker 1: people don't know what to believe and what not to believe.

676
00:35:26,640 --> 00:35:30,360
Speaker 1: As the media landscape has changed, it's it certainly seems

677
00:35:30,400 --> 00:35:33,480
Speaker 1: like a harder environment to even to even have some

678
00:35:33,560 --> 00:35:36,440
Speaker 1: of these conversations. Well, I think in some ways it's

679
00:35:36,480 --> 00:35:38,440
Speaker 1: harder in some ways. I think the other thing that

680
00:35:38,560 --> 00:35:41,319
Speaker 1: is a catalyst for the discussions is realizing that there's

681
00:35:41,320 --> 00:35:43,879
Speaker 1: a dimension of race to all of us, I think,

682
00:35:43,880 --> 00:35:47,719
Speaker 1: and talking about artificial intelligence and facial recognition, not many

683
00:35:47,760 --> 00:35:50,920
Speaker 1: people saw that as an issue of structural racism. You know,

684
00:35:50,960 --> 00:35:53,160
Speaker 1: that there's something wrong with how we're putting together the

685
00:35:53,160 --> 00:35:55,480
Speaker 1: algorithms and it ends up that John Lewis is going

686
00:35:55,520 --> 00:35:58,520
Speaker 1: to be misidentified as somebody who matches a mug shot

687
00:35:58,560 --> 00:36:01,120
Speaker 1: and that Robert Williams is going to be arrested. So

688
00:36:01,200 --> 00:36:04,000
Speaker 1: I think that the fact that we now know that

689
00:36:04,000 --> 00:36:08,520
Speaker 1: that is an additional concern enables us to have richer conversations.

690
00:36:08,920 --> 00:36:11,000
Speaker 1: So we're not only talking about is there a trade

691
00:36:11,040 --> 00:36:14,600
Speaker 1: off between security and privacy? Plus, I think the other

692
00:36:14,680 --> 00:36:16,960
Speaker 1: thing that people are feeling much more open to is

693
00:36:17,000 --> 00:36:21,000
Speaker 1: to have that deeper conversation about what are our goals

694
00:36:21,000 --> 00:36:24,600
Speaker 1: here and if we're enabling all this government surveillance in

695
00:36:24,680 --> 00:36:27,759
Speaker 1: order to help the government to catch criminals, well, you

696
00:36:27,800 --> 00:36:30,120
Speaker 1: know what do we mean by criminals? What crimes are

697
00:36:30,120 --> 00:36:32,279
Speaker 1: they solving? And how are they using you know, how

698
00:36:32,320 --> 00:36:34,680
Speaker 1: how are how is this actually being used in services?

699
00:36:34,719 --> 00:36:36,880
Speaker 1: Wood So I feel like in some ways, you know,

700
00:36:37,239 --> 00:36:39,520
Speaker 1: with the election coming up, I think that gives people

701
00:36:40,600 --> 00:36:43,319
Speaker 1: more impetus to want to talk about these issues, because

702
00:36:43,320 --> 00:36:46,320
Speaker 1: the elections aren't only about the president. They're also about

703
00:36:46,520 --> 00:36:49,360
Speaker 1: local prosecutors and sheriffs and the people who make the

704
00:36:49,400 --> 00:36:53,759
Speaker 1: decisions about whether to buy surveillance equipment and what they're

705
00:36:53,760 --> 00:36:56,840
Speaker 1: gonna do with their authority over the criminal justice system.

706
00:36:57,320 --> 00:36:58,920
Speaker 1: So one thing the a c l U has been

707
00:36:58,960 --> 00:37:01,560
Speaker 1: doing in addition to everything else, as we've been very

708
00:37:01,640 --> 00:37:05,520
Speaker 1: involved in elections of prosecutors because that's the place where

709
00:37:05,520 --> 00:37:08,200
Speaker 1: almost people never used to pay attention to, you know who,

710
00:37:08,239 --> 00:37:11,000
Speaker 1: who were these people running and maybe they would vote

711
00:37:11,000 --> 00:37:13,759
Speaker 1: for somebody without really knowing what they voted for. So

712
00:37:13,800 --> 00:37:15,960
Speaker 1: what we're urging, and I think this is very much

713
00:37:16,000 --> 00:37:19,360
Speaker 1: what we're talking about about having an educated public. We're

714
00:37:19,440 --> 00:37:23,080
Speaker 1: urging people to go to elections or to go to debates,

715
00:37:23,320 --> 00:37:26,200
Speaker 1: to go to campaign events, attend I guess on zoom

716
00:37:26,239 --> 00:37:30,400
Speaker 1: these days, to attend campaign events and ask the candidates questions,

717
00:37:30,719 --> 00:37:32,839
Speaker 1: what would be your policy about whether or not you're

718
00:37:32,840 --> 00:37:35,920
Speaker 1: going to accept military equipment from the federal government in

719
00:37:35,920 --> 00:37:38,880
Speaker 1: your police department? Are you going to buy tanks? Are

720
00:37:38,880 --> 00:37:41,120
Speaker 1: you going to buy you know, these horrible weapons that

721
00:37:41,520 --> 00:37:44,279
Speaker 1: are used. Is that something you would do? Are you

722
00:37:44,320 --> 00:37:46,840
Speaker 1: going to buy you know, facial recognition software? Is that

723
00:37:46,880 --> 00:37:48,799
Speaker 1: how you would use your power? If we left you

724
00:37:49,480 --> 00:37:53,640
Speaker 1: um say that the prosecutors, would you support a reduction

725
00:37:53,680 --> 00:37:57,920
Speaker 1: in cash bail and increase of increased alternatives to incarceration?

726
00:37:58,400 --> 00:38:01,000
Speaker 1: So that's a place where without way for the government

727
00:38:01,040 --> 00:38:04,920
Speaker 1: to do something, we can ourselves affect what's happening in

728
00:38:04,920 --> 00:38:10,200
Speaker 1: our communities by encouraging candidates to think about what positions

729
00:38:10,200 --> 00:38:12,400
Speaker 1: they're taking on these different issues and letting them know

730
00:38:12,520 --> 00:38:16,040
Speaker 1: that they're gonna lose votes. The more people educated, the

731
00:38:16,120 --> 00:38:18,560
Speaker 1: more they are educated, the more they can tell people

732
00:38:18,640 --> 00:38:21,040
Speaker 1: that they'll lose votes, and to try that. This is

733
00:38:21,080 --> 00:38:24,279
Speaker 1: something that's worked in some places to encourage candidates to

734
00:38:24,800 --> 00:38:28,040
Speaker 1: take a better position. Yeah. Yeah, they might never never

735
00:38:28,200 --> 00:38:30,160
Speaker 1: thought of that, but you know, once they commit themselves,

736
00:38:30,160 --> 00:38:32,040
Speaker 1: you know that that's going to be better. So there

737
00:38:32,040 --> 00:38:33,919
Speaker 1: are all sorts of ways that we can affect things.

738
00:38:37,440 --> 00:38:39,920
Speaker 1: More from Susan after the break and make sure you

739
00:38:39,960 --> 00:38:42,080
Speaker 1: set up for our newsletter at dot dot dot media

740
00:38:42,200 --> 00:39:00,880
Speaker 1: dot com. Backslash newsletter will be launching this summer. Before

741
00:39:00,880 --> 00:39:03,880
Speaker 1: I move on from specifically some of the tech issues,

742
00:39:04,120 --> 00:39:08,680
Speaker 1: I have to bring up predator drones right right, you know,

743
00:39:08,880 --> 00:39:11,480
Speaker 1: the the U S. Customs and Border Protection flew a

744
00:39:11,560 --> 00:39:14,960
Speaker 1: large predator drone over the Minneapolis protests. You know, people

745
00:39:15,000 --> 00:39:17,640
Speaker 1: were protesting police brutality and the killing of George Floyd,

746
00:39:18,080 --> 00:39:20,759
Speaker 1: and for many reasons, it almost felt symbolic. You know,

747
00:39:20,800 --> 00:39:25,520
Speaker 1: it was raising all these questions about aerial surveillance, about

748
00:39:25,760 --> 00:39:29,960
Speaker 1: what data was being collected, where was this going? What

749
00:39:30,160 --> 00:39:33,480
Speaker 1: is your take on this? Well, you know, as you're saying, Laurie,

750
00:39:33,520 --> 00:39:36,520
Speaker 1: and you know that really it really magnifies the opportunity

751
00:39:36,560 --> 00:39:38,960
Speaker 1: to gather more information because you don't even have to

752
00:39:38,960 --> 00:39:42,040
Speaker 1: have the helicopters or whatever. But so you know that

753
00:39:42,200 --> 00:39:44,560
Speaker 1: of course is a concern just you how much information

754
00:39:44,680 --> 00:39:46,560
Speaker 1: is the government gathering, what are they going to do

755
00:39:46,600 --> 00:39:48,480
Speaker 1: with it, who's going to have access to it? Will

756
00:39:48,480 --> 00:39:50,560
Speaker 1: would ever be deleted or will it just got to

757
00:39:50,640 --> 00:39:53,799
Speaker 1: stay there in the government databasis forever. But I think

758
00:39:53,840 --> 00:39:56,200
Speaker 1: the other thing that the Predator drone brings to mind

759
00:39:56,680 --> 00:39:58,840
Speaker 1: is a question that people were also asking, which is

760
00:39:58,880 --> 00:40:02,200
Speaker 1: about the new live touris aation of law enforcement we've

761
00:40:02,280 --> 00:40:05,279
Speaker 1: had for years in this country, a passi Comma tat

762
00:40:05,360 --> 00:40:07,799
Speaker 1: us Act as it's called, which says, you don't want

763
00:40:07,800 --> 00:40:12,600
Speaker 1: the military doing everyday law enforcement, because that's that's not

764
00:40:13,000 --> 00:40:15,640
Speaker 1: our country. We don't want the military to be quote

765
00:40:15,680 --> 00:40:19,400
Speaker 1: dominating the streets, and we don't want the people who

766
00:40:19,480 --> 00:40:21,960
Speaker 1: are out protesting to be considered the enemy of the

767
00:40:22,040 --> 00:40:25,160
Speaker 1: United States, there are people who are expressing their opinions,

768
00:40:25,760 --> 00:40:28,719
Speaker 1: and so the whole idea of you know, it's one thing.

769
00:40:29,120 --> 00:40:32,520
Speaker 1: It's enough if the police held helicopters are flying overhead

770
00:40:32,960 --> 00:40:34,719
Speaker 1: and trying to keep track of, you know, who's in

771
00:40:34,760 --> 00:40:37,200
Speaker 1: the crowd and what the crowd is doing. But once

772
00:40:37,239 --> 00:40:41,520
Speaker 1: you start adding an element of something the military helicopters

773
00:40:41,600 --> 00:40:45,480
Speaker 1: or the military drones or things that feel like we

774
00:40:45,560 --> 00:40:48,280
Speaker 1: are being treated as the enemy of the government, instaid

775
00:40:48,280 --> 00:40:50,760
Speaker 1: that the people who are the government, who are supposed

776
00:40:50,760 --> 00:40:53,920
Speaker 1: to be controlling the government, I think that that's just it.

777
00:40:54,080 --> 00:40:58,640
Speaker 1: It's a very bad paradigm. You think it's a slippery slope, Well,

778
00:40:58,680 --> 00:41:01,080
Speaker 1: it's a slippery slope unless we stopped the slipping. And

779
00:41:01,120 --> 00:41:04,279
Speaker 1: as we saw with you with Amazon and the facial recognition,

780
00:41:04,320 --> 00:41:06,600
Speaker 1: if people say, wait a minute, yeah, I think we

781
00:41:06,640 --> 00:41:08,879
Speaker 1: can make that stuff. But I think if people don't

782
00:41:08,880 --> 00:41:11,440
Speaker 1: pay attention, I think we have a very slippery slope.

783
00:41:11,840 --> 00:41:13,719
Speaker 1: And that's what I've been saying about most of the

784
00:41:13,719 --> 00:41:16,920
Speaker 1: issues we've talked about you, starting with the contact tracing

785
00:41:17,480 --> 00:41:20,040
Speaker 1: and the surveillance and everything else. It seems to me

786
00:41:20,160 --> 00:41:23,359
Speaker 1: that what's really important is transparency. We should know what

787
00:41:23,400 --> 00:41:27,040
Speaker 1: the government is doing and accountability. Back on the issue

788
00:41:27,040 --> 00:41:29,279
Speaker 1: of contact tracing, one thing that the AHL you do,

789
00:41:29,480 --> 00:41:32,359
Speaker 1: together with the a l U of Massachusetts, is we

790
00:41:32,440 --> 00:41:36,320
Speaker 1: have filed a lawsuit. We're actually a records request demanding

791
00:41:36,440 --> 00:41:40,600
Speaker 1: that the government, including the CDC, release information about the

792
00:41:40,640 --> 00:41:43,319
Speaker 1: possible uses of all the location data that they would

793
00:41:43,320 --> 00:41:47,359
Speaker 1: be collecting in connection with contact tracing, because you know,

794
00:41:47,640 --> 00:41:49,680
Speaker 1: once if you don't know what they're doing, then you

795
00:41:49,719 --> 00:41:51,799
Speaker 1: can't have a discussion about what they should be doing.

796
00:41:52,239 --> 00:41:54,040
Speaker 1: And one reason why I was bringing up all the

797
00:41:54,040 --> 00:41:56,439
Speaker 1: post nine eleven changes of law is that I think

798
00:41:56,480 --> 00:42:00,560
Speaker 1: that the whole idea that we can't know the government

799
00:42:00,640 --> 00:42:02,719
Speaker 1: is doing. The government has to act in secret in

800
00:42:02,800 --> 00:42:04,960
Speaker 1: order to keep us safe or else the enemy will

801
00:42:05,000 --> 00:42:06,880
Speaker 1: be able to know what they're doing and you know,

802
00:42:07,000 --> 00:42:10,200
Speaker 1: and work around it. But the government can know everything

803
00:42:10,200 --> 00:42:13,360
Speaker 1: that we're doing. I think that just has democracy backwards.

804
00:42:14,000 --> 00:42:15,919
Speaker 1: You know, we have to be able to know what's

805
00:42:15,920 --> 00:42:18,839
Speaker 1: happening inside the government. And that applies to why are

806
00:42:18,840 --> 00:42:20,719
Speaker 1: they sending the Predator drone? What are they going to

807
00:42:20,760 --> 00:42:22,799
Speaker 1: do with the information? What does this mean? Are they

808
00:42:22,800 --> 00:42:25,160
Speaker 1: going to do it again? And it also has to

809
00:42:25,200 --> 00:42:28,400
Speaker 1: do with the contact track tracking and tracing. Once they

810
00:42:28,440 --> 00:42:31,040
Speaker 1: get that data, what happens to it? Are they going

811
00:42:31,120 --> 00:42:33,000
Speaker 1: to erase itever? You know, who do they share it with?

812
00:42:33,040 --> 00:42:35,200
Speaker 1: What are they going to do with it? And I feel,

813
00:42:35,239 --> 00:42:37,879
Speaker 1: you know, those are really important issues in a democracy,

814
00:42:37,920 --> 00:42:39,719
Speaker 1: that we just have the right to know what the

815
00:42:39,760 --> 00:42:42,080
Speaker 1: government is doing so that we can talk about it.

816
00:42:42,440 --> 00:42:44,359
Speaker 1: And I feel like to sort of say, well, this

817
00:42:44,440 --> 00:42:46,680
Speaker 1: is what the government is doing and that's really bad,

818
00:42:47,120 --> 00:42:49,800
Speaker 1: and that upsets me. I think that kind of misses

819
00:42:49,800 --> 00:42:52,239
Speaker 1: the point. If the government is doing something bad, then

820
00:42:52,320 --> 00:42:55,480
Speaker 1: it is the duty of every American to find out

821
00:42:55,480 --> 00:42:57,799
Speaker 1: what they're doing and to push back. And so at

822
00:42:57,840 --> 00:42:59,680
Speaker 1: the a C. O you we have a program that

823
00:42:59,680 --> 00:43:03,399
Speaker 1: we call People Power. We first invented that and used

824
00:43:03,400 --> 00:43:07,120
Speaker 1: it to explain to cities and localities all over the

825
00:43:07,160 --> 00:43:11,200
Speaker 1: country about how they could fight back against draconian immigration

826
00:43:11,320 --> 00:43:14,839
Speaker 1: rules by becoming quote sanctuary cities, what what their rights

827
00:43:14,880 --> 00:43:18,080
Speaker 1: actually were. We then used it for voting rights. We're

828
00:43:18,120 --> 00:43:20,319
Speaker 1: about to use it some more for voting rights. But

829
00:43:20,440 --> 00:43:22,279
Speaker 1: what we have really urged and I hope that you know,

830
00:43:22,360 --> 00:43:23,920
Speaker 1: some of your listeners will go to the a C

831
00:43:24,040 --> 00:43:26,239
Speaker 1: l U website and see about what people power is

832
00:43:26,280 --> 00:43:28,080
Speaker 1: doing in addition to what the a c l U

833
00:43:28,280 --> 00:43:30,600
Speaker 1: is doing, Because what is the A c L you doing?

834
00:43:30,640 --> 00:43:32,759
Speaker 1: And that's all the staffers at home trying to you know,

835
00:43:32,800 --> 00:43:34,799
Speaker 1: work on their new laptops while they're trying to, you know,

836
00:43:34,880 --> 00:43:38,080
Speaker 1: keep their talkers quiet. But people power is about what

837
00:43:38,160 --> 00:43:40,919
Speaker 1: every single person can and I think should be doing.

838
00:43:41,280 --> 00:43:43,840
Speaker 1: You know, if people really educate themselves and think about

839
00:43:44,200 --> 00:43:46,880
Speaker 1: the ethical issues, the costs and benefits of all this

840
00:43:47,040 --> 00:43:50,600
Speaker 1: technology in addition to a lot of other things going on,

841
00:43:50,680 --> 00:43:52,839
Speaker 1: I think we get a lot better results if people

842
00:43:52,880 --> 00:43:55,680
Speaker 1: pay attention. Yeah, I mean, it's interesting to watch the

843
00:43:55,719 --> 00:43:59,040
Speaker 1: A c L you take on issues like surveillance, facial recognition.

844
00:43:59,080 --> 00:44:00,600
Speaker 1: I know, the A C L you out a lawsuit

845
00:44:00,640 --> 00:44:04,440
Speaker 1: against clear View AI, which was this very controversial company

846
00:44:04,480 --> 00:44:08,160
Speaker 1: that was using biometric data. I think facial recognition technology

847
00:44:08,160 --> 00:44:10,640
Speaker 1: helped them collect something like three billion face prints, and

848
00:44:10,640 --> 00:44:15,320
Speaker 1: they were giving access to private companies, wealthy individuals, federal,

849
00:44:15,320 --> 00:44:18,520
Speaker 1: state and local law enforcement agencies. And you know, coming

850
00:44:18,560 --> 00:44:22,120
Speaker 1: from the tech space, it certainly feels like sometimes these stories,

851
00:44:22,200 --> 00:44:25,160
Speaker 1: you just don't know what these companies are doing until

852
00:44:25,200 --> 00:44:28,120
Speaker 1: you start, you know, peeling back the layers and seeing

853
00:44:28,160 --> 00:44:30,200
Speaker 1: all the data went to here and here, and why

854
00:44:30,239 --> 00:44:33,279
Speaker 1: did it go there? And why wasn't this disclosed? And

855
00:44:33,280 --> 00:44:38,160
Speaker 1: and oftentimes it takes the watchdog to really understand where

856
00:44:38,200 --> 00:44:40,879
Speaker 1: some of this can can go wrong and how it's

857
00:44:40,920 --> 00:44:44,160
Speaker 1: being used in ways in ways that can be dangerous

858
00:44:44,200 --> 00:44:46,960
Speaker 1: in many ways. Yeah, I think that's exactly right. And

859
00:44:46,960 --> 00:44:49,040
Speaker 1: that's why I was saying before that aren't concerned before

860
00:44:49,040 --> 00:44:51,800
Speaker 1: everybody jumps on the bandwagon about let's have more contact

861
00:44:51,840 --> 00:44:54,000
Speaker 1: tracing and then you know, like everybody should just be

862
00:44:54,040 --> 00:44:57,040
Speaker 1: doing all this information. I think we have to get

863
00:44:57,080 --> 00:45:00,479
Speaker 1: a dog. Yeah, you're not gonna have the watch dog

864
00:45:00,520 --> 00:45:03,160
Speaker 1: telling you things unless you build a watchdog into the system.

865
00:45:03,600 --> 00:45:05,600
Speaker 1: And if everything is just you know, a company has

866
00:45:05,640 --> 00:45:07,719
Speaker 1: invented this and is selling it to the police, or

867
00:45:07,719 --> 00:45:09,600
Speaker 1: a company who has invented this and now we're all

868
00:45:09,600 --> 00:45:11,680
Speaker 1: going to buy it. If you just leave out any

869
00:45:11,680 --> 00:45:15,840
Speaker 1: sort of oversight, then you really have a tremendous potential problem.

870
00:45:15,880 --> 00:45:17,880
Speaker 1: Are there any other examples of tech that we're not

871
00:45:17,960 --> 00:45:23,399
Speaker 1: thinking about the unintended consequences for our rights or privacy yet? Well,

872
00:45:23,440 --> 00:45:26,879
Speaker 1: you know, a AI is really big altogether across as

873
00:45:26,880 --> 00:45:30,040
Speaker 1: you're saying across many different kinds of issues. I was

874
00:45:30,080 --> 00:45:32,760
Speaker 1: just actually, this is not a tangential to your question,

875
00:45:32,800 --> 00:45:34,759
Speaker 1: but you were asking me before about cases that I

876
00:45:34,800 --> 00:45:36,560
Speaker 1: had worked on, and there was another case that I

877
00:45:36,600 --> 00:45:39,040
Speaker 1: worked on that was about tech where I wrote the

878
00:45:39,280 --> 00:45:41,920
Speaker 1: A c l Use brief in the Supreme Court. It

879
00:45:42,040 --> 00:45:44,759
Speaker 1: was an ancus brief. It wasn't about our client, but

880
00:45:44,880 --> 00:45:47,680
Speaker 1: it was a kid called Riley versus California. And what

881
00:45:47,800 --> 00:45:51,239
Speaker 1: the police were saying they're most law enforcement places, the

882
00:45:51,320 --> 00:45:53,880
Speaker 1: federal government as well as the state of California and

883
00:45:53,920 --> 00:45:57,680
Speaker 1: many other jurisdictions, was that when you arrest somebody, the

884
00:45:57,719 --> 00:45:59,880
Speaker 1: police get to do what is called a search incident

885
00:46:00,000 --> 00:46:01,920
Speaker 1: to arrest, so they get to see what you have

886
00:46:01,960 --> 00:46:03,839
Speaker 1: in your pocket. Makes some sense, right, you know, if

887
00:46:03,840 --> 00:46:05,480
Speaker 1: you have a gun in your pocket, that's a problem,

888
00:46:05,600 --> 00:46:07,239
Speaker 1: or you know whatever, So they get to do a

889
00:46:07,239 --> 00:46:09,879
Speaker 1: surgeon sent in to arrest. And the law had been

890
00:46:10,200 --> 00:46:12,600
Speaker 1: that if they find something in your pocket like that's

891
00:46:12,680 --> 00:46:15,560
Speaker 1: that's a container, they can search inside the container to

892
00:46:15,560 --> 00:46:18,760
Speaker 1: see if there's anything in it that that could be harmful.

893
00:46:18,840 --> 00:46:21,360
Speaker 1: And in fact, there was one situation where they opened

894
00:46:21,400 --> 00:46:24,040
Speaker 1: up a cigarette package that somebody had and they you know,

895
00:46:24,080 --> 00:46:26,719
Speaker 1: they could find a razor blade, they could find marijuana,

896
00:46:26,800 --> 00:46:30,040
Speaker 1: cigarette whatever. So that was law where the Supreme Court said, yes,

897
00:46:30,040 --> 00:46:32,640
Speaker 1: you're allowed to search people and search the containers that

898
00:46:32,680 --> 00:46:35,480
Speaker 1: are on them. Well, what law enforcement said was, your

899
00:46:35,520 --> 00:46:38,600
Speaker 1: cell phone is a container. When we arrest you, we

900
00:46:38,640 --> 00:46:41,239
Speaker 1: can search your cell phone. It's a container. We have

901
00:46:41,280 --> 00:46:43,640
Speaker 1: the right to search incident to risk. And so we

902
00:46:43,719 --> 00:46:46,160
Speaker 1: wrote a brief saying no, it's not you know, it's

903
00:46:46,160 --> 00:46:49,040
Speaker 1: a container, but it's a container that essentially is your home,

904
00:46:49,440 --> 00:46:53,520
Speaker 1: it's your library, it's your desk. So allowing the police

905
00:46:53,560 --> 00:46:56,160
Speaker 1: to look in your cell phone when they only had

906
00:46:56,440 --> 00:47:00,560
Speaker 1: really very feeble and very unlikely scenarios, things just wouldn't

907
00:47:00,600 --> 00:47:02,600
Speaker 1: happen too often for what the need was. You know,

908
00:47:02,640 --> 00:47:04,560
Speaker 1: maybe you had some remote thing that would go off

909
00:47:04,560 --> 00:47:07,040
Speaker 1: and would blow something up, you know, oh come on, yeah,

910
00:47:07,080 --> 00:47:08,640
Speaker 1: but yeah, there were other ways to deal with a

911
00:47:08,680 --> 00:47:11,440
Speaker 1: lot of that, and so the Supreme Court actually agreed

912
00:47:11,520 --> 00:47:13,480
Speaker 1: with that. They said, yeah, you know, this is really

913
00:47:13,600 --> 00:47:17,400
Speaker 1: is just a technological way of finding out what's in

914
00:47:17,440 --> 00:47:20,680
Speaker 1: your your papers and books and records. It used to

915
00:47:20,680 --> 00:47:22,120
Speaker 1: be they were in your desk, and now they're in

916
00:47:22,200 --> 00:47:24,839
Speaker 1: your cell phone. So that to me, it's a sort

917
00:47:24,880 --> 00:47:26,960
Speaker 1: of a whole thread of what we've been talking about.

918
00:47:27,000 --> 00:47:30,400
Speaker 1: But the challenges to civil liberties are different and in

919
00:47:30,440 --> 00:47:35,280
Speaker 1: some ways greater when the technology builds up. Yeah, there's

920
00:47:35,400 --> 00:47:37,600
Speaker 1: there's a great quote on the A c. L U website.

921
00:47:37,640 --> 00:47:40,120
Speaker 1: The fact that technology now allows an individual to carry

922
00:47:40,160 --> 00:47:42,640
Speaker 1: such information in his hand does not make the information

923
00:47:42,719 --> 00:47:45,720
Speaker 1: any less worthy of the protection for which the founders fought.

924
00:47:46,080 --> 00:47:49,880
Speaker 1: The U. S. Supreme Court Chief Justice John Roberts exactly.

925
00:47:50,360 --> 00:47:52,239
Speaker 1: I like to talk about, you know, one of the

926
00:47:52,239 --> 00:47:55,239
Speaker 1: whole points of the Constitution adding the Fourth Amendment, which

927
00:47:55,280 --> 00:47:57,960
Speaker 1: is the protection of privacy, is they wanted to protect

928
00:47:57,960 --> 00:48:00,920
Speaker 1: what was in Benjamin Franklin's disk. You know, nobody should

929
00:48:00,920 --> 00:48:03,120
Speaker 1: know if he was writing some things that were anti government,

930
00:48:03,360 --> 00:48:05,319
Speaker 1: and we now have that on our cell phone, so

931
00:48:05,360 --> 00:48:07,759
Speaker 1: of course, But that's where I think that a lot

932
00:48:07,800 --> 00:48:12,080
Speaker 1: of the protection of civil liberties is applying our fundamental

933
00:48:12,120 --> 00:48:17,120
Speaker 1: principles in different circumstances. Taking a gigantic step back, what

934
00:48:17,200 --> 00:48:19,360
Speaker 1: do you think is the biggest threat to civil liberties

935
00:48:19,400 --> 00:48:22,839
Speaker 1: in the new World Order? In the New World Order, Well,

936
00:48:22,880 --> 00:48:24,640
Speaker 1: you know, it's hard to just select one. In sort

937
00:48:24,640 --> 00:48:26,560
Speaker 1: of like Sophie's choice, you know which, which is your

938
00:48:26,600 --> 00:48:30,319
Speaker 1: favorite child? Right now, I think one of our very

939
00:48:30,400 --> 00:48:33,200
Speaker 1: top priorities in Adenia. Mass incarceration is a big one

940
00:48:33,239 --> 00:48:36,319
Speaker 1: because so many people's lives are just being totally disrupted

941
00:48:36,320 --> 00:48:39,040
Speaker 1: their families, and often the question really has to be

942
00:48:39,120 --> 00:48:41,640
Speaker 1: for what. One thing that we're hoping is that the

943
00:48:41,680 --> 00:48:44,880
Speaker 1: work we've been doing around trying to get vulnerable people

944
00:48:45,040 --> 00:48:47,680
Speaker 1: released from prison so that they won't get the virus

945
00:48:47,719 --> 00:48:52,360
Speaker 1: and get seriously all possibly die is we're helping that

946
00:48:52,440 --> 00:48:56,280
Speaker 1: once jurisdictions see that they were able to release thousands

947
00:48:56,360 --> 00:48:59,360
Speaker 1: of people from prisons and jails and that it's not

948
00:48:59,400 --> 00:49:01,680
Speaker 1: going to cause a spike in the crime rate, it

949
00:49:01,680 --> 00:49:04,200
Speaker 1: really is pretty safe thing to do. We're hoping that

950
00:49:04,200 --> 00:49:07,360
Speaker 1: that's going to stick and that long run will be

951
00:49:07,400 --> 00:49:09,319
Speaker 1: able to rethink, well, did we really need to put

952
00:49:09,360 --> 00:49:11,399
Speaker 1: all those people in prison and jail to start with?

953
00:49:11,640 --> 00:49:13,920
Speaker 1: What are we doing with the criminal justice system? So

954
00:49:14,000 --> 00:49:16,160
Speaker 1: that's really big. But the other thing that I think

955
00:49:16,239 --> 00:49:19,120
Speaker 1: is really big right now is voting rights. I have

956
00:49:19,160 --> 00:49:22,239
Speaker 1: alluded to this at the beginning of our conversation, but

957
00:49:22,760 --> 00:49:25,400
Speaker 1: the premise of democracy is that the people get to

958
00:49:25,440 --> 00:49:27,839
Speaker 1: decide on who should be running the government and who

959
00:49:27,840 --> 00:49:30,239
Speaker 1: should be making the policy about all these things we're

960
00:49:30,239 --> 00:49:33,279
Speaker 1: talking about here. You know, what, what are the regulations

961
00:49:33,320 --> 00:49:36,960
Speaker 1: about technology? What are the regulations about your reproductive freedom?

962
00:49:37,520 --> 00:49:43,200
Speaker 1: Everything else? LGBT rights? Uh, And it's the people's vote

963
00:49:43,239 --> 00:49:46,000
Speaker 1: is distorted. That's a real problem that people can't vote.

964
00:49:46,320 --> 00:49:49,160
Speaker 1: So we have litigation going on right now in I

965
00:49:49,200 --> 00:49:53,160
Speaker 1: think it's like thirty different states trying to get people

966
00:49:53,200 --> 00:49:56,359
Speaker 1: the opportunity to vote. So one of the things that

967
00:49:57,160 --> 00:50:01,160
Speaker 1: has happened in addition to all way is that incumbents

968
00:50:01,160 --> 00:50:03,480
Speaker 1: had been using to try to protect their own seats,

969
00:50:04,160 --> 00:50:06,560
Speaker 1: is that the virus has really made it dangerous for

970
00:50:06,600 --> 00:50:10,000
Speaker 1: people to vote in public places. So we saw the

971
00:50:10,080 --> 00:50:13,480
Speaker 1: election in Wisconsin where people were just lined up for

972
00:50:13,640 --> 00:50:16,000
Speaker 1: you know, tremendous distance is waiting for a really long

973
00:50:16,040 --> 00:50:19,080
Speaker 1: time to vote because Wisconsin would not allow them to

974
00:50:19,200 --> 00:50:22,880
Speaker 1: submit absentee ballots. And in fact, a study showed afterwards

975
00:50:22,880 --> 00:50:25,760
Speaker 1: that at least seventeen people got got the virus from voting.

976
00:50:26,560 --> 00:50:30,040
Speaker 1: Many many polling places were closed because they, first of all,

977
00:50:30,080 --> 00:50:32,799
Speaker 1: the poll poll workers are generally elderly people, and the

978
00:50:32,800 --> 00:50:35,279
Speaker 1: poll workers were not able and willing to to man

979
00:50:35,400 --> 00:50:38,440
Speaker 1: the polling places. There are a number of states that

980
00:50:38,480 --> 00:50:41,239
Speaker 1: don't allow absentee ballots at all unless you have a

981
00:50:41,280 --> 00:50:44,839
Speaker 1: particular situation, like if you're disabled, and the states are saying, oh, well,

982
00:50:44,880 --> 00:50:46,879
Speaker 1: you know, the pure the virus are getting yill, that's

983
00:50:46,920 --> 00:50:50,120
Speaker 1: not a disability. Or before you get an absentee ballot,

984
00:50:50,160 --> 00:50:52,880
Speaker 1: you have to have it notarized, you have to have witnesses. Now,

985
00:50:53,160 --> 00:50:56,359
Speaker 1: how is all this going to happen? So it's very

986
00:50:56,400 --> 00:50:59,480
Speaker 1: concerning that people are going to have to choose between

987
00:50:59,520 --> 00:51:02,120
Speaker 1: their health and they're right to vote, and we don't

988
00:51:02,120 --> 00:51:04,600
Speaker 1: think that that should happen. And that's something that has

989
00:51:04,640 --> 00:51:07,799
Speaker 1: to be attended to right now, because if states don't

990
00:51:07,840 --> 00:51:10,839
Speaker 1: come up with plans for trying to enable everyone who

991
00:51:10,920 --> 00:51:13,839
Speaker 1: wants to vote to be able to vote, and for

992
00:51:14,040 --> 00:51:17,279
Speaker 1: counting absentee ballots and for administering this program, if you

993
00:51:17,280 --> 00:51:19,640
Speaker 1: don't come up right now with the plan and the resources,

994
00:51:19,880 --> 00:51:21,440
Speaker 1: a lot of people are going to be left out

995
00:51:21,480 --> 00:51:23,520
Speaker 1: and they're going to find that either you know, they

996
00:51:23,560 --> 00:51:25,919
Speaker 1: can't vote because they're afraid to go out to the poll,

997
00:51:26,400 --> 00:51:28,400
Speaker 1: or the vote is not going to be adequately counted.

998
00:51:28,880 --> 00:51:31,520
Speaker 1: So I think that right now, making democracy work is

999
00:51:31,560 --> 00:51:34,560
Speaker 1: really one of our top projects. What is the solution

1000
00:51:34,680 --> 00:51:37,239
Speaker 1: to some of these problems? What are your tangible solutions?

1001
00:51:37,560 --> 00:51:39,960
Speaker 1: But one tangible solution is that more states have to

1002
00:51:40,000 --> 00:51:43,319
Speaker 1: make absentee balloting available to people without having all these

1003
00:51:43,320 --> 00:51:48,279
Speaker 1: conditions and you know obstacles. Uh. The other solution is

1004
00:51:48,560 --> 00:51:52,200
Speaker 1: you were talking before about truth. A lot of the

1005
00:51:52,200 --> 00:51:56,080
Speaker 1: reason that's given the very thin veneer of justification that's

1006
00:51:56,120 --> 00:51:58,399
Speaker 1: given for we don't want absentee ballots or we need

1007
00:51:58,480 --> 00:52:02,560
Speaker 1: voter i D keep to carry government to approved voter

1008
00:52:02,680 --> 00:52:04,520
Speaker 1: i D, which means you have to go down to

1009
00:52:04,560 --> 00:52:06,960
Speaker 1: a governmental office live and get your voter i D

1010
00:52:07,120 --> 00:52:10,160
Speaker 1: and show it at the polls. The excuse for a

1011
00:52:10,200 --> 00:52:12,960
Speaker 1: lot of this is is that there could be fraud. Well,

1012
00:52:12,960 --> 00:52:16,160
Speaker 1: studies have shown that there's virtually no voter fraud, and

1013
00:52:16,400 --> 00:52:19,040
Speaker 1: it's just it's really a real unicorn. And again, I

1014
00:52:19,040 --> 00:52:21,640
Speaker 1: think if people understood that, that might sound good, but

1015
00:52:21,680 --> 00:52:24,560
Speaker 1: it's not true. I think truth is another thing that

1016
00:52:24,600 --> 00:52:26,719
Speaker 1: we're really fighting for these days. Can you listen to

1017
00:52:26,760 --> 00:52:29,120
Speaker 1: the evidence. Can you listen to the public health officials?

1018
00:52:29,120 --> 00:52:31,680
Speaker 1: Can you listen to what you what's real? I know

1019
00:52:31,760 --> 00:52:34,480
Speaker 1: for a fact that tech companies are very concerned about

1020
00:52:34,560 --> 00:52:38,759
Speaker 1: voter suppression, you know, and misinformation spreading online. This idea

1021
00:52:38,800 --> 00:52:41,920
Speaker 1: of countering truth around a lot of these very important initiatives,

1022
00:52:41,960 --> 00:52:44,640
Speaker 1: whether it's absentee ballots, whether it's showing up to the polls,

1023
00:52:44,760 --> 00:52:47,680
Speaker 1: all that kind of thing. You know. I'd be curious

1024
00:52:47,719 --> 00:52:50,399
Speaker 1: to know your take. There's a current battle happening right now.

1025
00:52:50,400 --> 00:52:54,680
Speaker 1: You have seven fifty advertisers boycotting Facebook asking for better

1026
00:52:54,719 --> 00:52:58,680
Speaker 1: policing of hateful content. Our social media companies doing enough

1027
00:52:58,719 --> 00:53:02,400
Speaker 1: to police harmful content, especially as we head into an

1028
00:53:02,440 --> 00:53:05,239
Speaker 1: election where voter suppression and the spread of misinformation will

1029
00:53:05,280 --> 00:53:08,719
Speaker 1: most certainly be attacked. It used to manipulate voters. Well,

1030
00:53:08,800 --> 00:53:11,360
Speaker 1: let me actually break your question down into two different parts,

1031
00:53:11,360 --> 00:53:13,520
Speaker 1: because you were starting by saying about the concern about

1032
00:53:13,600 --> 00:53:16,560
Speaker 1: voter suppression. I think one thing that everybody should be

1033
00:53:16,600 --> 00:53:20,200
Speaker 1: doing is to increase awareness of what is a fair

1034
00:53:20,239 --> 00:53:24,000
Speaker 1: way to improve access to the ballot for everybody. And

1035
00:53:24,040 --> 00:53:26,160
Speaker 1: some of those things are tech solutions. We've had tech

1036
00:53:26,239 --> 00:53:29,640
Speaker 1: solutions for years that are available and not widely enough used.

1037
00:53:30,080 --> 00:53:33,560
Speaker 1: How to enable differently able people to vote. You can

1038
00:53:33,600 --> 00:53:36,920
Speaker 1: blind people, do they have the technology? So there are

1039
00:53:36,960 --> 00:53:39,080
Speaker 1: a lot of places where we need the tech community

1040
00:53:39,320 --> 00:53:42,480
Speaker 1: and we need everybody to find out how you vote,

1041
00:53:42,560 --> 00:53:44,600
Speaker 1: to find out a voting can be made easier, and

1042
00:53:44,640 --> 00:53:47,480
Speaker 1: to let people know what the rules for voting are

1043
00:53:47,480 --> 00:53:49,040
Speaker 1: where they live. So one thing the a c l

1044
00:53:49,120 --> 00:53:51,080
Speaker 1: U Is doing is we have on our website you

1045
00:53:51,080 --> 00:53:54,000
Speaker 1: know your rights, you know what you're voting regulations are,

1046
00:53:54,560 --> 00:53:56,400
Speaker 1: and that's something that I think people really have to

1047
00:53:56,440 --> 00:53:59,359
Speaker 1: start thinking a lot about and to let let all

1048
00:53:59,400 --> 00:54:01,839
Speaker 1: their community is all their friends and family know about

1049
00:54:01,880 --> 00:54:04,360
Speaker 1: the importance of voting and how they what they have

1050
00:54:04,480 --> 00:54:06,400
Speaker 1: to do to vote, and to urge them to just

1051
00:54:06,440 --> 00:54:08,759
Speaker 1: get out and vote in whatever form that's going to take.

1052
00:54:09,200 --> 00:54:12,840
Speaker 1: So I think that's really important. In terms of disinformation

1053
00:54:13,000 --> 00:54:17,759
Speaker 1: on social media, people talk about the First Amendment and

1054
00:54:17,800 --> 00:54:20,400
Speaker 1: whether you know there's a First Amendment problem with Facebook

1055
00:54:20,719 --> 00:54:23,479
Speaker 1: telling you what you can't do. Well, there isn't, because

1056
00:54:23,520 --> 00:54:26,680
Speaker 1: the First Amendment only applies to the government, so you

1057
00:54:26,719 --> 00:54:28,880
Speaker 1: don't have a First Amendment right to say whatever you

1058
00:54:28,920 --> 00:54:31,680
Speaker 1: want on Facebook. However, I have to say that we're

1059
00:54:31,719 --> 00:54:34,680
Speaker 1: you know, we don't regard that issue is altogether a

1060
00:54:34,760 --> 00:54:37,640
Speaker 1: simplistic issue that Facebook should be telling everybody what they

1061
00:54:37,719 --> 00:54:40,560
Speaker 1: can't say, because even though the First Amendment does not

1062
00:54:40,640 --> 00:54:44,080
Speaker 1: apply to private companies, there's still a tremendous value to

1063
00:54:44,120 --> 00:54:47,560
Speaker 1: free speech. And there are a number of examples which

1064
00:54:47,600 --> 00:54:50,760
Speaker 1: you know, are we've come up with about people who

1065
00:54:50,800 --> 00:54:53,960
Speaker 1: are have speech suppressed for bad reasons. I'll give you

1066
00:54:54,000 --> 00:54:57,120
Speaker 1: one example. There was a woman who African American woman

1067
00:54:57,680 --> 00:54:59,960
Speaker 1: who posted something on Twitter and she got all the

1068
00:55:00,000 --> 00:55:04,359
Speaker 1: it is horrible racist responses, and she posted a screenshot

1069
00:55:04,480 --> 00:55:06,560
Speaker 1: of the responses that she got to show people what

1070
00:55:06,680 --> 00:55:09,080
Speaker 1: she was up against, and Twitter took it down because

1071
00:55:09,080 --> 00:55:13,000
Speaker 1: it included racist words that you know, okay, you know

1072
00:55:13,080 --> 00:55:16,080
Speaker 1: kind of this is the point. There was another uh

1073
00:55:16,360 --> 00:55:20,000
Speaker 1: An CLU lawyer wrote about a statue in Kansas that

1074
00:55:20,160 --> 00:55:23,799
Speaker 1: was a topless statue is a woman who were bare aggreasted,

1075
00:55:24,080 --> 00:55:27,319
Speaker 1: and so whatever the locality was in Kansas decided to

1076
00:55:27,360 --> 00:55:30,080
Speaker 1: take it down because yeah, that was they considered that

1077
00:55:30,160 --> 00:55:32,400
Speaker 1: to be important. So the A. C. L You lawyer

1078
00:55:32,400 --> 00:55:34,680
Speaker 1: who was challenging whether or not the I think it

1079
00:55:34,760 --> 00:55:37,759
Speaker 1: was city could take it down, posted a portrait a

1080
00:55:37,760 --> 00:55:40,160
Speaker 1: picture of the statue and that was it wasn't Twitter

1081
00:55:40,239 --> 00:55:42,680
Speaker 1: as I think Facebook, and that was taken down on

1082
00:55:42,719 --> 00:55:44,880
Speaker 1: the ground that it was obscene, so she couldn't post

1083
00:55:45,239 --> 00:55:47,520
Speaker 1: the picture of what she wanted to do. So we

1084
00:55:47,640 --> 00:55:51,200
Speaker 1: think that social media control is really a two age sword.

1085
00:55:51,880 --> 00:55:54,040
Speaker 1: What I liked is at one point Facebook had a

1086
00:55:54,040 --> 00:55:56,800
Speaker 1: protocol for about you what's true and what isn't true,

1087
00:55:57,280 --> 00:55:59,120
Speaker 1: And what they did was they gave you a flag.

1088
00:55:59,600 --> 00:56:02,760
Speaker 1: So they were concerned that something that was said wasn't true,

1089
00:56:03,160 --> 00:56:05,839
Speaker 1: they would have a neutral fact checker check it, and

1090
00:56:05,880 --> 00:56:08,080
Speaker 1: then if it didn't turn up well, they would put

1091
00:56:08,080 --> 00:56:10,520
Speaker 1: a little flag over it and say this has been questioned,

1092
00:56:10,560 --> 00:56:12,319
Speaker 1: and you could click on the flag and you could

1093
00:56:12,360 --> 00:56:14,640
Speaker 1: see why it was questioned. But they didn't just take

1094
00:56:14,680 --> 00:56:17,440
Speaker 1: it down. So you know, I I agree that, you know,

1095
00:56:17,480 --> 00:56:20,880
Speaker 1: disinformation is a tremendous problem, but I think that the

1096
00:56:20,960 --> 00:56:23,560
Speaker 1: idea that the solution is asked the tchech companies to

1097
00:56:23,640 --> 00:56:26,399
Speaker 1: decide what we should and shouldn't see. Yeah, I don't

1098
00:56:26,400 --> 00:56:29,360
Speaker 1: think that's so great either, And certainly they should not

1099
00:56:29,440 --> 00:56:32,400
Speaker 1: be doing it without a lot of transparency and accountability.

1100
00:56:32,440 --> 00:56:34,840
Speaker 1: If they're going to be taking things down, they should

1101
00:56:34,840 --> 00:56:38,239
Speaker 1: tell us what their protocols are, and you know, there

1102
00:56:38,239 --> 00:56:42,040
Speaker 1: should be more public discussion about where the balance is there. Yeah,

1103
00:56:42,040 --> 00:56:44,239
Speaker 1: It certainly seems like the protocols changed quite a bit,

1104
00:56:44,360 --> 00:56:47,239
Speaker 1: especially having covered tent for for this many years. It

1105
00:56:47,239 --> 00:56:50,040
Speaker 1: certainly seems like Facebook changes at Twitter changes it, and

1106
00:56:50,120 --> 00:56:52,879
Speaker 1: oftentimes it depends on public pressure. I'm curious to see

1107
00:56:52,920 --> 00:56:56,320
Speaker 1: what happens with all these advertisers boycotting. I think personally,

1108
00:56:56,360 --> 00:56:58,120
Speaker 1: I have a feeling it won't impact the bottom line

1109
00:56:58,200 --> 00:57:00,879
Speaker 1: much and they'll go back to business, says normal. But

1110
00:57:00,880 --> 00:57:03,239
Speaker 1: but who knows, you know, I do know that Zuckerbird

1111
00:57:03,320 --> 00:57:06,839
Speaker 1: cares uh deeply about his employees and and but they've

1112
00:57:06,840 --> 00:57:09,719
Speaker 1: been kind of up against you know, public scrutiny for

1113
00:57:09,719 --> 00:57:12,240
Speaker 1: a very long time. But but it certainly is interesting,

1114
00:57:12,360 --> 00:57:16,240
Speaker 1: especially when the stakes get higher and disinformation can go further,

1115
00:57:16,360 --> 00:57:19,520
Speaker 1: and especially as we get closer to an election, it

1116
00:57:19,600 --> 00:57:24,919
Speaker 1: certainly feels like everyone feels more triggered around it. Yeah, yeah, well,

1117
00:57:24,920 --> 00:57:27,080
Speaker 1: you know, one of the classics statements about the First

1118
00:57:27,120 --> 00:57:29,800
Speaker 1: Amendment is that in the marketplace of ideas, the best

1119
00:57:29,840 --> 00:57:33,760
Speaker 1: antidote to bad speech is more speech, right, so, you know, suppression.

1120
00:57:33,760 --> 00:57:35,920
Speaker 1: I think we always have to worry every time somebody's

1121
00:57:35,960 --> 00:57:39,880
Speaker 1: censoring and suppressing. Yeah, who are we giving that power to?

1122
00:57:40,320 --> 00:57:42,440
Speaker 1: You know, nearing a clothes because we don't have you

1123
00:57:42,560 --> 00:57:44,840
Speaker 1: for too much longer. I saw that you gave a

1124
00:57:44,880 --> 00:57:49,080
Speaker 1: talk um a Democrat and a Republican walk into a bar,

1125
00:57:49,640 --> 00:57:52,080
Speaker 1: and you're saying that it seems like these days Democrats

1126
00:57:52,120 --> 00:57:54,840
Speaker 1: and Republicans can't really agree on anything, but we all

1127
00:57:54,880 --> 00:57:58,480
Speaker 1: need to agree on fundamental American principles like do process,

1128
00:57:58,520 --> 00:58:04,040
Speaker 1: equality and freedom of conscience. So is that possible? Do

1129
00:58:04,080 --> 00:58:06,000
Speaker 1: you believe are you? Are you an optimist? Do you

1130
00:58:06,040 --> 00:58:10,400
Speaker 1: believe that in this current environment? Is that possible? Well?

1131
00:58:10,440 --> 00:58:12,560
Speaker 1: I think that's that's a great wrap up question. Where

1132
00:58:13,160 --> 00:58:15,720
Speaker 1: so that speech I gave it the Central Arkansas Library,

1133
00:58:16,480 --> 00:58:18,720
Speaker 1: And my chief point, as you're saying, is I think

1134
00:58:18,760 --> 00:58:23,680
Speaker 1: that people have to be able to agree on neutral principles.

1135
00:58:24,280 --> 00:58:27,440
Speaker 1: The Constitution was designed not to say what we're going

1136
00:58:27,480 --> 00:58:30,440
Speaker 1: to do about everything. It was designed to have everybody

1137
00:58:30,440 --> 00:58:33,960
Speaker 1: have a fair opportunity to be part of the process

1138
00:58:34,040 --> 00:58:36,360
Speaker 1: of deciding what we're going to do. So it sets

1139
00:58:36,400 --> 00:58:39,000
Speaker 1: up all these democratic structures where we get to vote

1140
00:58:39,040 --> 00:58:40,920
Speaker 1: for the people who are the policy makers and we

1141
00:58:40,960 --> 00:58:44,120
Speaker 1: all get to decide. But the principles there, the underlying

1142
00:58:44,160 --> 00:58:46,600
Speaker 1: principle is that everybody should have a fair and you know,

1143
00:58:46,920 --> 00:58:49,320
Speaker 1: the principle should be neutral. Everyone should get to vote.

1144
00:58:49,640 --> 00:58:51,960
Speaker 1: It's not like, you know, if you're a Democrat, your

1145
00:58:52,000 --> 00:58:54,400
Speaker 1: vote doesn't count in this area, and if your republic

1146
00:58:54,440 --> 00:58:56,600
Speaker 1: and your vote doesn't count in that area, is that's

1147
00:58:56,640 --> 00:59:01,280
Speaker 1: not fair. And the basic ideas of the freedom of speech,

1148
00:59:01,320 --> 00:59:04,840
Speaker 1: freedom of religion, they're all to be they manage the

1149
00:59:04,880 --> 00:59:07,680
Speaker 1: stations of the Golden rule that if I want the

1150
00:59:07,680 --> 00:59:10,120
Speaker 1: ability to just choose my own religion and decide what

1151
00:59:10,200 --> 00:59:12,520
Speaker 1: religion I'm going to practice, I have to respect your

1152
00:59:12,640 --> 00:59:14,960
Speaker 1: right to make a different choice and have your own religion,

1153
00:59:15,200 --> 00:59:17,480
Speaker 1: because that's the golden rule. If I want to say

1154
00:59:17,520 --> 00:59:20,120
Speaker 1: something that's unpopular, I have to respect your right to

1155
00:59:20,160 --> 00:59:22,640
Speaker 1: say something that's unpopular. And if I want to be

1156
00:59:22,720 --> 00:59:25,200
Speaker 1: treated fairly and not locked away for you know, doing

1157
00:59:25,240 --> 00:59:27,760
Speaker 1: something minor and never given a fair trial, I have

1158
00:59:27,840 --> 00:59:30,120
Speaker 1: to respect your right to have the same thing happened

1159
00:59:30,120 --> 00:59:33,320
Speaker 1: to you and to be all those fundamental principles are

1160
00:59:33,360 --> 00:59:35,880
Speaker 1: things that we really all should agree on. I think

1161
00:59:35,880 --> 00:59:39,120
Speaker 1: people get into arguing and assuming that they can never

1162
00:59:39,200 --> 00:59:42,840
Speaker 1: agree on the principles because they're differing on what they

1163
00:59:42,840 --> 00:59:45,440
Speaker 1: think the results should be. And I think to be

1164
00:59:45,520 --> 00:59:47,760
Speaker 1: part of the point of civil liberties is it's all

1165
00:59:47,800 --> 00:59:50,120
Speaker 1: about process, it's not about results. The a c l

1166
00:59:50,160 --> 00:59:53,240
Speaker 1: U is nonpartisan. We don't try to get Republicans elected,

1167
00:59:53,240 --> 00:59:55,920
Speaker 1: we don't try to get Democrats elected. We don't favor

1168
00:59:56,000 --> 01:00:00,360
Speaker 1: or disfavor individual politicians or individual parties, but we we

1169
01:00:00,480 --> 01:00:03,960
Speaker 1: favor or that there should be neutral principles that everybody

1170
01:00:04,000 --> 01:00:06,840
Speaker 1: can agree to to say, okay, here's what's fair. And

1171
01:00:06,880 --> 01:00:09,640
Speaker 1: the analogy I used in that talk at the Central

1172
01:00:09,760 --> 01:00:13,560
Speaker 1: Arkansas Library. It was one of the nights during the

1173
01:00:13,640 --> 01:00:15,920
Speaker 1: World Series, but fortunately not a night where there was

1174
01:00:15,920 --> 01:00:19,040
Speaker 1: a game, so people were able to come. And I said, okay,

1175
01:00:19,200 --> 01:00:22,280
Speaker 1: so what happens before a baseball game is that everybody

1176
01:00:22,360 --> 01:00:25,720
Speaker 1: has agreed on the underlying rules, and everyone agrees that

1177
01:00:25,720 --> 01:00:29,480
Speaker 1: the your umpires, your referees, and any sports should be neutral.

1178
01:00:30,320 --> 01:00:32,640
Speaker 1: And you don't want somebody who's partisan. If they were

1179
01:00:32,680 --> 01:00:34,960
Speaker 1: favoring one team, you get rid of them at all.

1180
01:00:35,000 --> 01:00:37,360
Speaker 1: Sports fans could agree to that. You know, maybe there

1181
01:00:37,360 --> 01:00:39,880
Speaker 1: would be a few who would be just so you know, matchiavellian,

1182
01:00:39,920 --> 01:00:43,800
Speaker 1: that they would rather have the biased umpire to always

1183
01:00:43,880 --> 01:00:45,760
Speaker 1: rule for their side. But I think sports fans can

1184
01:00:45,800 --> 01:00:48,600
Speaker 1: agree what you really want for a fair game. Is

1185
01:00:48,640 --> 01:00:50,600
Speaker 1: you want a fair game, you want everyone to agree

1186
01:00:50,640 --> 01:00:53,440
Speaker 1: on the principles beforehand. And I think that if we

1187
01:00:53,480 --> 01:00:56,000
Speaker 1: could sit down in small groups around the country and

1188
01:00:56,040 --> 01:00:59,800
Speaker 1: really talk about what the fundamental principles are, I am

1189
01:00:59,840 --> 01:01:01,880
Speaker 1: an enough of the patriot to think we actually could

1190
01:01:01,880 --> 01:01:03,800
Speaker 1: agree about a lot. And let me give you an

1191
01:01:03,840 --> 01:01:07,440
Speaker 1: example of why. I think there's some basis for hope.

1192
01:01:07,960 --> 01:01:11,200
Speaker 1: Maybe not optimism, but certainly hope. We were talking about

1193
01:01:11,280 --> 01:01:14,280
Speaker 1: voting rights. So one of the major problems is gerrymandering,

1194
01:01:14,600 --> 01:01:17,760
Speaker 1: the way when a party is in power they try

1195
01:01:17,800 --> 01:01:20,760
Speaker 1: to distort all the district and they try to stack

1196
01:01:20,840 --> 01:01:23,040
Speaker 1: the deck so that their party will remain in power.

1197
01:01:23,880 --> 01:01:26,280
Speaker 1: Or if the party in power in a particular state

1198
01:01:26,360 --> 01:01:28,720
Speaker 1: thinks it's to their advantage to not have that many

1199
01:01:28,720 --> 01:01:31,240
Speaker 1: people vote, they try to make it harder to register

1200
01:01:31,640 --> 01:01:36,160
Speaker 1: to vote for new voters, etcetera. Uh, we have had

1201
01:01:36,160 --> 01:01:38,560
Speaker 1: the A C l U and a number of other

1202
01:01:38,680 --> 01:01:41,440
Speaker 1: organizations working in coalition with US have had a fair

1203
01:01:41,480 --> 01:01:45,200
Speaker 1: amount of success doing ballot initiatives going to the people

1204
01:01:45,280 --> 01:01:48,720
Speaker 1: of the state in states like Michigan and Nevada and

1205
01:01:48,800 --> 01:01:51,880
Speaker 1: Missouri and Florida, where we were part of getting the

1206
01:01:51,920 --> 01:01:54,880
Speaker 1: amendment for a past that gave the vote back to

1207
01:01:54,960 --> 01:01:57,080
Speaker 1: people who have been convicted of a felony at some

1208
01:01:57,160 --> 01:02:00,160
Speaker 1: point and the people of the state. When you us

1209
01:02:00,240 --> 01:02:02,280
Speaker 1: the people of the state, you can get a majority

1210
01:02:02,440 --> 01:02:04,760
Speaker 1: sometimes the super majority of people who say no, we

1211
01:02:04,840 --> 01:02:07,720
Speaker 1: want the rules to be fair. Who doesn't want the

1212
01:02:07,800 --> 01:02:10,960
Speaker 1: rules to be fair are legislators who want who are

1213
01:02:11,000 --> 01:02:13,160
Speaker 1: incumbents and who want to keep their seats even if

1214
01:02:13,200 --> 01:02:16,080
Speaker 1: it takes unfair procedures to do it. So that's a

1215
01:02:16,480 --> 01:02:19,280
Speaker 1: real problem right where we have right now that the incumbents,

1216
01:02:19,320 --> 01:02:21,800
Speaker 1: the people who are trying to maintain power and not

1217
01:02:21,920 --> 01:02:25,160
Speaker 1: allow any sort of regime change, are pulling all the levers.

1218
01:02:25,680 --> 01:02:27,920
Speaker 1: But what I think, I think the chief grounds for

1219
01:02:27,960 --> 01:02:30,840
Speaker 1: optimism is that when you go to the American people

1220
01:02:30,920 --> 01:02:33,200
Speaker 1: themselves and say, well, do you want a fair system

1221
01:02:33,320 --> 01:02:34,720
Speaker 1: or do you want a system where you think your

1222
01:02:34,800 --> 01:02:37,560
Speaker 1: side is more likely to win? You talk to them

1223
01:02:37,600 --> 01:02:39,200
Speaker 1: about that, and I think that you're going to get

1224
01:02:39,200 --> 01:02:40,880
Speaker 1: them to say they would really like to see a

1225
01:02:40,920 --> 01:02:45,040
Speaker 1: fair system, and that is the promise of America. UM.

1226
01:02:45,280 --> 01:02:47,720
Speaker 1: Last question, you have taught at Brooklyn Law School since

1227
01:02:48,840 --> 01:02:51,000
Speaker 1: what is the lesson your students will take from this

1228
01:02:51,080 --> 01:02:55,880
Speaker 1: moment in history. Well, I know there are lots of lessons,

1229
01:02:55,920 --> 01:02:58,680
Speaker 1: but if you could extract it, what is the lesson

1230
01:02:58,720 --> 01:03:01,919
Speaker 1: your students will take from this moment in history? Well,

1231
01:03:01,960 --> 01:03:05,720
Speaker 1: you know, in an individual setting, one thing I'm doing

1232
01:03:05,760 --> 01:03:07,800
Speaker 1: for the fall is I am preparing of course that

1233
01:03:07,800 --> 01:03:11,240
Speaker 1: I'm calling COVID nineteen and the Constitution. So what we're

1234
01:03:11,240 --> 01:03:12,920
Speaker 1: gonna do in this seminar is we're going to be

1235
01:03:12,960 --> 01:03:15,320
Speaker 1: looking at the way in which the Constitution has been

1236
01:03:15,400 --> 01:03:17,440
Speaker 1: challenged and to see, you know, how well it holds up.

1237
01:03:17,440 --> 01:03:19,760
Speaker 1: What does the Constitution have to say about whether you

1238
01:03:19,760 --> 01:03:22,800
Speaker 1: can quarantine people and whether you can allow people to

1239
01:03:22,840 --> 01:03:24,560
Speaker 1: be at a religious assembly but not to go to

1240
01:03:24,600 --> 01:03:27,560
Speaker 1: a protest, and etcetera, etcetera. So I think there's a

1241
01:03:27,560 --> 01:03:29,600
Speaker 1: lot of interesting things there which I think are very

1242
01:03:29,640 --> 01:03:32,919
Speaker 1: much this particular moment, But big picture, what I would

1243
01:03:32,920 --> 01:03:36,960
Speaker 1: like the students to take away the constitutional law students

1244
01:03:37,040 --> 01:03:39,680
Speaker 1: especially is essentially what I just said to you, that

1245
01:03:39,760 --> 01:03:43,400
Speaker 1: the Constitution is about process. It's not about results. It's

1246
01:03:43,400 --> 01:03:45,720
Speaker 1: not about you know, you're a Republican and you're a Democrat,

1247
01:03:45,760 --> 01:03:48,320
Speaker 1: and we have two different countries depending on what your

1248
01:03:48,320 --> 01:03:51,440
Speaker 1: party is I think that we have one country and

1249
01:03:51,480 --> 01:03:54,400
Speaker 1: it's all about a neutral process for very good reasons,

1250
01:03:54,480 --> 01:03:56,439
Speaker 1: and I would like people to think more about that.

1251
01:03:56,840 --> 01:03:59,840
Speaker 1: After my speech at the Central Arkansas Library, I had

1252
01:04:00,600 --> 01:04:02,760
Speaker 1: two examples of people who talked to me. One guy

1253
01:04:02,800 --> 01:04:05,400
Speaker 1: came up to me, he said, I'm the Republican who

1254
01:04:05,440 --> 01:04:09,960
Speaker 1: walked into that bar, and he said, you know, you're

1255
01:04:09,960 --> 01:04:11,960
Speaker 1: making a lot of sense to me. And then there

1256
01:04:12,000 --> 01:04:14,240
Speaker 1: was another guy who talked to me who was a Democrat.

1257
01:04:14,280 --> 01:04:16,360
Speaker 1: He said, you know, I never really thought about that,

1258
01:04:16,360 --> 01:04:18,600
Speaker 1: that maybe it's not right if we're only trying to win.

1259
01:04:18,800 --> 01:04:20,680
Speaker 1: I never thought about you know, that's that's not what

1260
01:04:20,720 --> 01:04:23,760
Speaker 1: we do in sports. And that's what I'd like people

1261
01:04:23,800 --> 01:04:25,440
Speaker 1: to think about. You know, do you really want to

1262
01:04:25,480 --> 01:04:27,320
Speaker 1: do things that are only about how you think it's

1263
01:04:27,320 --> 01:04:29,920
Speaker 1: going to come out and cheat and destroy the system

1264
01:04:29,960 --> 01:04:31,720
Speaker 1: and you know, put a film on the scale and

1265
01:04:32,040 --> 01:04:34,800
Speaker 1: you know, stack the deck in order to make things

1266
01:04:34,840 --> 01:04:37,680
Speaker 1: come out to what your preferred result is in the

1267
01:04:37,720 --> 01:04:40,480
Speaker 1: short run or a long term. Is that just a

1268
01:04:40,520 --> 01:04:44,120
Speaker 1: really bad idea because it's just totally inconsistent. You know,

1269
01:04:44,160 --> 01:04:46,960
Speaker 1: we've just come from fourth of July and totally inconsistent

1270
01:04:47,000 --> 01:04:50,320
Speaker 1: with the premises on which we would like to believe

1271
01:04:50,360 --> 01:04:53,920
Speaker 1: our country was founded. Does technology throw a wrench in

1272
01:04:53,920 --> 01:04:56,720
Speaker 1: the system? I mean it does. It does create lots

1273
01:04:56,720 --> 01:05:00,160
Speaker 1: of things you can't control, and and it it always

1274
01:05:00,000 --> 01:05:02,560
Speaker 1: it's always you know, it's always new environment. So you know,

1275
01:05:02,640 --> 01:05:05,960
Speaker 1: different kind of example, we were talking about technology and surveillance,

1276
01:05:06,000 --> 01:05:08,520
Speaker 1: where of course technology has enabled a whole lot of

1277
01:05:08,520 --> 01:05:11,560
Speaker 1: surveillance that we then have to deal with. But technology

1278
01:05:11,640 --> 01:05:14,520
Speaker 1: also enabled a whole lot of new marketplaces of ideas.

1279
01:05:15,200 --> 01:05:16,600
Speaker 1: So the A. C. L. You did a lot of

1280
01:05:16,640 --> 01:05:19,720
Speaker 1: litigation you a few decades ago on applying first two

1281
01:05:19,720 --> 01:05:23,160
Speaker 1: Moment principles to the Internet, right, you know, because the

1282
01:05:23,160 --> 01:05:26,320
Speaker 1: government censor what was on the Internet because you know, child,

1283
01:05:26,360 --> 01:05:28,960
Speaker 1: a child might see it. Yeah, And so you know,

1284
01:05:29,040 --> 01:05:33,360
Speaker 1: every new generation of technology, there are new challenges about

1285
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Speaker 1: how you apply our principles like privacy and free speech,

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Speaker 1: et cetera to the Internet, but the principles remained the same.

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Speaker 1: I hope everyone is doing well in these strange and

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Speaker 1: surreal times and adjusting to the new normal. Most of important,

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Speaker 1: I hope you're staying healthy and somewhat saying follow along

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Speaker 1: on our social media. I'm at Lorie Siegel on Twitter

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Speaker 1: and Instagram, and the show is at First Contact Podcasts

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Speaker 1: on Instagram and on Twitter. We're at First Contact pod

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Speaker 1: and for even more from dot dot dot sign up

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Speaker 1: for our newsletter at dot dot dot media dot com

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Speaker 1: Backslash Newsletter. And if you like what you heard, leave

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Speaker 1: us a review on Apple Podcasts or wherever you listen.

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Speaker 1: We really appreciate it. First Contact is a production of

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Speaker 1: dot dot dot Media, Executive produced by Laurie Siegel and

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Speaker 1: Derek Dodge. This episode was produced and edited by Sabine

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Speaker 1: Jansen and Jack Reagan. The original theme music is by

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Speaker 1: Zander Sing. First Contact with Lori Siegel is a production

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Speaker 1: of dot dot dot Media and I Heart Radio