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Speaker 1: Justin heads up, this episode talks quite a bit about

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Speaker 1: sex trafficking. There Are No Girls on the Internet as

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Speaker 1: a production of iHeartRadio and Unbossed Creative. I'm Bridge Toad

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Speaker 1: and this is there Are No Girls on the Internet.

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Speaker 1: I am here with my producer Mike, and we have

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Speaker 1: to talk about anti trafficking work yet again. Specifically, we

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Speaker 1: have to talk about actor Ashton Kutcher's anti trafficking organization Thorn.

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Speaker 1: So we've talked about this a few times on the

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Speaker 1: show before. We did an episode with Michael Hobbs, formerly

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Speaker 1: of the podcast You're Wrong about digging into why anti

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Speaker 1: trafficking work lends itself so easily to inflated numbers and grandstanding.

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Speaker 1: We also did a recent episode about the anti trafficking

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Speaker 1: hero fantasy film Sound of Freedom and the dangers that

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Speaker 1: occur when anti trafficking work is treated as autumnally and

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Speaker 1: inherently good, because if you ask any questions too or

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Speaker 1: about the people who say they've dedicated their entire lives

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Speaker 1: to combating trafficking, you're basically on the side of the traffickers, right.

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Speaker 1: So I would argue that this is actually a very

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Speaker 1: harmful dynamic because trafficking is such a sensitive and important issue,

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Speaker 1: and because people pretty much all agree that trafficking is horrible,

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Speaker 1: and it creates this easy proxy to get people not

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Speaker 1: to mention money on your side if you say that

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Speaker 1: you're working on combating trafficking. So I would argue that

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Speaker 1: all of this means that if somebody says they work

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Speaker 1: on anti trafficking work, it actually should invite further scrutiny

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Speaker 1: into exactly what that work looks like. Case in point,

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Speaker 1: Ashton Kutcher's anti trafficking organization Thorn. So the reason that

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Speaker 1: I'm talking about this now is because actor Danny Masterson,

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Speaker 1: who played Hide on the very popular sitcom That seventy Show,

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Speaker 1: was convict did of raping two women, both of whom

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Speaker 1: were former members of the Church of Scientology. Danny is

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Speaker 1: also a scientologist, and survivors say that he used this

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Speaker 1: very powerful network of Scientology to avoid accountability for his actions.

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Speaker 1: Last week, Masterson was sentenced to thirty years to life

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Speaker 1: in prison. After his conviction, Masterson's friends and family wrote

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Speaker 1: letters to the judge asking for leniency in his sentencing.

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Speaker 1: The letters, frankly were not good. From my understanding at

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Speaker 1: this point, what judges are really looking for is less

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Speaker 1: of letters that are like, oh, he's such a nice guy,

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Speaker 1: he couldn't have done this, YadA YadA, but rather some

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Speaker 1: indication of contrition or some insight into the abuser's plan

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Speaker 1: to make better choices in the future. I have seen

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Speaker 1: some people say that these letters were leaked, but that.

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Speaker 2: Is actually not true.

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Speaker 1: It sounds like those letters were always part of the

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Speaker 1: public record, and then it's just up to the judge's

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Speaker 1: discretion on whether or not to make those letters public,

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Speaker 1: which in this case, the judge did do that.

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Speaker 2: So two of the people who.

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Speaker 1: Wrote letters on behalf of Danny Masterson asking for leniency

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Speaker 1: and his sentencing were fellow actors Mi Lacunis and Ashton Kutcher,

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Speaker 1: also from that seventy show. After public outcry about these letters,

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Speaker 1: they ended up releasing a video that basically apologized if

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Speaker 1: those letters were hurtful for people to hear, and saying

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Speaker 1: that they had only intended for the judge to see

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Speaker 1: those letters. Their letters basically read like a checklist of

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Speaker 1: all of the things and an abuser's friends say, after

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Speaker 1: he faces some consequences, you probably know the drill, right,

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Speaker 1: Like he's such a good guy. He's always been really

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Speaker 1: nice to me. He couldn't have done this, like he

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Speaker 1: has a daughter that's always a big one, like, oh,

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Speaker 1: his daughter. Ashton's letter talks specifically about how Danny never

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Speaker 1: did drugs and instructed the entire cast of that seventy

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Speaker 1: show to avoid drug use. I guess he was using

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Speaker 1: those drugs to incapacitate a woman that he wanted to assault.

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Speaker 2: I don't know.

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Speaker 1: That is also probably because drugs are forbidden in scientology.

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Speaker 1: Ashton also wrote about how Danny Masterson was really supportive

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Speaker 1: of the firefighters after nine to eleven and how, to

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Speaker 1: his knowledge, he has never lied.

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Speaker 2: Like these are really I don't know the letters. You can.

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Speaker 2: You can find them online.

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Speaker 1: I found them to be full of hyper specific information,

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Speaker 1: none of which are related to whether or not this

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Speaker 1: person as a rapist. Ashton ends his letter quote, while

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Speaker 1: I'm aware that the judgment has been cast as guilty

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Speaker 1: on two counts of rape by force, and the victims

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Speaker 1: have a great desire for justice, I hope that my

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Speaker 1: testament to his character is taken into consideration and sentencing.

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Speaker 1: I do not believe he is an ongoing harm to

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Speaker 1: society and having his daughter raised without a present father

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Speaker 1: would be a tertiary injustice in and of itself. So

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Speaker 1: those letters were pretty bad. Like if someone I knew

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Speaker 1: was convicted of two counts of forcible rape, you are

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Speaker 1: on your own, Like, I don't care how close we were,

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Speaker 1: I don't care how many times you told me not

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Speaker 1: to do drugs or how many times you like shook

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Speaker 1: hands with firefighters after nine to eleven, I probably wouldn't

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Speaker 1: be writing a letter like this on your behalf. So

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Speaker 1: here is where Ashton's anti sex trafficking work comes in,

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Speaker 1: because one thing that I keep reading online is people

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Speaker 1: asking why would Ashton Kutcher, someone who has dedicated his

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Speaker 1: life to combating sex trafficking and working with survivors, have

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Speaker 1: sexual violence, risk his reputation and the reputation of his

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Speaker 1: organization by writing a letter like this, And honestly, I

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Speaker 1: get it. I get that this looks like hypocrisy, but

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Speaker 1: I would actually argue that it speaks to hell when

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Speaker 1: someone is working to stop trafficking, it creates this sine

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Speaker 1: of automatic good doing, when in reality it should actually

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Speaker 1: be a sign for all of us to look deeper

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Speaker 1: into what their at work actually looks like. Because when

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Speaker 1: you do a lot of times what you see is

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Speaker 1: not work that is just working to combat the trafficking

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Speaker 1: of children, but rather working to further survey and criminalize

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Speaker 1: adult consensual sex workers. Then adult sex work is kind

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Speaker 1: of lumped in with the work to prevent sex trafficking

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Speaker 1: of children. They're kind of conflated. And that's what I

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Speaker 1: think is going on here. So let's start by a

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Speaker 1: little bit of a history around Ashton Kutcher's anti trafficking work.

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Speaker 1: So it might be a little bit hard to remember,

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Speaker 1: but back in the day, Ashton Kutcher and Demi Moore

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Speaker 1: were kind of a like power couple.

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Speaker 2: That's the only way that I can describe it, Like

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Speaker 2: you kind of had to be there.

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Speaker 1: Ashton was the first person to ever reach one million

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Speaker 1: followers on Twitter, and this was like a big deal.

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Speaker 1: I watched a clip from Good Morning America where I

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Speaker 1: think Ryan Seacrest actually comes out and presents him with

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Speaker 1: a framed award from the Guinness Book of World Records

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Speaker 1: because he was the first person to get a million

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Speaker 1: Twitter followers. At this point, Ashton like going on TV

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Speaker 1: and being asked to like wax philosophically about Twitter and

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Speaker 1: social media and technology. He was the person who was

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Speaker 1: sort of credited with getting a lot of other celebrities

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Speaker 1: to use Twitter too. This is a bit of a

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Speaker 1: side note and it's really just my opinion, but Ashton Kutcher,

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Speaker 1: like heretofore, he had kind of had a persona as

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Speaker 1: being a hot but stupid himbo frat guy, and I

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Speaker 1: think that this was definitely a move like him being

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Speaker 1: a tech guy. I think was definitely an attempt to

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Speaker 1: rebrand himself as like a serious tech guy.

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Speaker 2: You know.

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Speaker 1: He published a piece in Harper's called is Tech the

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Speaker 1: New Black?

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Speaker 2: Where he wrote about all of his tech predictions.

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Speaker 1: And I think it's really interesting how celebrities can use

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Speaker 1: certain issues like technology to really rebrand themselves, you know,

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Speaker 1: like pop on a pair of fake black rimmed glasses

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Speaker 1: and start talking tech wearing a blazer, and boom, you

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Speaker 1: are no longer the dude from Dude, Where's my car?

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Speaker 2: Now?

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Speaker 1: You are smart and thoughtful and nuance. Then you have

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Speaker 1: an elevated persona. Do you know what I'm saying?

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Speaker 3: Yeah, those fake glasses are really the key.

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Speaker 1: The one person who I think is really rocking, like,

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Speaker 1: oh I'm smart now is Rick Perry. He He has

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Speaker 1: really been like all on the like, oh did you

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Speaker 1: see I'm wearing glasses now, I'm smart now Kick.

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Speaker 3: It's like a total rebrand with those new glasses that

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Speaker 3: Rick Perry has started wearing. And it's I guess it's working.

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Speaker 2: Yeah, he's like Clark Kent in reverse. He puts on

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Speaker 2: the glasses. You don't even know what's him. Okay.

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Speaker 1: So back in two thousand and nine, when Ashton Kutcher

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Speaker 1: was in a relationship with Demi Moore, they started the

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Speaker 1: DNA Foundation, an anti trafficking organization together. The name DNA

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Speaker 1: Foundation is a mix of their name Demi and Ashton Foundation.

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Speaker 1: They got the idea to do this work after Demi

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Speaker 1: Moore reportedly was watching a documentary about sex slavery in

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Speaker 1: Cambodia on MSNBC and wanted to start an organization that

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Speaker 1: combat in that kind of stuff in the United States too.

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Speaker 1: Folks listening might recall Ashton's Real Men Don't Buy Girls campaign.

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Speaker 2: Mike, do you remember this at all?

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Speaker 3: I wish I did. I know, we talked about it

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Speaker 3: a little bit earlier. I don't. I don't know where

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Speaker 3: I was when this campaign was happening.

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Speaker 1: Well, I remember like it was yesterday, and I searched

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Speaker 1: high and low, like I did a deep, deep search

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Speaker 1: to try to find an original video from the Real

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Speaker 1: Men Don't Buy Girls campaign. This is a little bit

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Speaker 1: of the conspiracy theory. I think they must have just

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Speaker 1: removed it, had it scrubbed from the Internet, because I

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Speaker 1: can't find anything.

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Speaker 2: I can only find two.

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Speaker 1: Videos that were clearly user generated content that were not

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Speaker 1: representative of what the original videos were.

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Speaker 3: Where were these videos airing? Were you everywhere everywhere like

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Speaker 3: television commercials.

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Speaker 2: They were on YouTube, they were on TV, they were

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Speaker 2: on Facebook.

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Speaker 1: This also was the early two thousands when we just

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Speaker 1: didn't have a lot of social media content. If a

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Speaker 1: celebrity was doing something online, it was like automatically newsworthy,

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Speaker 1: So I can't believe you don't remember them. Basically, the

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Speaker 1: point of the campaign was that it was showing all

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Speaker 1: of these like manly famous men like Bradley Cooper and

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Speaker 1: Sean Penn doing all kinds of manly things, but then

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Speaker 1: being like, oh, well, manly men make grilled cheese sandwiches

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Speaker 1: using an iron because they're manly. But real men don't

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Speaker 1: Buy Girls, and the commercials were I guess I will

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Speaker 1: just say. They were mostly panned for being cringey, which

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Speaker 1: is probably why I cannot find any of them now.

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Speaker 2: Like literally, when I Google, when I found a.

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Speaker 1: Website that had like six squares from YouTube like it

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Speaker 1: like embedded YouTube videos, each of those squares was like

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Speaker 1: video not found, Video not found, Video not found, video

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Speaker 1: not found. So Ashton Kutcher apparently has the power to

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Speaker 1: get these videos removed from the Internet.

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Speaker 2: But they I remember them like they were yesterday.

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Speaker 1: You can still find if you google real Men Don't

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Speaker 1: Buy Girls, you can still find the images of like

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Speaker 1: Ashton Kutcher. He's wearing like a nit beanie, like a

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Speaker 1: slouchy knit beanie, and he's holding the sign that says

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Speaker 1: real men don't Buy Girls.

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Speaker 2: That still exists.

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Speaker 1: But the commercials You'll just have to take my word

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Speaker 1: for it because I remember them like they were yesterday.

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Speaker 4: Let's take a quick break at her back.

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Speaker 1: So back then, DNA was really kind of centered around

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Speaker 1: celebrities raising awareness about trafficking, which, on his face, is

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Speaker 1: not like the worst thing in the world. If anything,

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Speaker 1: these ads were a little bit cringe, but they weren't

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Speaker 1: like the worst thing ever. But DNA was a little

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Speaker 1: bit tricky with their numbers. When the organization was first

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Speaker 1: getting started, they misreped and inflated stats and numbers around trafficking,

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Speaker 1: which is going to be a theme in this episode.

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Speaker 1: This will not be the first time that you're hearing

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Speaker 1: that this anti sex trafficking organization is misreporting and inflating

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Speaker 1: numbers around sex trafficking. So this part of the conversation

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Speaker 1: really ties into this one stat Ashton repeats this one

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Speaker 1: claim over and over that there are between one hundred

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Speaker 1: thousand and three hundred thousand child sex slaves in the

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Speaker 1: United States today. He said it unchecked on CNN's Pierce Morgan.

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Speaker 1: He testified before Congress and gave that same stat This

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Speaker 1: stat is repeated over and over and over again. If

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Speaker 1: you google it, you get lots and lots of hits.

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Speaker 1: But surprise, this is not an accurate stat not even close.

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Speaker 1: The Washington Post dug into this specific statistic and found

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Speaker 1: that it comes from a two thousand and one report

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Speaker 1: from Richard J. ST's and Neil Wiener of the University

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Speaker 1: of Pennsylvania. So this study was published in two thousand

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Speaker 1: and one, so it relied on data from the nineteen nineties.

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Speaker 1: But people have been parroting it well unto the twenty

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Speaker 1: tens and even sometimes today, so obviously it's already like

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Speaker 1: a little bit suspect. The report never said that one

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Speaker 1: hundred thousand to three hundred thousand children are being trafficked

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Speaker 1: in the United States. What they did say was that

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Speaker 1: one hundred thousand to three hundred thousand children are quote

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Speaker 1: at risk for commercial sexual exploitation. So that's a different

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Speaker 1: thing than how Ashton Kutcher reported it. That there are

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Speaker 1: one hundred thousand to three hundred thousand kids being trafficked

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Speaker 1: in the United States then was not true. But even

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Speaker 1: if that is just the number of young people at risk,

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Speaker 1: it's still not totally correct. The post dug into how

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Speaker 1: this figure came to be. The researcher started by compiling

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Speaker 1: the number of youth in fourteen different categories, such as

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Speaker 1: foreign children, children in public housing, or female gang members,

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Speaker 1: but many of these categories could overlap, such as a

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Speaker 1: female foreign born child and public housing who was part

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Speaker 1: of a gang that one person would count a S three.

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Speaker 1: The three hundred thousand figure also relied on a series

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Speaker 1: of guesses on the part of the researchers, such as

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Speaker 1: the assumption that thirty five percent of runaway youth away

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Speaker 1: from home at least a week were quote at risk,

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Speaker 1: or that one quarter of one percent of all youth

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Speaker 1: ages ten to seventeen we're at risk. So I should

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Speaker 1: say that the researchers themselves in their report, they say

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Speaker 1: that they're guessing at certain things, and they make it

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Speaker 1: very clear that they are not trying to give an

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Speaker 1: authoritative number. Yet that stat is often misreported as an

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Speaker 1: authoritative number.

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Speaker 3: Yeah, that's like the eternal challenge and fear. I think

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Speaker 3: of scientists trying to communicate the results to a popular press, right,

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Speaker 3: Like anytime you take a like a training of how

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Speaker 3: scientists should talk to journalists, one of the things that

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Speaker 3: they highlight is the emphasis of scientists on precision and accuracy.

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Speaker 3: And you know, that's exactly what gets lost when things

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Speaker 3: get translated, results get translated to popular press. So it's

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Speaker 3: like they could have in that paper dotted all their eyes,

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Speaker 3: crossed all their t's to lay out every single assumption

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Speaker 3: and every kind They just had to guess at a

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Speaker 3: quantity to like really qualify it that this is our

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Speaker 3: best estimate. But there are all these different ways that

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Speaker 3: it could be inaccurate. But then either through people just

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Speaker 3: trying to be convenient or perhaps people trying to distort

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Speaker 3: statistics into to serve narratives that they're trying to push.

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Speaker 3: You know, that very careful, nuanced estimate becomes just a number, right,

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Speaker 3: which loses all of the nuance, and that I mean

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Speaker 3: all that aside. It also sounds like whoever changed one

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Speaker 3: hundred thousand of three hundred thousand children at risk to

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Speaker 3: one hundred thousand to three hundred child thousand children currently

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Speaker 3: being trafficked. That's not just like losing nuance, that's I

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Speaker 3: would say, an active distortion.

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Speaker 1: So keep that in mind as we go on, because

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Speaker 1: that's going to be very relevant in just a moment.

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Speaker 1: So I think I know somebody who would agree with you,

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Speaker 1: which is SD's one of the researchers who worked on

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Speaker 1: this study who openly agrees that that number is bunk.

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Speaker 1: St says, I'm fully aware of the controversy that surrounds

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Speaker 1: my and other scholar's estimates of the number of children

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Speaker 1: at risk of sexual exploitation, he told The Post's fact checker.

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Speaker 1: He added, clearly a new, more current study is needed.

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Speaker 1: So in two thousand and eight, a year before Ashton

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Speaker 1: and Demi would start DNA, Michelle Stransky and David Finkelhor

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Speaker 1: of the respected Crimes Against Children Research Center at the

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Speaker 1: University of New Hampshire wrote a report explaining the problems

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Speaker 1: with SD's and Wiener's estimate, as well as other claims

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Speaker 1: about the extent of juvenile sex work. Their report said,

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Speaker 1: in all caps, please do not cite these numbers. The

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Speaker 1: report pleaded, the reality is that we don't currently know

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Speaker 1: how many juveniles are involved in sex work. Scientifically, credible

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Speaker 1: estimates do not exist. So I kind of feel for

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Speaker 1: these researchers who put together this like nuanced thing where

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Speaker 1: they were like, please don't use this number.

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Speaker 2: Somebody takes that number and they're like, this is the number.

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Speaker 1: And then it's repeated over and over and over again,

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Speaker 1: and I kind of do need to give a little

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Speaker 1: bit of a caveat to hear that Astion was far

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Speaker 1: from the only person who repeated this bad statistic. In fact,

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Speaker 1: this number has been used by elected officials on both

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Speaker 1: sides of the aisle. After the Washington Post fact check

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Speaker 1: on it, Senator Rob Portman from Ohio pledged to stop

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Speaker 1: citing that number. I'm inclined to think like, maybe Ashton

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Speaker 1: and Demi didn't know that this number was like not

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Speaker 1: really a number that they should be repeating. I do

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Speaker 1: think if I were creating an organization entirely dedicated to trafficking,

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Speaker 1: I might not have gone on TV multiple times and

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Speaker 1: repeated a number that respected names who were already in

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Speaker 1: this space had loudly debunked and all caps asked people

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Speaker 1: to stop using.

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Speaker 2: But the question really is.

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Speaker 1: Did Ashton and Demi know that they were repeating incorrect

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Speaker 1: statistics to champion their anti trafficking work, And to be honest,

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Speaker 1: it kind of sounds like maybe they did. The Village

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Speaker 1: Voice did a whole investigation where they actually contracted a

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Speaker 1: methodologist at Arizona State University to dig into the Demi

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Speaker 1: and Ashton Foundation for their record. This is probably because

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Speaker 1: The Village Voice was known for their prolific adult classified

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Speaker 1: section where people engaged in sex for money, So it's

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Speaker 1: probably one of the reasons why the Village Voice would

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Speaker 1: be very, very invested in debunking the claims made by

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Speaker 1: Demi and Ashton's foundation. So in the Village Voice piece,

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Speaker 1: they talk about Maggie and Trevor Nielsen, who run the

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Speaker 1: California based Global Philanthropy Group and worked as consultants to

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Speaker 1: celebrities who want to start charities. When confronted with the

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Speaker 1: inaccuracy of their numbers, Maggie Nielsen did not seem super faced.

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Speaker 1: She said, quote Versus most social issues I've worked on,

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Speaker 1: there's actually a dearth of data, so it was absolutely

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Speaker 1: cobbled together. All of the core data that we use

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Speaker 1: gets attacked all the time, She says. The challenge is

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Speaker 1: that it's that or nothing right, And I frankly don't

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Speaker 1: care if the number is two hundred thousand, five hundred thousand,

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Speaker 1: a million, or one hundred thousand. It needs to be addressed.

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Speaker 1: While I absolutely agree that there is a need for

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Speaker 1: better data, that people who want to spend all day

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Speaker 1: bitching about the methodologies used, I'm not very interested in,

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Speaker 1: she says.

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Speaker 2: So as a methodologist, Mike is what are your thoughts

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Speaker 2: on that?

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Speaker 3: Well, it's funny that she seems totally fine to use

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Speaker 3: the numbers but is actively hostile to questioning the methodology

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Speaker 3: behind them, Like, you can't have it both ways. You

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Speaker 3: don't get to use the numbers if they're not based

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Speaker 3: on a rigorous method. You can't have your cake and

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Speaker 3: eat it too well.

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Speaker 1: There's a reason why she might be interested in using

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Speaker 1: the numbers when they sound very high, and that might

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Speaker 1: be money. You know, if you're able to use a

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Speaker 1: big number, that means that you're better able to fundraise.

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Speaker 1: According to Professor Steve doug Night, Chair of Journalism at

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Speaker 1: Arizona State University, who specializes in the analysis of quantitative methodology,

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Speaker 1: this is the person that the Village of Voice contracted

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Speaker 1: out to do their investigation into Demi and Ashen's foundation.

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Speaker 1: Dwig says, let's face it, a study or a story

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Speaker 1: saying several thousand young teens are being exploited in the

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Speaker 1: sex trade has a lot less impact than one suggesting

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Speaker 1: that several hundred thousand are at risk. Researchers, journalists, law

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Speaker 1: enforcement and politicians alike have incentives to focus on the

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Speaker 1: much bigger number, and I kind of can't help but

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Speaker 1: think that that's part of what's going on here. I do,

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Speaker 1: to be super clear, I do think that one of

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Speaker 1: the issues is that trafficking is such is the kind

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Speaker 1: of crime where it is legitimately hard to find hard,

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Speaker 1: accurate numbers around. And I also think that when you're

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Speaker 1: putting together a charity where you're fundraising, I can imagine

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Speaker 1: not balking at a number that's big when you're when

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Speaker 1: you're an organization that is like trying to raise fun

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Speaker 1: So I don't think that that they were entirely motivated

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Speaker 1: by using the biggest number for fundraising. But I also

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Speaker 1: don't think that was not a motivation, you know. And

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Speaker 1: I also think that, like when you're starting an organization

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Speaker 1: or charity that is dedicated to combating an issue that

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Speaker 1: is as sensitive and complex as trafficking, I think that

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Speaker 1: you really have to be invested in telling people the truth.

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Speaker 1: And if the truth is this is the kind of

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Speaker 1: crime where there aren't hard numbers, here's what we know.

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Speaker 2: That's the truth that you gotta give.

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Speaker 1: Like you can't just cobble together numbers about something that

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Speaker 1: is as sensitive and important and as big of a

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Speaker 1: deal as trafficking.

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Speaker 2: Yeah.

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Speaker 3: Absolutely, I mean it sounds like they really positioned this

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Speaker 3: nonprofit DNA, which is it's such a terrible name. They

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Speaker 3: just slapped their first names there with like acut C,

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Speaker 3: like not even the letter just an.

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Speaker 2: N, Demi and Ashton.

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Speaker 3: Yeah, Like it feels like an issue that deserves a

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Speaker 3: little more respect, but even still, it seems like they're

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Speaker 3: trying to position it as like a population health organization

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Speaker 3: where they're talking about like this many American children are

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Speaker 3: subject to this harm. That's a population health argument, and

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Speaker 3: so there's an obligation to get the numbers right, to

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Speaker 3: like have real numbers if your numbers aren't made up,

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Speaker 3: Like there should be somebody at the organization who has

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Speaker 3: looked at the primary source that original article and reviewed

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Speaker 3: the methodology and is willing to like put their name

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Speaker 3: on the line and saying like, oh, yeah, this number

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Speaker 3: that we're using is a good one. And sounds like

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Speaker 3: they just like didn't have that at all, Like it's

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Speaker 3: just pure marketing.

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Speaker 1: And I guess I just feel like this is me

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Speaker 1: on my soapbox. But people deserve the truth. If you

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Speaker 1: are raising awareness but you're not able to tell the truth,

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Speaker 1: what are you raising awareness for? If you knowingly are like,

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Speaker 1: I know, these numbers are cobbled together, but it's an

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Speaker 1: important issue. We just need to talk about it, and

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Speaker 1: like we have to have numbers attached to it, so

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Speaker 1: we're just gonna make them up. I don't think that

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Speaker 1: raising awareness, But that doesn't give people the awareness they need.

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Speaker 1: If people are going to be aware of an issue,

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Speaker 1: they need to be aware of it accurately. And if

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Speaker 1: you're raising awareness for an issue and you're doing it

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Speaker 1: in a way that is not accurate, what are you

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Speaker 1: actually doing other than raising money?

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Speaker 3: If you don't have any rigorous numbers that you can

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Speaker 3: point to as evidence that it's happening, maybe your first

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Speaker 3: step should be to like get some decent numbers and

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Speaker 3: some decent data to guide your efforts. Otherwise you're just

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Speaker 3: operating based on intuition and preferences.

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Speaker 2: Vibes. Yeah, that's what I think.

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Speaker 1: All of the best anti trafficking work is just going

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Speaker 1: off vibes, you know.

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Speaker 4: More. After a quick break, let's get right back into it.

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Speaker 2: Okay.

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Speaker 1: So in twenty twelve, Demi Moore and Ashton Kutcher have

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Speaker 1: a very messy, very public split following rumors of Ashton

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Speaker 1: being unfaithful and Demi's addiction issues, and amid that split,

437
00:24:15,320 --> 00:24:19,359
Speaker 1: the organization is rebranded from Demi and Ashton to Thorn

438
00:24:19,720 --> 00:24:22,800
Speaker 1: Colon Digital Defenders of Children, and this is where they

439
00:24:22,840 --> 00:24:26,400
Speaker 1: adopt a much bigger technology and data focus rather than

440
00:24:26,560 --> 00:24:31,560
Speaker 1: just sort of general celebrity awareness campaigns about trafficking, and

441
00:24:31,680 --> 00:24:34,560
Speaker 1: this is what leads to Thorn becoming a major player

442
00:24:34,640 --> 00:24:38,560
Speaker 1: in law enforcement and Internet regulation that they are today.

443
00:24:38,960 --> 00:24:42,200
Speaker 1: So Ashton and DEMI explained saying, quote, for the past

444
00:24:42,200 --> 00:24:44,680
Speaker 1: three years, we have focused our work broadly on combating

445
00:24:44,760 --> 00:24:47,639
Speaker 1: child sex trafficking. It has become crystal clear in our

446
00:24:47,640 --> 00:24:50,639
Speaker 1: effort that technology plays an increasingly large role in this

447
00:24:50,760 --> 00:24:54,280
Speaker 1: crime and in sexual exploitation of children overall. We believe

448
00:24:54,320 --> 00:24:57,040
Speaker 1: that the technology driven aspect of these crimes demands its

449
00:24:57,080 --> 00:25:01,000
Speaker 1: own attention and investment. Thorn's website that their motto is

450
00:25:01,280 --> 00:25:04,640
Speaker 1: we build technology to defend children from sexual abuse, and

451
00:25:04,920 --> 00:25:07,400
Speaker 1: I think it is really important to dig into what

452
00:25:07,440 --> 00:25:11,040
Speaker 1: that has actually looked like. So major, major shout outs

453
00:25:11,040 --> 00:25:13,359
Speaker 1: to Violet Blue, who wrote a great piece at n

454
00:25:13,440 --> 00:25:16,919
Speaker 1: Gadget called sex Flies and Surveillance. Something's wrong with the

455
00:25:16,920 --> 00:25:20,240
Speaker 1: war on sex trafficking. So earlier I talked about how

456
00:25:21,000 --> 00:25:25,000
Speaker 1: oftentimes anti sex trafficking work conflates a bunch of issues

457
00:25:25,359 --> 00:25:28,320
Speaker 1: the trafficking of adults for labor, the trafficking of adults

458
00:25:28,359 --> 00:25:31,200
Speaker 1: for sex, the trafficking of children for sex, and adults

459
00:25:31,280 --> 00:25:34,960
Speaker 1: engaging and consensual sex work, and then is essentially.

460
00:25:34,600 --> 00:25:35,960
Speaker 2: What is going on with Thorn.

461
00:25:36,359 --> 00:25:40,800
Speaker 1: Violet Blue reports that of Thorn's thirty one nonprofit partners,

462
00:25:40,920 --> 00:25:44,000
Speaker 1: twenty seven of those groups target adults and vowed to

463
00:25:44,040 --> 00:25:47,760
Speaker 1: abolish consensual sex work under the banner of saving children

464
00:25:47,840 --> 00:25:51,639
Speaker 1: from sex trafficking. Perhaps unsurprisingly, Thorn was a champion of

465
00:25:51,680 --> 00:25:56,560
Speaker 1: SESTA Fasta, legislation that openly conflated adults, consensual sex work

466
00:25:56,880 --> 00:25:59,800
Speaker 1: and the trafficking of children. If folks don't know, a

467
00:26:00,040 --> 00:26:02,600
Speaker 1: pick and dirty way to understand Cesta Fasta is that

468
00:26:02,720 --> 00:26:07,000
Speaker 1: under SESTA Fasta, every website or online platform can be

469
00:26:07,080 --> 00:26:10,240
Speaker 1: held liable for posting what the law describes as sex work.

470
00:26:10,440 --> 00:26:14,080
Speaker 1: But sex work is undefined and there's no distinction between

471
00:26:14,440 --> 00:26:17,960
Speaker 1: human trafficking and consensual sex work. Even the Department of

472
00:26:18,119 --> 00:26:21,719
Speaker 1: Justice asked the House Judiciary Committee to change Foster's focus

473
00:26:21,760 --> 00:26:25,240
Speaker 1: to traffickers and not cases where there is minimal federal

474
00:26:25,280 --> 00:26:29,200
Speaker 1: interests like consensual sex work, and that request was ignored.

475
00:26:29,440 --> 00:26:32,600
Speaker 1: According to the Government Accountability Office, which is an organization

476
00:26:32,640 --> 00:26:36,320
Speaker 1: that provides fact based information to Congress, SESTA FOSTA and

477
00:26:36,359 --> 00:26:40,440
Speaker 1: the shuttering of the adult classified website Backpage actually made

478
00:26:40,440 --> 00:26:43,960
Speaker 1: it harder for authorities to track traffickers because quote it

479
00:26:44,040 --> 00:26:47,840
Speaker 1: disrupted the landscape of the online commercial sex market Cesta

480
00:26:47,840 --> 00:26:50,520
Speaker 1: Fasta and the shuttering of known sites where sex is

481
00:26:50,560 --> 00:26:53,800
Speaker 1: sold like Backpage, actually made it harder to tract traffickers,

482
00:26:54,040 --> 00:26:57,639
Speaker 1: the report reads, quote Gathering tips and evidence to investigate

483
00:26:57,680 --> 00:27:01,520
Speaker 1: and prosecute those who control or you online platforms has

484
00:27:01,600 --> 00:27:04,720
Speaker 1: become more difficult due to the relocation of platforms overseas,

485
00:27:05,000 --> 00:27:08,280
Speaker 1: platform's use of complex payment systems, and the increased use

486
00:27:08,320 --> 00:27:11,800
Speaker 1: of social media platforms. So when Sesta Fosta was passed,

487
00:27:11,840 --> 00:27:14,919
Speaker 1: Thorn released a statement saying, quote, the passage of Sesta

488
00:27:14,960 --> 00:27:18,280
Speaker 1: Fasta marks an important moment in the mission to defend happiness,

489
00:27:18,359 --> 00:27:21,159
Speaker 1: and we are encouraged that advocates, government and technology are

490
00:27:21,160 --> 00:27:25,320
Speaker 1: working together to prioritize the protection of youth online. So

491
00:27:25,600 --> 00:27:27,800
Speaker 1: if you've listening to this podcast, you probably already know

492
00:27:27,840 --> 00:27:30,280
Speaker 1: that whenever I hear someone say that they are working

493
00:27:30,320 --> 00:27:34,360
Speaker 1: to protect youth online, my Spidey senses are tingling, as

494
00:27:34,400 --> 00:27:38,800
Speaker 1: should yours be tingling, because that generally is basically code

495
00:27:38,920 --> 00:27:43,720
Speaker 1: for we are going to surveil sex workers, comma, everybody

496
00:27:44,320 --> 00:27:48,399
Speaker 1: in an effort to protect kids online. So Thorn has

497
00:27:48,640 --> 00:27:52,240
Speaker 1: two main tech products that they make. One is called Safer,

498
00:27:52,440 --> 00:27:55,440
Speaker 1: a content moderation tool that they offer to Internet companies.

499
00:27:55,640 --> 00:27:58,760
Speaker 1: The other is called Spotlight, a data mining and user

500
00:27:58,800 --> 00:28:02,400
Speaker 1: profiling tool offer to law enforcement. Now, both of these

501
00:28:02,440 --> 00:28:05,439
Speaker 1: tools use data sources and AI to automate policing of

502
00:28:05,480 --> 00:28:08,200
Speaker 1: sex content, but Spotlight is the one that I really

503
00:28:08,240 --> 00:28:10,600
Speaker 1: want to focus on. So at a police training video

504
00:28:10,640 --> 00:28:14,120
Speaker 1: for Spotlight, an analyst described that as quote the Google

505
00:28:14,160 --> 00:28:18,120
Speaker 1: for human trafficking or online escort activities. Thorn has been

506
00:28:18,320 --> 00:28:21,800
Speaker 1: a little cagey about exactly how Spotlight works, but it

507
00:28:21,880 --> 00:28:25,320
Speaker 1: is basically a data mining and facial recognition operation that

508
00:28:25,440 --> 00:28:29,320
Speaker 1: scrapes websites for adult content. Pre SESSTA Fasta, I would

509
00:28:29,320 --> 00:28:32,439
Speaker 1: have said that it scrapes websites like Craigslist and Backpage,

510
00:28:32,480 --> 00:28:36,280
Speaker 1: But after Sessta Fosta was passed, Craigslist removed their personal ads,

511
00:28:36,280 --> 00:28:39,760
Speaker 1: and then right before Sesta Foster was passed, Backpage shuttered

512
00:28:39,800 --> 00:28:43,440
Speaker 1: and it no longer exists. Spotlight's handout says Spotlight is

513
00:28:43,440 --> 00:28:45,760
Speaker 1: built on a data archive of millions of records of

514
00:28:45,880 --> 00:28:49,719
Speaker 1: escort ads and form data collected from various websites.

515
00:28:50,000 --> 00:28:53,440
Speaker 2: So after Spotlight scrapes all of this data.

516
00:28:53,480 --> 00:28:56,560
Speaker 1: They then take this massive amount of data and turn

517
00:28:56,600 --> 00:28:59,680
Speaker 1: it into an asset for law enforcement. The tool allows

518
00:28:59,680 --> 00:29:02,640
Speaker 1: on all sort of search or filter escort ads based

519
00:29:02,680 --> 00:29:07,239
Speaker 1: on phone number, email, keywords, age, and location. So the

520
00:29:07,280 --> 00:29:10,080
Speaker 1: first thing that I think is really worth zeroing in

521
00:29:10,160 --> 00:29:13,600
Speaker 1: on are these partnerships that Thorn and Spotlight have with

522
00:29:13,680 --> 00:29:17,000
Speaker 1: law enforcement. They're concerning because they open the door for

523
00:29:17,160 --> 00:29:20,200
Speaker 1: this use of a vast surveillance network to be done

524
00:29:20,480 --> 00:29:24,040
Speaker 1: totally unregulated. Forbes spoke to med Foster, who authored a

525
00:29:24,080 --> 00:29:27,080
Speaker 1: report for the Center on Privacy and Technology at Georgetown

526
00:29:27,160 --> 00:29:29,760
Speaker 1: Law who released a research report saying that there was

527
00:29:29,840 --> 00:29:34,000
Speaker 1: no scientific validation for facial recognition as a reliable tool

528
00:29:34,080 --> 00:29:38,360
Speaker 1: in any criminal investigation. She writes, technology initially intended to

529
00:29:38,360 --> 00:29:41,880
Speaker 1: serve a narrow purpose like combating human trafficking, can easily

530
00:29:41,960 --> 00:29:45,680
Speaker 1: and quickly expand into a tool for mass surveillance, especially

531
00:29:45,880 --> 00:29:48,800
Speaker 1: when there is no oversight of these private and philanthropic

532
00:29:49,080 --> 00:29:53,719
Speaker 1: partnerships between technology creators and law enforcement agencies. Which Thorn

533
00:29:53,960 --> 00:29:57,000
Speaker 1: is a nonprofit, It is a philanthropic effort, and so

534
00:29:57,200 --> 00:29:59,840
Speaker 1: there is not a lot of oversight into how the

535
00:30:00,360 --> 00:30:03,800
Speaker 1: partners with law enforcement. You know, a public entity of

536
00:30:03,840 --> 00:30:08,360
Speaker 1: the state so we already know that Spotlight is using

537
00:30:08,480 --> 00:30:12,720
Speaker 1: facial recognition technology that might be less than effective. That

538
00:30:12,840 --> 00:30:16,440
Speaker 1: is because Spotlight uses a tool by Amazon called Recognition.

539
00:30:16,920 --> 00:30:19,080
Speaker 1: It's recognition with a K. So if you're googling this

540
00:30:19,200 --> 00:30:21,720
Speaker 1: to find more information, they're spelling it like edgy.

541
00:30:21,800 --> 00:30:23,479
Speaker 2: It's not recognition with a C, is with OK.

542
00:30:24,000 --> 00:30:28,520
Speaker 3: Was it created by like nineteen nineties socialists.

543
00:30:29,520 --> 00:30:33,640
Speaker 1: It's like the same branding as like really edgy energy

544
00:30:33,720 --> 00:30:38,200
Speaker 1: drinks or something. I was as they didn't throw an

545
00:30:38,400 --> 00:30:40,080
Speaker 1: X in there, you know, like we gotta get that

546
00:30:40,120 --> 00:30:44,960
Speaker 1: like edgy X branding. So Recognition Amazon's facial recognition technology

547
00:30:45,040 --> 00:30:48,160
Speaker 1: that is used by Ashton Cutcher's anti trafficking tools Spotlight

548
00:30:48,480 --> 00:30:51,920
Speaker 1: to text spaces in images and video. There was a

549
00:30:52,480 --> 00:30:56,920
Speaker 1: very very long, messy battle to pressure Amazon to stop

550
00:30:57,040 --> 00:31:00,440
Speaker 1: selling this technology that is used by Thorn to lawn enforcement.

551
00:31:00,680 --> 00:31:03,560
Speaker 1: I'll give you like a little like quick and dirty

552
00:31:03,640 --> 00:31:07,520
Speaker 1: history of what happened there. So that is because Amazon's

553
00:31:07,520 --> 00:31:12,280
Speaker 1: recognition is janki as fuck, especially when it comes to

554
00:31:12,440 --> 00:31:17,160
Speaker 1: marginalized people. Two researchers, doctors Joy Boloweni and Debra Vagee,

555
00:31:17,520 --> 00:31:21,200
Speaker 1: published a paper demonstrating that Recognition was classifying the gender

556
00:31:21,280 --> 00:31:24,440
Speaker 1: of dark skinned women thirty one point four percentage points

557
00:31:24,520 --> 00:31:28,320
Speaker 1: less accurately than that of light skinned men. Amazon responded

558
00:31:28,520 --> 00:31:32,040
Speaker 1: not by acknowledging these results, but by attacking the credibility

559
00:31:32,120 --> 00:31:36,000
Speaker 1: of these black women researchers. Amazon published two blog posts

560
00:31:36,040 --> 00:31:40,080
Speaker 1: claiming that Raji and Bolomwini's work was misleading. In response,

561
00:31:40,240 --> 00:31:44,480
Speaker 1: nearly eighty AI researchers defended their work and yet again

562
00:31:44,680 --> 00:31:48,040
Speaker 1: called for Amazon to stop selling face recognition technology to

563
00:31:48,080 --> 00:31:51,959
Speaker 1: the police. In twenty twenty, after the death of George Floyd,

564
00:31:52,280 --> 00:31:55,320
Speaker 1: House and Senate Democrats introduced a police reform bill that

565
00:31:55,440 --> 00:31:58,760
Speaker 1: included a proposal to limit facial recognition in law enforcement,

566
00:31:59,240 --> 00:32:03,200
Speaker 1: marking the flow largest federal effort ever to regulate this technology.

567
00:32:03,400 --> 00:32:07,240
Speaker 1: So in July, after IBM was like, we're not using

568
00:32:07,240 --> 00:32:10,680
Speaker 1: this tech anymore, the ACLU of Northern California conducted its

569
00:32:10,720 --> 00:32:14,160
Speaker 1: own study and found that the system falsely matched photos

570
00:32:14,160 --> 00:32:17,160
Speaker 1: of twenty eight members of the US Congress with mugshots,

571
00:32:17,520 --> 00:32:22,200
Speaker 1: which really got Congress's attention. The false matches were disproportionately

572
00:32:22,320 --> 00:32:23,200
Speaker 1: people of color.

573
00:32:23,520 --> 00:32:24,680
Speaker 2: So after all of this.

574
00:32:24,680 --> 00:32:29,080
Speaker 1: Outcry, Amazon did announce a one year moratorium on partnering

575
00:32:29,080 --> 00:32:33,080
Speaker 1: with law enforcement to use recognition, with one exception for

576
00:32:33,200 --> 00:32:37,040
Speaker 1: when police are using Thorn's spotlight. So they know that

577
00:32:37,080 --> 00:32:41,680
Speaker 1: the facial recognition technology used in spotlight is not great.

578
00:32:41,920 --> 00:32:44,880
Speaker 1: It's racially biased, this has gender biased. They know all

579
00:32:44,920 --> 00:32:47,520
Speaker 1: of this, so they're going to stop using that technology

580
00:32:48,040 --> 00:32:53,280
Speaker 1: unless it is being used for police to combat trafficking

581
00:32:53,640 --> 00:32:54,920
Speaker 1: via spotlight.

582
00:32:55,360 --> 00:32:58,800
Speaker 2: So this technology, if it is being used.

583
00:32:58,680 --> 00:33:02,520
Speaker 1: Purportedly to identify people who are potentially involved in exchanging

584
00:33:02,600 --> 00:33:07,160
Speaker 1: sex for money, but it's also really janky and misidentifies

585
00:33:07,200 --> 00:33:09,680
Speaker 1: the black and brown face as women's faces, you can

586
00:33:09,920 --> 00:33:12,440
Speaker 1: see why that would be a problem, right.

587
00:33:12,800 --> 00:33:18,520
Speaker 3: Yeah, for sure, And like, how do you justify discontinuing

588
00:33:18,560 --> 00:33:22,880
Speaker 3: the technology because it doesn't work well, but we're gonna

589
00:33:22,920 --> 00:33:26,040
Speaker 3: keep using it for this one purpose, Like it just

590
00:33:26,080 --> 00:33:27,880
Speaker 3: doesn't even begin to make sense.

591
00:33:28,720 --> 00:33:33,120
Speaker 1: I completely agree. And remember, despite Thorn's state admission of

592
00:33:33,160 --> 00:33:37,160
Speaker 1: helping kids who are being trafficked, they have conflated children

593
00:33:37,240 --> 00:33:41,160
Speaker 1: who are trafficked with adults who engage in consensual sex work.

594
00:33:41,400 --> 00:33:42,800
Speaker 2: Now, Thorn, I need to be.

595
00:33:42,760 --> 00:33:46,800
Speaker 1: Clear, does specifically say that their focus of the organization

596
00:33:47,120 --> 00:33:50,880
Speaker 1: is helping kids who are being trafficked, not adults who

597
00:33:50,880 --> 00:33:53,880
Speaker 1: engage in sex work. And that might be true at

598
00:33:53,920 --> 00:33:56,760
Speaker 1: an organizational level, but that is not what's going on

599
00:33:56,800 --> 00:34:01,640
Speaker 1: in practice. Thorn's CEO Julia Cordua told Forbes that Spotlight

600
00:34:01,720 --> 00:34:05,400
Speaker 1: was limited to officers who investigate child sex trafficking, saying

601
00:34:05,800 --> 00:34:08,960
Speaker 1: law enforcement has reported that with Spotlight they have seen

602
00:34:09,000 --> 00:34:12,600
Speaker 1: over sixty percent time saving in their investigative process, and

603
00:34:12,640 --> 00:34:15,719
Speaker 1: over twenty one thousand children have been identified using this tool.

604
00:34:16,160 --> 00:34:19,239
Speaker 1: Court filings and interviews with current and former police show

605
00:34:19,280 --> 00:34:23,040
Speaker 1: that Spotlight is frequently used to identify adults, a point

606
00:34:23,080 --> 00:34:26,640
Speaker 1: on which Cordia declined to comment. And this is what

607
00:34:26,719 --> 00:34:29,480
Speaker 1: I think is really going on at THORN. They can

608
00:34:29,520 --> 00:34:32,440
Speaker 1: say all day long that they do not exist to

609
00:34:32,520 --> 00:34:37,040
Speaker 1: target adult consensual sex workers, but their technology just is

610
00:34:37,120 --> 00:34:40,680
Speaker 1: being used to target those adult consensual sex workers who

611
00:34:40,760 --> 00:34:43,000
Speaker 1: they say they don't want to target. And this is

612
00:34:43,040 --> 00:34:45,759
Speaker 1: not just messed up because it's wrong to surveil and

613
00:34:45,800 --> 00:34:49,680
Speaker 1: criminalize adults who consensually engage in sex work, which it is,

614
00:34:50,000 --> 00:34:54,160
Speaker 1: but also adult sex workers have networks of support and

615
00:34:54,280 --> 00:34:57,399
Speaker 1: advocacy where they can help detect and prevent children from

616
00:34:57,440 --> 00:35:00,719
Speaker 1: being trafficked for sex. But sex workers not be a

617
00:35:00,800 --> 00:35:04,839
Speaker 1: resource to help combat trafficking if sex work is further

618
00:35:05,000 --> 00:35:08,319
Speaker 1: surveiled and criminalized. Like time and time again, we see

619
00:35:08,360 --> 00:35:11,640
Speaker 1: that sex workers are on the front line of seeing

620
00:35:11,680 --> 00:35:15,360
Speaker 1: when something non consensual or something is going on with

621
00:35:15,400 --> 00:35:18,680
Speaker 1: a minor, and they're the ones who frequently work with

622
00:35:18,800 --> 00:35:21,600
Speaker 1: law enforcement to help curb that. But they're not going

623
00:35:21,640 --> 00:35:24,080
Speaker 1: to be able to do that if they are continually

624
00:35:24,120 --> 00:35:28,120
Speaker 1: being surveiled and criminalized. Not only that, laws that surveil

625
00:35:28,120 --> 00:35:30,839
Speaker 1: and criminalize sex workers also make it harder for them

626
00:35:30,880 --> 00:35:33,840
Speaker 1: to do their work safely. It shuts down resources and

627
00:35:33,920 --> 00:35:37,359
Speaker 1: services used to research and vet clients. And so if

628
00:35:37,360 --> 00:35:41,000
Speaker 1: your whole thing is we want to prevent people from

629
00:35:41,040 --> 00:35:45,319
Speaker 1: being trafficked for sex, don't then make it harder to

630
00:35:45,400 --> 00:35:48,080
Speaker 1: engage in sex work, because actually you are going to

631
00:35:48,760 --> 00:35:52,560
Speaker 1: make it easier for people to be harmed and trafficked

632
00:35:52,640 --> 00:35:56,759
Speaker 1: and caught up in non consensual situations. Kate Diamato, a

633
00:35:56,800 --> 00:35:59,600
Speaker 1: sex worker advocate at Reframe Health and Justice, a queer

634
00:35:59,640 --> 00:36:02,240
Speaker 1: and trans people of color collective focused on harm reduction

635
00:36:02,320 --> 00:36:05,640
Speaker 1: and legal reform, said that by monitoring sex workers rather

636
00:36:05,680 --> 00:36:08,680
Speaker 1: than working with them, police are making it incredibly hard

637
00:36:08,719 --> 00:36:11,920
Speaker 1: to do sex work in any way. That is remotely safe.

638
00:36:12,080 --> 00:36:14,360
Speaker 1: And even if Thorn says that they do not want

639
00:36:14,400 --> 00:36:18,120
Speaker 1: to target adults who are consensually engaging against sex work,

640
00:36:18,520 --> 00:36:21,080
Speaker 1: it kind of sounds like they are including those same

641
00:36:21,120 --> 00:36:24,600
Speaker 1: adults in the numbers of trafficking victims that they report

642
00:36:24,640 --> 00:36:28,960
Speaker 1: that they have saved in scare quotes from trafficking, because

643
00:36:29,360 --> 00:36:34,360
Speaker 1: just like the previous iteration of Thorn DNA, they still

644
00:36:34,400 --> 00:36:38,799
Speaker 1: are kind of misrepresenting their numbers. Ashton Kutcher gave testimony

645
00:36:38,800 --> 00:36:42,000
Speaker 1: before Senator John McCain and other US lawmakers where he

646
00:36:42,080 --> 00:36:45,120
Speaker 1: said that Spotlight had helped save more than six thousand

647
00:36:45,320 --> 00:36:48,880
Speaker 1: US sex trafficking victims, including two thousand miners, in the

648
00:36:48,960 --> 00:36:49,840
Speaker 1: last twelve months.

649
00:36:50,000 --> 00:36:50,799
Speaker 2: But according to a.

650
00:36:50,840 --> 00:36:54,719
Speaker 1: Report from Reason, these numbers wildly outpace the average number

651
00:36:54,800 --> 00:36:58,200
Speaker 1: of new criminal investigations into sex trafficking open in the

652
00:36:58,320 --> 00:37:01,680
Speaker 1: US each year, or average number of victims identified by

653
00:37:01,760 --> 00:37:04,840
Speaker 1: US law enforcement. For instance, between late two thousand and

654
00:37:04,880 --> 00:37:08,160
Speaker 1: nine and late twenty fifteen, FBI agents working with state

655
00:37:08,160 --> 00:37:11,200
Speaker 1: and local police across America identified an average of just

656
00:37:11,239 --> 00:37:14,800
Speaker 1: one hundred and seventy five minor victims per year. According

657
00:37:14,840 --> 00:37:17,880
Speaker 1: to the Attorney Generals twenty fifteen annual Report to Congress,

658
00:37:18,000 --> 00:37:20,960
Speaker 1: an assessment of the US government's activities to combat trafficking

659
00:37:21,040 --> 00:37:24,560
Speaker 1: in persons. The report also notes that in government fiscal

660
00:37:24,600 --> 00:37:27,840
Speaker 1: year twenty fifteen, the FBI identified around six hundred and

661
00:37:27,880 --> 00:37:31,280
Speaker 1: seventy two adult and child victims of sex or labor trafficking.

662
00:37:31,600 --> 00:37:35,239
Speaker 1: The FBI opened eight hundred and two human trafficking investigations,

663
00:37:35,239 --> 00:37:37,759
Speaker 1: resulting in four hundred and fifty three convictions that year,

664
00:37:38,120 --> 00:37:41,640
Speaker 1: while ICE opened one thousand, thirty four sex or labor

665
00:37:41,719 --> 00:37:45,960
Speaker 1: trafficking investigations and got fifty one sex trafficking convictions. In

666
00:37:45,960 --> 00:37:49,040
Speaker 1: addition to that, uniform crime reporting data from the United

667
00:37:49,040 --> 00:37:52,279
Speaker 1: States indicate that seven hundred and forty four investigations into

668
00:37:52,280 --> 00:37:55,280
Speaker 1: state level sex trafficking offenses were opened in twenty fifteen,

669
00:37:55,600 --> 00:38:00,160
Speaker 1: so there is probably overlap between state and FBI investigation,

670
00:38:00,560 --> 00:38:03,319
Speaker 1: But the report says, even if we count all of

671
00:38:03,320 --> 00:38:06,640
Speaker 1: those cases separately, we're looking at a total of two thousand,

672
00:38:06,880 --> 00:38:10,400
Speaker 1: five hundred and eighty investigations into sex or labor trafficking,

673
00:38:11,360 --> 00:38:15,239
Speaker 1: seven hundred and twenty five less cases than Thorn allegedly

674
00:38:15,280 --> 00:38:17,760
Speaker 1: helped identify in a one year period.

675
00:38:18,080 --> 00:38:21,560
Speaker 3: Yeah, I mean, based on those disparaities you just cited,

676
00:38:21,920 --> 00:38:26,080
Speaker 3: and their history of playing fast and loose with numbers,

677
00:38:27,920 --> 00:38:30,359
Speaker 3: one kind of has to suspect that, like, maybe they're

678
00:38:30,360 --> 00:38:32,560
Speaker 3: playing fast and loose with their numbers here. Do you

679
00:38:32,560 --> 00:38:34,040
Speaker 3: suppose it helps with their fundraising?

680
00:38:34,320 --> 00:38:36,640
Speaker 2: Oh, I can imagine that it might.

681
00:38:36,920 --> 00:38:39,719
Speaker 1: Basically, according to this report, it looks like Thorn is

682
00:38:39,760 --> 00:38:43,319
Speaker 1: inflating the numbers of trafficking arrests that their technology has

683
00:38:43,400 --> 00:38:46,919
Speaker 1: led to, and the most likely explanation for how they're

684
00:38:47,000 --> 00:38:48,960
Speaker 1: kind of being funny with the numbers is that they

685
00:38:49,000 --> 00:38:52,920
Speaker 1: are counting adults arrested for posting ads on places like

686
00:38:53,000 --> 00:38:56,960
Speaker 1: Craigslist or Backpage or other escort sites looking to engage

687
00:38:57,000 --> 00:38:59,520
Speaker 1: in the exchange of sex for money in the number

688
00:38:59,520 --> 00:39:02,520
Speaker 1: of people that their technology has saved from trafficking, whether

689
00:39:02,520 --> 00:39:05,480
Speaker 1: they were actually being trafficked or not. So back to

690
00:39:05,520 --> 00:39:08,880
Speaker 1: that reason report reason rights, the majority of adult ads

691
00:39:08,920 --> 00:39:12,040
Speaker 1: on Backpage are posted by sex workers themselves and people

692
00:39:12,120 --> 00:39:15,920
Speaker 1: arrested in cops human trafficking stings based on these ads

693
00:39:15,960 --> 00:39:18,919
Speaker 1: are predominantly sex workers and or people looking to pay

694
00:39:18,960 --> 00:39:22,040
Speaker 1: other adults for sex. Police might be looking for trafficking

695
00:39:22,160 --> 00:39:25,360
Speaker 1: victims when they contact ads featuring young looking women or

696
00:39:25,400 --> 00:39:28,879
Speaker 1: certain supposed code words, but when their hunches don't pan out,

697
00:39:29,000 --> 00:39:31,760
Speaker 1: and this is most of the time, they arrest the target.

698
00:39:32,200 --> 00:39:34,800
Speaker 1: Considering the data we do have on state and federal

699
00:39:34,840 --> 00:39:37,920
Speaker 1: human trafficking cases, the only way the numbers from Ashton

700
00:39:37,960 --> 00:39:41,280
Speaker 1: Kutcher's group could make sense is if a they're counting

701
00:39:41,360 --> 00:39:45,160
Speaker 1: every red flag ad Spotlight identifies, regardless of whether these

702
00:39:45,200 --> 00:39:49,200
Speaker 1: tips are ultimately deemed worthwhild enough to prompt a criminal investigation,

703
00:39:49,760 --> 00:39:53,760
Speaker 1: or B they're counting cases of consensual sex work between

704
00:39:53,760 --> 00:39:58,280
Speaker 1: adults and lumping all adult sex workers identified into adult

705
00:39:58,440 --> 00:40:04,120
Speaker 1: trafficking victim number, and in some way they are doing

706
00:40:04,160 --> 00:40:09,799
Speaker 1: something with their numbers. Either a anytime a law enforcement

707
00:40:09,960 --> 00:40:12,960
Speaker 1: looks into one of these ads, they're counting that as

708
00:40:13,000 --> 00:40:15,920
Speaker 1: somebody who has been saved from trafficking, or they are

709
00:40:15,960 --> 00:40:21,400
Speaker 1: just lumping in consensual adult sex workers as adult trafficking victims.

710
00:40:21,760 --> 00:40:24,440
Speaker 1: Either way, it's not great, and the fact that we

711
00:40:24,480 --> 00:40:27,000
Speaker 1: can't answer this question, the fact that these numbers don't

712
00:40:27,000 --> 00:40:29,319
Speaker 1: really make sense, and like they have not provided an

713
00:40:29,320 --> 00:40:31,359
Speaker 1: explanation as to how they've gotten these numbers and why

714
00:40:31,400 --> 00:40:34,560
Speaker 1: they're repeating them publicly, is a problem, and I want

715
00:40:34,560 --> 00:40:37,520
Speaker 1: to be very very clear that I am not saying

716
00:40:37,560 --> 00:40:40,399
Speaker 1: that thorn does not do any good. That is far

717
00:40:40,440 --> 00:40:42,800
Speaker 1: from what I am saying. I hope that nobody takes

718
00:40:42,840 --> 00:40:46,120
Speaker 1: that away. But what I am saying is that I

719
00:40:46,160 --> 00:40:48,600
Speaker 1: think that we need to push back against this dynamic

720
00:40:48,680 --> 00:40:52,279
Speaker 1: that says that all anti trafficking work is just by

721
00:40:52,400 --> 00:40:57,960
Speaker 1: nature above reproach, because trafficking and misrepresentations around trafficking has

722
00:40:58,000 --> 00:41:01,080
Speaker 1: been used to advocate for all kinds of harmful legislation

723
00:41:01,560 --> 00:41:05,880
Speaker 1: and surveillance that does not actually curb trafficking. That's because

724
00:41:05,880 --> 00:41:09,719
Speaker 1: these campaigns are often about curbing sex work, not trafficking.

725
00:41:10,040 --> 00:41:13,920
Speaker 1: After SESTA FOSTA was passed, trafficking actually increased.

726
00:41:14,320 --> 00:41:15,200
Speaker 2: An in depth.

727
00:41:15,000 --> 00:41:17,880
Speaker 1: Legal analysis of SESTA FOSTA and its impact published in

728
00:41:17,880 --> 00:41:20,719
Speaker 1: the Columbia Human Rights Law Review concluded that the law

729
00:41:20,760 --> 00:41:23,520
Speaker 1: has had a chilling effect on free speech, has created

730
00:41:23,640 --> 00:41:26,879
Speaker 1: dangerous working conditions for sex workers, and has actually made

731
00:41:26,920 --> 00:41:30,120
Speaker 1: it more difficult for police to find trafficked individuals.

732
00:41:30,360 --> 00:41:32,920
Speaker 2: But when you say, like, oh.

733
00:41:32,680 --> 00:41:36,720
Speaker 1: I'm doing an organization that's going to combat trafficking, people listen,

734
00:41:37,040 --> 00:41:40,799
Speaker 1: People open their checkbooks, people fund you, because everybody is

735
00:41:40,840 --> 00:41:44,160
Speaker 1: against trafficking. But if you were to say, oh, my

736
00:41:44,320 --> 00:41:49,600
Speaker 1: organization is about eradicating sex work through surveilling and criminalizing

737
00:41:49,640 --> 00:41:52,520
Speaker 1: people who engage in it, it would probably be a

738
00:41:52,520 --> 00:41:56,960
Speaker 1: lot less effective, and people like Ashton Kutcher continue to

739
00:41:57,000 --> 00:42:01,080
Speaker 1: be the people who are funding and profiting from tech futures.

740
00:42:01,400 --> 00:42:05,440
Speaker 1: So maybe it starts with surveilling and criminalizing sex workers,

741
00:42:05,600 --> 00:42:09,320
Speaker 1: but it certainly does not stop there. Here's Ashton Kutcher

742
00:42:09,440 --> 00:42:12,200
Speaker 1: on a recent episode of the podcast Hot Ones. If

743
00:42:12,200 --> 00:42:13,960
Speaker 1: he sounds a little funky, it's because he's eating a

744
00:42:13,960 --> 00:42:16,120
Speaker 1: hot wing, because like that's the point of the podcast.

745
00:42:16,680 --> 00:42:18,640
Speaker 2: Like, I have an app in my phone in my

746
00:42:18,719 --> 00:42:19,399
Speaker 2: pocket right now.

747
00:42:19,400 --> 00:42:20,240
Speaker 4: It's like a big app.

748
00:42:21,040 --> 00:42:22,359
Speaker 3: It's a facial recognition app.

749
00:42:22,640 --> 00:42:24,920
Speaker 4: I can hold it up to anybody's face here and

750
00:42:25,040 --> 00:42:29,680
Speaker 4: like find exactly who you are, what Internet accounts are on,

751
00:42:29,880 --> 00:42:30,640
Speaker 4: what they look like.

752
00:42:31,680 --> 00:42:32,360
Speaker 2: That's terrifying.

753
00:42:33,160 --> 00:42:36,960
Speaker 1: So this whole conversation provides what I think is a

754
00:42:37,000 --> 00:42:41,799
Speaker 1: pretty good blueprint for how internet legislation can ostensibly be

755
00:42:42,000 --> 00:42:46,040
Speaker 1: about combating a very narrow, kind of reasonable sounding kind

756
00:42:46,040 --> 00:42:48,880
Speaker 1: of harm, particularly a harm against kids, but then that

757
00:42:48,960 --> 00:42:52,840
Speaker 1: definition is expanded such that more and more things are included,

758
00:42:53,320 --> 00:42:55,440
Speaker 1: the same way that when we talk about legislation like

759
00:42:55,480 --> 00:42:58,239
Speaker 1: the Kids Online Safety Act, where the legislation says that

760
00:42:58,280 --> 00:43:01,160
Speaker 1: it's about protecting kids from harmful one content, but then

761
00:43:01,200 --> 00:43:04,200
Speaker 1: the definition of that harmful online content is just expanded

762
00:43:04,640 --> 00:43:08,680
Speaker 1: to include all different kinds of content, both for miners

763
00:43:08,880 --> 00:43:11,239
Speaker 1: and adults. And so I say all of this to

764
00:43:11,280 --> 00:43:15,799
Speaker 1: say that when you hear that somebody is trying to

765
00:43:16,360 --> 00:43:20,000
Speaker 1: combat harm of kids on the internet, it should not

766
00:43:20,200 --> 00:43:22,680
Speaker 1: just be a sign of like, oh, that's a good person,

767
00:43:22,880 --> 00:43:25,480
Speaker 1: like we don't need to look at what that actually involves.

768
00:43:25,840 --> 00:43:28,120
Speaker 1: It should actually be the opposite. We should actually look

769
00:43:28,200 --> 00:43:31,640
Speaker 1: deeper into their actual framework, how they're actually.

770
00:43:31,320 --> 00:43:32,160
Speaker 2: Thinking about this.

771
00:43:32,320 --> 00:43:35,120
Speaker 1: Is it punitive, is it surveillance base, Is it based

772
00:43:35,160 --> 00:43:39,160
Speaker 1: around further criminalizing people who are already surveiled and criminalized

773
00:43:39,200 --> 00:43:41,920
Speaker 1: and marginalized. So if you're listening to this and you're

774
00:43:41,960 --> 00:43:45,440
Speaker 1: wondering what exactly is the big deal? So what if

775
00:43:45,440 --> 00:43:49,320
Speaker 1: they inflate their numbers? Here's why this matters. Inflating the

776
00:43:49,400 --> 00:43:53,719
Speaker 1: danger around sex trafficking not only lends credibility to specific

777
00:43:53,880 --> 00:43:57,799
Speaker 1: harmful legislation, legislation like cesta fasta that can actually make

778
00:43:57,840 --> 00:44:01,080
Speaker 1: trafficking worse, but it all also makes it seem like

779
00:44:01,160 --> 00:44:04,760
Speaker 1: sex trafficking is more likely to be a situation where

780
00:44:05,120 --> 00:44:08,040
Speaker 1: a white child is like snatched off the street by

781
00:44:08,080 --> 00:44:12,080
Speaker 1: a stranger and forced into the sex trade. Now, this

782
00:44:12,239 --> 00:44:15,759
Speaker 1: is obviously a very sympathetic narrative and one that is

783
00:44:15,880 --> 00:44:18,799
Speaker 1: very easy to throw money and legislation at. But if

784
00:44:18,880 --> 00:44:22,800
Speaker 1: trafficking and sex crimes begin and end with strangers snatching

785
00:44:22,880 --> 00:44:25,560
Speaker 1: kids off the street and forcing them into the sex trade,

786
00:44:25,840 --> 00:44:28,000
Speaker 1: you are going to be trained that that is the

787
00:44:28,120 --> 00:44:31,919
Speaker 1: real threat, when in reality it's not. It is much

788
00:44:32,000 --> 00:44:35,040
Speaker 1: more likely that the threat is someone the victim knows,

789
00:44:35,719 --> 00:44:38,680
Speaker 1: or that they are groomed or coerced into being trafficked.

790
00:44:39,040 --> 00:44:42,200
Speaker 1: But if trafficking is your end all, be all for

791
00:44:42,400 --> 00:44:45,080
Speaker 1: sex crimes, you are going to be a lot less

792
00:44:45,160 --> 00:44:48,200
Speaker 1: likely to see your buddy who has always been really

793
00:44:48,280 --> 00:44:50,839
Speaker 1: nice to you, who seems like a really good guy

794
00:44:50,880 --> 00:44:53,760
Speaker 1: and always shakes hands with firefighters after nine to eleven

795
00:44:54,080 --> 00:44:57,360
Speaker 1: as a threat, because in your mind, the real threat

796
00:44:57,719 --> 00:45:01,520
Speaker 1: is strangers. So when we inflate the numbers around trafficking

797
00:45:01,800 --> 00:45:04,960
Speaker 1: and use that to justify all kinds of harmful practices

798
00:45:05,000 --> 00:45:09,120
Speaker 1: and laws, we are not actually keeping ourselves safe. And

799
00:45:09,200 --> 00:45:12,920
Speaker 1: so yeah, I think as people are talking about Ashton

800
00:45:13,000 --> 00:45:16,880
Speaker 1: Kutcher and why he would risk his reputation and his

801
00:45:17,000 --> 00:45:22,200
Speaker 1: work in the space to defend Danny Masterson from his

802
00:45:22,560 --> 00:45:25,760
Speaker 1: two forcible rape convictions. I hope this is a helpful

803
00:45:25,760 --> 00:45:28,879
Speaker 1: explanation as to why he might do that. And also

804
00:45:29,120 --> 00:45:31,759
Speaker 1: I hope this is a good invitation for folks to

805
00:45:31,960 --> 00:45:34,680
Speaker 1: listen to marginalize people when they speak up about what

806
00:45:34,719 --> 00:45:38,000
Speaker 1: they're experiencing online, because pretty much everything that I just

807
00:45:38,040 --> 00:45:40,920
Speaker 1: said I got from sex workers. Sex workers have not

808
00:45:41,040 --> 00:45:43,520
Speaker 1: been quiet about this. They have been speaking up about

809
00:45:43,680 --> 00:45:46,560
Speaker 1: harmful legislation on the Internet for a very long time,

810
00:45:46,960 --> 00:45:49,120
Speaker 1: and I think we really got to listen to them.

811
00:45:49,520 --> 00:45:50,640
Speaker 2: And that's it. That's all I got.

812
00:45:51,080 --> 00:45:53,400
Speaker 3: I learned a lot here. I had no idea Ashton

813
00:45:53,440 --> 00:45:56,480
Speaker 3: Kutcher was doing any of this. What a weird story.

814
00:45:57,440 --> 00:45:59,600
Speaker 2: Yeah, I mean, let me just like before I let

815
00:45:59,600 --> 00:45:59,920
Speaker 2: you go.

816
00:46:00,000 --> 00:46:01,600
Speaker 1: So, I just want to look up all the different

817
00:46:02,080 --> 00:46:05,400
Speaker 1: tech companies that he is investing in, because he was

818
00:46:05,440 --> 00:46:08,400
Speaker 1: an investor at Uber. He was tweeting about how like

819
00:46:09,320 --> 00:46:13,920
Speaker 1: it's good for tech companies like Uber, presumably tech companies

820
00:46:13,920 --> 00:46:17,720
Speaker 1: that he invests in. It's good when when they track

821
00:46:17,840 --> 00:46:22,000
Speaker 1: down dirt on journalists who are reporting on on those companies,

822
00:46:22,320 --> 00:46:24,200
Speaker 1: and then later he was like, oh no, no, no, no,

823
00:46:24,400 --> 00:46:26,719
Speaker 1: I was just speaking as myself. I wasn't speaking as

824
00:46:26,800 --> 00:46:31,920
Speaker 1: like an uber investor. I was just that's just my opinion.

825
00:46:32,600 --> 00:46:39,040
Speaker 3: There's like a real paternalistic through thread of I'm going

826
00:46:39,080 --> 00:46:45,840
Speaker 3: to start this charity named after myself to protect girls

827
00:46:45,840 --> 00:46:51,000
Speaker 3: from trafficking, and also I'm going to make a lot

828
00:46:51,040 --> 00:46:55,960
Speaker 3: of money through investing in surveillance software. It's probably not

829
00:46:56,000 --> 00:46:59,040
Speaker 3: a coincidence that both of those things are happening here.

830
00:46:59,600 --> 00:47:03,319
Speaker 1: Oh yeah, I And that's sort of like one of

831
00:47:03,320 --> 00:47:04,920
Speaker 1: the reasons that it was important to me to talk

832
00:47:04,920 --> 00:47:09,680
Speaker 1: about this is that Ashton Cutcher is propping up, profiting

833
00:47:10,160 --> 00:47:14,520
Speaker 1: from and funding a vast surveillance network and then like

834
00:47:14,920 --> 00:47:18,080
Speaker 1: going on podcasts and like going on hot ones and

835
00:47:18,200 --> 00:47:21,239
Speaker 1: like making it seem like it's cool and funny, and

836
00:47:21,280 --> 00:47:23,319
Speaker 1: I just I hate the idea that he has so

837
00:47:23,400 --> 00:47:27,520
Speaker 1: effectively been able to brand himself as like a smart

838
00:47:27,600 --> 00:47:31,560
Speaker 1: tech guy who will go on TV and sell our

839
00:47:31,640 --> 00:47:34,120
Speaker 1: own harm back to us in a way that looks

840
00:47:34,280 --> 00:47:37,000
Speaker 1: cool and like, oh my god, he really like, you know,

841
00:47:37,840 --> 00:47:40,759
Speaker 1: he's he's really like doing something interesting. People thought he

842
00:47:40,800 --> 00:47:43,560
Speaker 1: was a dumb stoner, but actually he's making millions off

843
00:47:43,600 --> 00:47:46,520
Speaker 1: of me. I mean, just a few months ago, Ashton

844
00:47:46,560 --> 00:47:50,680
Speaker 1: Kutcher launched a two hundred and forty million dollar AI

845
00:47:51,000 --> 00:47:54,480
Speaker 1: investment fund. And so somebody who goes on hot ones

846
00:47:54,960 --> 00:47:59,520
Speaker 1: and giggles and key keys about this like pretty creepy,

847
00:47:59,600 --> 00:48:02,360
Speaker 1: scary surveillance. I don't know that this is somebody that

848
00:48:02,400 --> 00:48:06,759
Speaker 1: I would trust with architecting something like AI. Like it

849
00:48:06,880 --> 00:48:10,359
Speaker 1: just is, it's it's concerning to me, Ashton Kutcher, what are.

850
00:48:10,239 --> 00:48:12,719
Speaker 3: You doing, dude? Where's my privacy?

851
00:48:14,840 --> 00:48:17,239
Speaker 2: That is a great place to end. Mike, thank you

852
00:48:17,280 --> 00:48:18,000
Speaker 2: so much for being.

853
00:48:17,960 --> 00:48:20,960
Speaker 3: Here, Thank you for having me, Bridget, and thanks for

854
00:48:21,120 --> 00:48:27,880
Speaker 3: this deep background about Ashton Kutcher and surveillance capitalism.

855
00:48:27,920 --> 00:48:34,240
Speaker 1: To save the Kids, got a story about an interesting

856
00:48:34,280 --> 00:48:36,319
Speaker 1: thing in tech. I just want to say Hi. You

857
00:48:36,320 --> 00:48:38,600
Speaker 1: can reach us at Hello at tengody dot com. You

858
00:48:38,600 --> 00:48:41,480
Speaker 1: can also find transcripts for today's episode at tengody dot com.

859
00:48:41,840 --> 00:48:43,520
Speaker 1: There Are No Girls on the Internet was created by

860
00:48:43,520 --> 00:48:46,920
Speaker 1: Me Bridget Toad. It's a production of iHeartRadio, an unbossed creative.

861
00:48:47,320 --> 00:48:50,279
Speaker 1: Jonathan Strickland is our executive producer. Tarry Harrison is our

862
00:48:50,320 --> 00:48:53,800
Speaker 1: producer and sound engineer. Michael Almato is our contributing producer.

863
00:48:54,080 --> 00:48:56,759
Speaker 1: I'm your host, Bridget Todd. If you want to help

864
00:48:56,840 --> 00:49:00,080
Speaker 1: us grow, rate and review us on Apple Podcasts a

865
00:49:00,120 --> 00:49:03,280
Speaker 1: podcast from iHeartRadio, check out the iHeartRadio app, Apple Podcasts,

866
00:49:03,320 --> 00:49:04,640
Speaker 1: or wherever you get your podcasts.