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Speaker 1: There Are No Girls on the Internet. As a production

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Speaker 1: of iHeartRadio and Unbossed Creative, I'm bridgetat and this is

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Speaker 1: there Are No Girls on the Internet. I'm here as

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Speaker 1: my producer, Mike. Thanks for being.

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Speaker 2: Here, Mike, thanks for having me back.

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Speaker 1: Bridget And here's what you may have missed this week

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Speaker 1: on the Internet. So in stuff, I absolutely fucking hate news.

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Speaker 1: TikTok is a wash with AI generated true crime victim stories.

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Speaker 1: So if you've been on TikTok at all, you've probably

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Speaker 1: seen this new subsection of true crime content where very

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Speaker 1: creepy AI generated children who speak in this like weird

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Speaker 1: robotic baby voice about how they were tortured or murdered

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Speaker 1: is flooding the platform. They purport to show actual murder

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Speaker 1: victims telling their stories. Now, there are adult victims being depicted,

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Speaker 1: including politicians and celebrities, but the ones that feature children

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Speaker 1: are by far more popular and plentiful. These TikTok accounts

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Speaker 1: claim to be honoring in heavy scare quotes the victims

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Speaker 1: and their stories of violence, but Rolling Stone reports that

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Speaker 1: they often get pretty major details wrong, which may even

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Speaker 1: be an intentional attempt to get around TikTok's rules against

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Speaker 1: AI generated deep fakes of famous people EXONERI. Amanda Knox,

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Speaker 1: who we spoke to on There No Girls on the

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Speaker 1: Internet last year, who also has a new podcast about

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Speaker 1: the history of true crime called Blood Money, had a

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Speaker 1: really interesting perspective that she shared on Blue Sky that

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Speaker 1: even though this addition of AI is definitely creepy, this

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Speaker 1: is nothing new. Amanda Knox writes, this isn't a fresh hell.

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Speaker 1: There's a precedent for this dating back over four hundred years.

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Speaker 1: One of the earliest forms of true crime was the

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Speaker 1: printed broadside poster. These posters would tell stories of brutal murders,

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Speaker 1: often murders of children, and these stories would often be

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Speaker 1: told in the ballad form, a song written in the

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Speaker 1: voice of either the condemned criminal or the murdered person.

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Speaker 1: Whoever all these ballots which just make them all up.

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Speaker 1: They would invent the pleading words of the child and

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Speaker 1: the moments before they were killed, or they would convey

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Speaker 1: the thoughts of the criminal as they confessed or acknowledged regret.

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Speaker 1: So these creepy AI generated true crime deep fakes of

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Speaker 1: children may not be a totally new phenomena, but the

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Speaker 1: addition of AI to recreate these child victims definitely makes

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Speaker 1: them much more creepy. Like to be able to tell

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Speaker 1: an AI program to generate the likeness of a murdered

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Speaker 1: child for views on social media is pretty weird if

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Speaker 1: I think it represents this AI presenting the possibility for

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Speaker 1: ever more expanded tech facilitated ghoulishness.

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Speaker 2: Yeah, gross creepy, you know. Amanaaxa as Usual makes a

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Speaker 2: pretty good point that it's not new that people have

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Speaker 2: been using true crime and broadsides for you know, hundreds

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Speaker 2: of years, ever since the invention of the printing press.

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Speaker 2: But I guess one thing that is new here with

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Speaker 2: AI is that historically, I think those like ghoulish tales

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Speaker 2: were often told for some sort of political purpose, either

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Speaker 2: to you know, vilify some savage enemy or heartless empire

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Speaker 2: who committed atrocities on local people, to get the local

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Speaker 2: people like riled up for some sort of political end.

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Speaker 2: But here maybe there's some sort of end.

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Speaker 1: Oh. I think that true crime is always political. I

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Speaker 1: think that you don't tell the story of something ghoulish

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Speaker 1: happening to someone, especially a child or a woman, without

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Speaker 1: there being some kind of whether it's implicit or explicit,

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Speaker 1: political agenda. We've seen time and time again where true

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Speaker 1: crime and the stories of bad things happening to people

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Speaker 1: and people's misfortune is used to strengthen and bolster specific legislation,

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Speaker 1: police crackdowns, more policing, increased criminalization of people of color,

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Speaker 1: and increased surveillance of of color and the poor. I

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Speaker 1: think that true crime stories are often political, even if

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Speaker 1: they don't seem political on their face. I think the

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Speaker 1: fact that these videos Rollingstone reports that they get millions

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Speaker 1: and millions and millions of views and are particularly more

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Speaker 1: popular when they feature the stories of murdered children, that

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Speaker 1: tells me right there that whether or not these videos

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Speaker 1: are explicitly trying to make some sort of political or

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Speaker 1: social argument, they absolutely do fuel political and social you know,

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Speaker 1: dynamics and are in our culture.

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Speaker 2: Yeah, it makes me curious who the perpetrators are in

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Speaker 2: these stories, you know. I think that would be a

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Speaker 2: pretty interesting thing for somebody to look at to get

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Speaker 2: a tack question of what is the political agenda behind

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Speaker 2: this new phenomenon on TikTok.

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Speaker 1: Well, I mean I don't even necessarily think that it's

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Speaker 1: like when I say political, I don't necessarily feel that

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Speaker 1: people are posting these because they're like, oh, this is

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Speaker 1: going to get everybody miled up, and they're going to

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Speaker 1: support X y Z legislation and policing. I'm not saying

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Speaker 1: that sometimes they absolutely can be. We see that a

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Speaker 1: lot when it comes to true crime content about trafficking,

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Speaker 1: that that has then used to support specific legislation. I'm

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Speaker 1: not saying that that's what's going on here, but I

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Speaker 1: am saying that when people are consuming more and more

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Speaker 1: and more content about grizzly murders and torture of children,

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Speaker 1: it is just that's not happening in a vacuum. It's

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Speaker 1: happening in a culture where we already have conversations about policing,

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Speaker 1: about you know, stranger danger, about you know, public safety.

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Speaker 1: And so when I say that they're political, I mean

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Speaker 1: that they feed into a current cultural conversation around those

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Speaker 1: issues that you know, they're not happening. I think it's

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Speaker 1: easy for people who are true crime fans to believe

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Speaker 1: that they're they're consuming this content in a vacuum standalone,

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Speaker 1: like oh, it's just entertainment, I'm just enjoying it. But

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Speaker 1: in fact, it is not happening in a vacuum. It

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Speaker 1: is happening against the backdrop of already existing conversations about

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Speaker 1: things like policing and public safety, which are inherently political.

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Speaker 2: Yeah, that makes a lot of sense, you know. Definitely,

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Speaker 2: one doesn't have to look very far to see ways

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Speaker 2: in which creating a feeling of fear and unease and

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Speaker 2: distrust among the population fits pretty squarely with a lot

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Speaker 2: of political actors social media efforts. Yeah, social media man

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Speaker 2: a powerful thing that plays to our emotions and particularly kids. Huh.

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Speaker 1: It sounds like you're setting me up for our next story, Mike.

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Speaker 1: So I'm gonna go ahead and take that.

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Speaker 2: You know. I'm just trying to do my part over here.

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Speaker 1: Last week, US Surgeon General Doctor Murphy issued an advisory

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Speaker 1: warning about the impact that social media is happening on

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Speaker 1: the mental health of young people. It's a pretty long report,

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Speaker 1: but the crux of it is summed up in this

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Speaker 1: quote from Mrthy. We are in the middle of a

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Speaker 1: national youth mental health crisis, and I am concerned that

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Speaker 1: social media is an important driver of that crisis, one

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Speaker 1: that we must urgently address.

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Speaker 2: So I just want to pause here to emphasize what

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Speaker 2: a big deal this is. The Surgeon General's Office doesn't

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Speaker 2: issue these kind of reports all the time, and when

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Speaker 2: they do, they often have a big impact that can

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Speaker 2: last for decades. Right, We probably all remember or know

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Speaker 2: about the Certain General's Report on Smoking and Health, which

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Speaker 2: really turned the tide on smoking in the US and

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Speaker 2: set us up decades later after a lot of fights,

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Speaker 2: to be in a place where we are now, where

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Speaker 2: fewer Americans smoke now than they have in the past

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Speaker 2: hundred years. So it really sets the agenda for a

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Speaker 2: whole generation of medical and public health initiatives. And it

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Speaker 2: didn't come out of the vacuum either. When it was

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Speaker 2: announced from the Certain General's office, there were supportive comments

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Speaker 2: from leaders of some of the country's largest medical and

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Speaker 2: public health organizations, including the American Academy of Family Physicians,

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Speaker 2: the American Academy of Pediatrics, the American Medical Association, the

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Speaker 2: American Psychiatric Association, American Psychological Association, the American Public Health Association,

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Speaker 2: and the National Parent Teacher Association. Right, like, those are

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Speaker 2: all ormous institutions that have a huge footprint, a huge impact.

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Speaker 2: And so I just mentioned that here to just really

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Speaker 2: underscore the point from the Surgeon General support here that

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Speaker 2: there's widespread consensus acknowledgment in the public health field that

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Speaker 2: social media is causing a lot of harm to our

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Speaker 2: young people and we need to do something about it asap.

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Speaker 1: So the report does say that social media can have

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Speaker 1: benefits to young people and that more researchers needed to

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Speaker 1: really understand the impact. So it is a little bit nuanced,

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Speaker 1: like it's not all bad, but the main takeaways that

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Speaker 1: we need to urgently take action to create safe and

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Speaker 1: healthy digital environments that minimize harm and safeguard children and

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Speaker 1: adolescents mental health and well being during critical stages of development.

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Speaker 1: That's kind of one of the main takeaways of the

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Speaker 1: report is that basically young brains are still cooking and

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Speaker 1: they don't handle social media very well, which like kind

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Speaker 1: of sounds like an obvious point. I always say, like,

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Speaker 1: I'm so glad that I didn't have the social media

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Speaker 1: climate that we have today with so many platforms that

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Speaker 1: I you know what I thirteen, God, I truly God

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Speaker 1: only knows what I would have done and what I

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Speaker 1: would be like if if if I had access to that.

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Speaker 1: But the report says that a highly sensitive period of

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Speaker 1: brain development happens between the ages of ten and nineteen,

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Speaker 1: which coincides when ninety five percent of thirteen to seventeen

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Speaker 1: year olds and nearly forty percent of eight to twelve

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Speaker 1: year olds are using social media. The advisory notes that

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Speaker 1: frequent use of social media platforms can impact brain development,

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Speaker 1: affecting areas associated with emotional learning, impulse control, and social behavior.

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Speaker 1: Murphy has previously said that he believes that even thirteen

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Speaker 1: year old is too early for children to be using

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Speaker 1: social media.

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Speaker 2: So there's two things here worth pulling out of that.

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Speaker 2: Right Like, social media is particularly harmful for children because

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Speaker 2: they are still developing their brains, Like you said, they're

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Speaker 2: still cooking, they're not fully formed, and so it's particularly

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Speaker 2: dangerous to them during this critical period. And also, we

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Speaker 2: have a long tradition in this country and society of

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Speaker 2: protecting young people, right Like, there's general agreement that we

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Speaker 2: should do more to protect young people than adults because adults,

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Speaker 2: they're adults, they're grown, they can make choices for themselves.

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Speaker 2: Children can by definition, And so I just wanted to

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Speaker 2: identify those two different reasons driving this call for more action.

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Speaker 1: So the report does say that social media can be

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Speaker 1: particularly beneficial to marginalize youth. It says studies have shown

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Speaker 1: that social media may support the mental health and well

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Speaker 1: being of lesbian, gay, bisexual, asexual, transgender queer intersects and

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Speaker 1: other youths by enabling peer connection, identity development and management,

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Speaker 1: and social support. This is also really important because I

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Speaker 1: have seen a lot of reporting that suggests that so

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Speaker 1: much of the research about the impacts of kids and

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Speaker 1: social media tends to be done on white kids, right Like,

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Speaker 1: when the researchers are doing that research, they're not necessarily

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Speaker 1: also doing research on all kids. And that is particularly

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Speaker 1: important because, according to a study from the Youth Media

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Speaker 1: and Well Being Research Lab, black and Latino fitth through

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Speaker 1: ninth graders adopt social media at a younger age and

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Speaker 1: their white peers. And because youth of color have such

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Speaker 1: specific online experiences, right Like, these experiences can include things

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Speaker 1: like hate speech and racialized harassment, and like racialized language

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Speaker 1: being used online. And so if you're not researching and

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Speaker 1: looking into the experiences of marginalized kids who are having

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Speaker 1: such specific online experiences, I don't necessarily feel like you're

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Speaker 1: getting a clear picture of the impact.

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Speaker 2: Yeah, totally agree. You know, we in social science we

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Speaker 2: talk about intersectionality and the importance of focusing on not

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Speaker 2: just different demographic groups, but you know, intersections of them.

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Speaker 2: And absolutely there's a lot of reason to think that

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Speaker 2: kids from marginalized backgrounds, specific with you know, who identify

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Speaker 2: with specific marginalized identities, would have a whole host of

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Speaker 2: specific harms and specific benefits that they get from access

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Speaker 2: to social media, and we really do need more research

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Speaker 2: to understand that. But also at the same time, we

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Speaker 2: urgently need restrictions to protect them, right like in this

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Speaker 2: in the US regulatory framework, we are reluctant to ever

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Speaker 2: try to regulate anything until it is solidly, conclusively demonstrated

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Speaker 2: beyond the shadow of a doubt through many lawsuits that like, yes,

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Speaker 2: it does is actually harmful, and so we are actually

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Speaker 2: able to pass some sort of regulation about it. In

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Speaker 2: other countries and in Europe, it's not like that, they're

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Speaker 2: much more willing to acknowledge that something is being harmful

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Speaker 2: and take action. And I really hope that we can

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Speaker 2: do that over here. Well.

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Speaker 1: The report calls for just that. It calls for more

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Speaker 1: research into the impacts of social media usage and for

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Speaker 1: social media companies themselves to be more transparent when it

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Speaker 1: comes to sharing data with outside experts. There's also recommendations

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Speaker 1: for lawmakers to develop stronger health and safety standards for

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Speaker 1: social media products and introduce stricter data privacy controls, which

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Speaker 1: plus plus plus plus plus I'm a who's proponent of,

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Speaker 1: especially for young people, but also for adults. Technology companies themselves, meanwhile,

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Speaker 1: or urge to assess the risk their products might pose

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Speaker 1: and attempt to minimize them. The report also mentioned something

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Speaker 1: that I find pretty important, and that is the role

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Speaker 1: that parents can and should be playing. The report says

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Speaker 1: the onus of mitigating the potential harms on social media

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Speaker 1: should not be placed solely on the shoulders of parents

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Speaker 1: and caregivers, or on children themselves. So this is where

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Speaker 1: I kind of have a lot to say. The people

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Speaker 1: who run social media platforms are making billions of dollars

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Speaker 1: from harming our kids. Parents and caregivers have been burdened

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Speaker 1: so much, especially in the last few years, having to

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Speaker 1: manage virtual learning, shifting workplace policies around remote work. You

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Speaker 1: know you can work from home now you get to

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Speaker 1: come back to the office. Attacks on schools, just to

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Speaker 1: name a few. Tech billionaires know that their technology harms kids.

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Speaker 1: Facebook is aware. We learn that from Francis Hougen's Facebook papers.

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Speaker 1: Facebook cannot say from their own internal reports, Facebook is

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Speaker 1: aware that their product harms kids. End of sentence, full stop.

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Speaker 1: So these tech billionaires know that their product's harm kids,

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Speaker 1: and they make billions of dollars from it from that harm,

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Speaker 1: while doing nothing to meaningfully mitigate how kids are showing

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Speaker 1: up to their platforms, and then to turn around and

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Speaker 1: then further burden parents to keep their kids away from

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Speaker 1: these platforms is unacceptable to me. And it also speaks

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Speaker 1: to the the really extractive relationship that I talked about

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Speaker 1: in the episode that we put out on Tuesday with

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Speaker 1: Paris Marks, where there's just an expectation that it is

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Speaker 1: fine for tech billionaires to do harm to the public,

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Speaker 1: including harm to our kids, if it makes them more money.

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Speaker 1: And it's clear that that extends to harm to some

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Speaker 1: of our most vulnerable children. We need to make it

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Speaker 1: clear that our kids well being is not for sale.

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Speaker 1: We are not sacrificing the health, safety, and wellbeing of

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Speaker 1: our children to help make Mark Zuckerberg more money. And

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Speaker 1: that's really what it comes down to. These companies know

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Speaker 1: what they're doing, they know they are causing harm. You know,

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Speaker 1: I respect this report, But I feel like in places

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Speaker 1: this report makes it seem like tech companies don't already

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Speaker 1: know that their products cause harm, that young people are

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Speaker 1: using them, and that it's causing them a lot of harm.

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Speaker 1: They do know that, and so we did a start

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Speaker 1: from that reality. They know they're making billions of dollars

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Speaker 1: of it, and they're doing nothing to stop it, and

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Speaker 1: that's despicable, and.

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Speaker 2: It's going to take some kind of either regulation or

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Speaker 2: new framework for lawsuits or something to go after them.

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Speaker 2: But you know, they're making a product that demonstrably harms kids. Right,

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Speaker 2: There's not that many categories of products where you can

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Speaker 2: get away with that. I think in the case of

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Speaker 2: social media companies, it's a little bit complicated by free speech,

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Speaker 2: you know, the First Amendment and also Section two thirty,

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Speaker 2: which we've talked about on this podcast, and I wouldn't

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Speaker 2: pretend it's not complicated, but you just can't. We can't

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Speaker 2: continue with this extremely powerful, important product that increasingly people

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Speaker 2: need to be on to interact in society that also

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Speaker 2: is just like harming people, and all of the responsibility

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Speaker 2: is placed on the individuals for not just proticting themselves

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Speaker 2: but their family. It's just it's nonsense and it doesn't work,

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Speaker 2: and it's not working.

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Speaker 1: Well, That's my thing is that parents already have to

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Speaker 1: do so much. You can't just create a harmful thing

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Speaker 1: and then be like, oh, well, just another thing that

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Speaker 1: parents it's up to them to keep this out of

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Speaker 1: their kids' hands, even though it's everywhere, even though that's

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Speaker 1: basically impossible. That's not a reasonable standard for parents too.

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Speaker 1: That's too big of a burden, especially when somebody is

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Speaker 1: people are making money off of it. And so you know,

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Speaker 1: I'm not a parent myself, but I do have a

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Speaker 1: lot of young people in my life. I'm close to

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Speaker 1: a lot of younger folks. I often have said the

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Speaker 1: thing that everybody before they have kids in the twenty

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Speaker 1: the two thousand says, it's like, when I have a child,

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Speaker 1: my child will never look at a scream. It's gonna

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Speaker 1: be books and wooden toys and nothing else. And in

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Speaker 1: twenty twenty three, be for real, right, Like, if if

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Speaker 1: you are listening and you are keeping your kid away

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Speaker 1: from screens, and you are very diligent about screen time,

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Speaker 1: I applaud you. I say, I can't tell you how

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Speaker 1: many times I have smugly told somebody that that is

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Speaker 1: the kind of parent I intend to be if in

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Speaker 1: when I have a child. But it's not a realistic standard, right,

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Speaker 1: like and allowing your kids access to social Like, kids

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Speaker 1: need to be online and they need to learn how

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Speaker 1: to have safe online experiences. I think the owners should

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Speaker 1: be on social media companies to not have platforms be

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Speaker 1: marketplaces for their pain and instead be places where they

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Speaker 1: can explore the internet safely and learn how to come

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Speaker 1: up digital age safely. And yeah, I think, like if

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Speaker 1: you like, it shouldn't just be up to parents who

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Speaker 1: are already burdened with so much to meet this impossible

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Speaker 1: standard of just keeping their kids away from social media platforms.

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Speaker 1: This is not realistic.

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Speaker 2: Yeah, it's not realistic, and it's not it's not an

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Speaker 2: either or thing, right, It's like a false dichotomy. Parents

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Speaker 2: always will ultimately have the ultimate responsibility for protecting their kids.

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Speaker 2: But at the same time, we should have some regulations

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Speaker 2: in place that prevent extremely harmful, dangerous platforms from just

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Speaker 2: being widely available and easily accessible to this kids. If

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Speaker 2: you think about other categories of products that we have

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Speaker 2: special laws to protect kids from tobacco, alcohol, I don't

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Speaker 2: know there's probably some others that I should be able

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Speaker 2: to think of, but I can't. But like, we have

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Speaker 2: laws designed to make those harmful products less easily accessible

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Speaker 2: to young people because there is clear evidence that that

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Speaker 2: is effective at protecting them from it. Why can't we

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Speaker 2: have that with social media?

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Speaker 1: What's interesting is that the Surgeon General says that thirteen

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Speaker 1: is too young to be on social media platforms. If

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Speaker 1: you saw the documentary The Social Dilemma, which is one

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Speaker 1: of my favorite documentaries where its features all of these

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Speaker 1: people who make social media platforms, like engineers, talking about

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Speaker 1: the harm that they know that these platforms have been

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Speaker 1: responsible for. The movie is great. It goes into all

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Speaker 1: these different ways that these platforms cause harm and get

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Speaker 1: you addictated and all of that. At the end, I

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Speaker 1: think it might even be a post credit scene. All

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Speaker 1: of these people who I mean, I hate to say it,

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Speaker 1: but insensibly made money creating these things that they then

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Speaker 1: go on to be like, oh, they're very dangerous, talk

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Speaker 1: about the role that social media plays in their household.

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Speaker 1: None of them let their kids on social media. And

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Speaker 1: these are people who build social media platforms. That really

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Speaker 1: told me something of like, these are the people who

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Speaker 1: built technology, and the technology that they built they don't

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Speaker 1: let their kids have they not allow in their homes

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Speaker 1: or around their children.

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Speaker 2: Yeah, it makes sense. If you're building harmful stuff that

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Speaker 2: you know harms tens of millions of young people, you

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Speaker 2: probably need to sell yourself a story about personal responsibility

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Speaker 2: to absolve yourself of the guilt of knowing that you

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Speaker 2: are pushing this product out into the world and actively

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Speaker 2: trying to get more kids to use it even though

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Speaker 2: it's going to hurt a lot of them.

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Speaker 1: Yeah, it is dirty business. Our kids. I mean, I

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Speaker 1: say this all the time. Our kids experiences should not

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Speaker 1: be for sale. We should not the dynamic where your

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Speaker 1: children are hurt and harmed in real ways so that

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Speaker 1: Mark Zuckerberg can get richer. We need to reject that dynamic.

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Speaker 1: I don't get that. I don't care if I don't

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Speaker 1: care if he goes bankrupt. Our kids experiences should not

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Speaker 1: be used to line the pockets of tech billionaires. And

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Speaker 1: ill guess I'll just leave it there.

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Speaker 3: Let's take a quick break and are back.

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Speaker 2: Are there have there redity stories this past week about

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Speaker 2: something positive happening in tech regulation.

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Speaker 1: I don't know if I would say positive, but there

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Speaker 1: have been some tech regulation news. Let's turn to New

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Speaker 1: York City where they have just passed a new law

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Speaker 1: around AI and hiring. So, in all this talk around

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Speaker 1: AI and jobs and how AI is going to impact

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Speaker 1: all of our jobs, it's clear there is need for

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Speaker 1: some kind of legislation or regulation to regulate how AI

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Speaker 1: impacts the workforce. The New York Times reports at New

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Speaker 1: York City just passed a law that requires companies using

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Speaker 1: AI software and hiring to notify candidates that an automated

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Speaker 1: system is being used. It also requires companies to have

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Speaker 1: independent auditors check the technology annually for bias. Candidates can

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Speaker 1: request and be told what data is being collected and

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Speaker 1: analyzed about them, and companies can be fined for violations.

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Speaker 1: Labor experts actually say that this law in New York

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Speaker 1: City could potentially be expanded nationwide. California, New Jersey, New

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Speaker 1: York and Vermont and DC heyy my hometown are all

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Speaker 1: working on laws to regulate AI and hiring. Now, this

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Speaker 1: sounds like a really positive step in the right direction,

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Speaker 1: but some critics argue that the law is too biased

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Speaker 1: towards businesses and corporate interests and has been watered down

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Speaker 1: to the point of not being effective. What could have

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Speaker 1: been a landmark law was watered down to lose effectiveness.

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Speaker 1: That's from Alexandra Gibvens, the president for the Center for

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Speaker 1: Democracy and Technology, a policy and civil rights organization. That's

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Speaker 1: what you told The New York Times. So you might

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Speaker 1: be wondering, well, what's so wrong with this law? It

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Speaker 1: sounds pretty good. This gets a little in the weeds.

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Speaker 1: But here's how the New York Times breaks down her

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Speaker 1: opposition to this law. They right, The law defines an

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Speaker 1: automated employment decision tool as technology used to substantially assist

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Speaker 1: or replace discretionary decision making. The rule adopted by the

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Speaker 1: city appears to interpret that phrasing narrowly, so that AI

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Speaker 1: software will require an audit only if it is the

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Speaker 1: loan or primary factor and a hiring decision, or if

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Speaker 1: it is used to override a human All of this

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Speaker 1: leaves out the main way that automated software is used,

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Speaker 1: with a human hiring manager invariably making the final choice.

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Speaker 1: The potential for AI driven discrimination typically comes in the

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Speaker 1: screening of hundreds of thousands of candidates down to a handful,

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Speaker 1: or in targeted online recruiting to generate a pool of candidates.

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Speaker 1: So basically what she is saying is that the definition

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Speaker 1: of whether or not AI is being employed in an

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Speaker 1: unemployment decision is so narrow that a company would very

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Speaker 1: rarely ever need to notify a candidate or perform an

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Speaker 1: audit under this law. Basically, they're saying that like they

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Speaker 1: are defining AI being used as an employment decision if

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Speaker 1: it is the only thing making a decision, or if

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Speaker 1: it overrides a human decision. In reality, that is not

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Speaker 1: usually the case. AI can be used to narrow down candidates,

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Speaker 1: but generally it is there is a human involved in

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Speaker 1: the process. So in the more typical iteration of how

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Speaker 1: things usually work with AI and hiring, very few companies

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Speaker 1: need to change their behavior under this law because of

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Speaker 1: that narrow definition of AI in the screening process. She

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Speaker 1: also objects to the fact that the law covers screening

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Speaker 1: candidates using AI for gender, race, and ethnicity, but not

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Speaker 1: for disability or age, which we know are big parts

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Speaker 1: of employment discrimination as is, So if this law does

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Speaker 1: become a national template, as it very well might, it

435
00:24:18,119 --> 00:24:20,159
Speaker 1: might not be as robust as it should be to

436
00:24:20,240 --> 00:24:21,480
Speaker 1: actually protect workers.

437
00:24:22,520 --> 00:24:25,480
Speaker 2: I really want to interpret this is a good start, though,

438
00:24:26,880 --> 00:24:30,320
Speaker 2: you know, she's definitely the expert and probably right about

439
00:24:30,359 --> 00:24:34,520
Speaker 2: it being too narrow, but like, maybe there'll be some

440
00:24:34,600 --> 00:24:38,600
Speaker 2: court case that interprets it a little more broadly. You know,

441
00:24:38,880 --> 00:24:42,760
Speaker 2: I appreciate that this law at least attempts to establish

442
00:24:42,760 --> 00:24:46,399
Speaker 2: some boundaries on what is acceptable to use AI for,

443
00:24:46,840 --> 00:24:51,520
Speaker 2: especially something as important as employment, and so, I don't know,

444
00:24:51,560 --> 00:24:54,000
Speaker 2: it feels nice that some legislators are at least trying,

445
00:24:55,480 --> 00:24:57,000
Speaker 2: And it seems like it would be hard for companies

446
00:24:57,000 --> 00:24:59,840
Speaker 2: to argue that being transparent about the software they use

447
00:25:00,320 --> 00:25:04,480
Speaker 2: is somehow an unacceptable burden to their hiring. So hopefully

448
00:25:04,520 --> 00:25:08,960
Speaker 2: this can be either amended or built on to maybe

449
00:25:09,040 --> 00:25:11,800
Speaker 2: expand that definition, add some teeth to it so that

450
00:25:11,840 --> 00:25:17,800
Speaker 2: it does become more accepted and expected, even that throughout

451
00:25:17,880 --> 00:25:21,560
Speaker 2: the hiring process, any sort of AI that even has

452
00:25:21,600 --> 00:25:28,280
Speaker 2: the potential to discriminate needs to be disclosed. At the

453
00:25:28,440 --> 00:25:31,000
Speaker 2: very least, it seems like asking to have it disclosed

454
00:25:31,080 --> 00:25:33,520
Speaker 2: shouldn't be objected to.

455
00:25:34,080 --> 00:25:37,520
Speaker 1: People deserve transparency when they're engaging with technology like this,

456
00:25:37,680 --> 00:25:40,320
Speaker 1: and I think that's probably one of my biggest concerns

457
00:25:40,359 --> 00:25:44,960
Speaker 1: about this technology, is that it's simply going to repeat

458
00:25:45,280 --> 00:25:49,520
Speaker 1: and also make worse the existing biases that are already

459
00:25:49,520 --> 00:25:53,160
Speaker 1: in our society. And so I think we definitely need strong, clear,

460
00:25:53,320 --> 00:25:55,480
Speaker 1: robust legislation to prevent that from happening. So I am

461
00:25:55,520 --> 00:25:57,399
Speaker 1: happy to see some legislation that it's not just a

462
00:25:57,440 --> 00:25:59,520
Speaker 1: wild West, but I want to make sure it's legislation

463
00:25:59,560 --> 00:26:03,480
Speaker 1: that acts. She really provides some meaningful and robust protection

464
00:26:03,680 --> 00:26:04,680
Speaker 1: for all of us.

465
00:26:05,119 --> 00:26:08,520
Speaker 2: Have you got any stories for me about health tech, bridget.

466
00:26:08,359 --> 00:26:10,120
Speaker 1: Let's talk about health tech, Mike.

467
00:26:10,240 --> 00:26:10,320
Speaker 2: So.

468
00:26:10,520 --> 00:26:13,760
Speaker 1: Back in March, staffers at the National Eating Disorder Association

469
00:26:14,200 --> 00:26:18,200
Speaker 1: were laid off just days after they voted to unionize. Subsequently,

470
00:26:18,560 --> 00:26:22,800
Speaker 1: the National Eating Disorders Association, or NEDA, announced they were

471
00:26:22,840 --> 00:26:26,280
Speaker 1: shutting down their twenty year old call in helpline for

472
00:26:26,320 --> 00:26:29,760
Speaker 1: folks dealing with disordered eating and food issues. They replaced

473
00:26:29,840 --> 00:26:32,480
Speaker 1: a handful of paid staffers and a large group of

474
00:26:32,560 --> 00:26:36,600
Speaker 1: volunteers with a chatbot called Tessa. Well. It turns out

475
00:26:36,600 --> 00:26:39,040
Speaker 1: that Tessa did not do so great because, rather than

476
00:26:39,080 --> 00:26:41,920
Speaker 1: giving counseling to people calling in for help with their

477
00:26:41,960 --> 00:26:44,600
Speaker 1: disordered eating, Tessa was found to be giving these people

478
00:26:44,800 --> 00:26:48,399
Speaker 1: weight loss and diet advice. A psychologist who specializes in

479
00:26:48,440 --> 00:26:51,080
Speaker 1: eating disorders ran a test with Tessa where she fed

480
00:26:51,080 --> 00:26:54,080
Speaker 1: it questions that someone seeking help for disordered eating might ask.

481
00:26:54,320 --> 00:26:56,600
Speaker 1: She pretended to be somebody who had recently gained weight

482
00:26:56,640 --> 00:27:00,960
Speaker 1: and subsequently hated their body. Tessa responded quote, approach weight

483
00:27:01,000 --> 00:27:04,000
Speaker 1: loss in a healthy and sustainable way. When the psychologist

484
00:27:04,119 --> 00:27:07,560
Speaker 1: followed up asking what that might look like, Tessa responded

485
00:27:07,560 --> 00:27:11,680
Speaker 1: with information about calorie deficits. Tessa provided information like how

486
00:27:11,680 --> 00:27:14,400
Speaker 1: to cut calories and how to avoid certain foods, which

487
00:27:14,440 --> 00:27:18,040
Speaker 1: is certainly not good advice to give somebody specifically engaging

488
00:27:18,040 --> 00:27:21,520
Speaker 1: with this spot for help with food issues and disordered eating.

489
00:27:22,000 --> 00:27:25,600
Speaker 1: So this is really interesting. Wired reports that Tessa was

490
00:27:25,640 --> 00:27:30,080
Speaker 1: not built using AI like chatbots like chat GPT. Instead,

491
00:27:30,440 --> 00:27:34,520
Speaker 1: Tessa is programmed to deliver an interactive program called body Positive,

492
00:27:34,800 --> 00:27:39,240
Speaker 1: a cognitive behavioral therapy based tool meant to prevent, not treat,

493
00:27:39,280 --> 00:27:43,000
Speaker 1: eating disorders, says Ellen Fitzimmons Craft, a professor of psychiatry

494
00:27:43,040 --> 00:27:46,040
Speaker 1: at Washington University School of Medicine who developed the program.

495
00:27:46,560 --> 00:27:49,440
Speaker 1: But here's where it gets weird. Fit simmons Craft says

496
00:27:49,480 --> 00:27:51,919
Speaker 1: that the weight loss advice given was not part of

497
00:27:51,920 --> 00:27:54,080
Speaker 1: the program that her team worked to develop. And she

498
00:27:54,119 --> 00:27:57,919
Speaker 1: does not know how Tessa got it into her repertoire,

499
00:27:58,119 --> 00:28:03,200
Speaker 1: Which is pretty scary that this non AI chatbot went

500
00:28:03,359 --> 00:28:07,720
Speaker 1: off script from the body positive program that it was

501
00:28:07,800 --> 00:28:11,960
Speaker 1: programmed to respond with and started saying advice about how

502
00:28:12,000 --> 00:28:13,880
Speaker 1: to lose weight and how to have a calorie deficit,

503
00:28:14,200 --> 00:28:17,440
Speaker 1: and the people who programmed it just have no idea

504
00:28:17,760 --> 00:28:20,560
Speaker 1: how or why it was able to go off script

505
00:28:20,640 --> 00:28:25,360
Speaker 1: in that very particular way. Pretty scary, right it is.

506
00:28:25,400 --> 00:28:28,240
Speaker 2: And I have a lot of questions about how that

507
00:28:28,320 --> 00:28:30,879
Speaker 2: might have happened. You know, I looked into this a

508
00:28:30,880 --> 00:28:35,679
Speaker 2: little bit, and their study was published in a reputable journal.

509
00:28:35,760 --> 00:28:40,400
Speaker 2: It had a good, solid design. So I really feel

510
00:28:40,560 --> 00:28:43,600
Speaker 2: for the researcher here, for Simmons Crafts, because it's by

511
00:28:44,040 --> 00:28:46,360
Speaker 2: from everything I can tell, you know, she's like a

512
00:28:46,480 --> 00:28:49,840
Speaker 2: legitimate researcher who legitimately is like doing good science, trying

513
00:28:49,880 --> 00:28:55,000
Speaker 2: to help people. And you know, it's not crazy to

514
00:28:55,080 --> 00:29:01,960
Speaker 2: imagine that that the researcher who created this tool would

515
00:29:01,960 --> 00:29:04,920
Speaker 2: not be the same people who decided that at the

516
00:29:04,920 --> 00:29:09,440
Speaker 2: programmatic level it should be used by the program to

517
00:29:09,520 --> 00:29:14,240
Speaker 2: replace the human coaches. Maybe maybe they were involved, maybe

518
00:29:14,240 --> 00:29:18,000
Speaker 2: they weren't. I don't know, and like maybe yeah, I'm

519
00:29:18,040 --> 00:29:22,240
Speaker 2: just like super curious about what it means that she

520
00:29:22,360 --> 00:29:26,240
Speaker 2: says the program did not have any of that content,

521
00:29:26,320 --> 00:29:28,959
Speaker 2: Like where did it come from? Who put it there?

522
00:29:29,920 --> 00:29:32,560
Speaker 2: You know, these are questions I'm really interested in. I

523
00:29:32,600 --> 00:29:33,440
Speaker 2: would love to know more.

524
00:29:33,840 --> 00:29:36,080
Speaker 1: The robots are going rogue and they want us to

525
00:29:36,120 --> 00:29:38,840
Speaker 1: lose weight. I think I think that's clear that's what's

526
00:29:38,840 --> 00:29:39,720
Speaker 1: going on here.

527
00:29:40,480 --> 00:29:42,840
Speaker 2: Maybe, but like I feel like they'd want to fatten

528
00:29:42,920 --> 00:29:46,680
Speaker 2: us up to provide more calories for their like robot machines.

529
00:29:47,120 --> 00:29:48,960
Speaker 1: Robots are going to be eating humans.

530
00:29:49,120 --> 00:29:52,680
Speaker 2: That's how they'll be digesting us for fuel obviously.

531
00:29:54,400 --> 00:29:57,400
Speaker 1: Okay, so this is kind of important context. The NDA

532
00:29:57,640 --> 00:30:00,440
Speaker 1: says that they were not and are not planning on

533
00:30:01,120 --> 00:30:05,960
Speaker 1: replacing humans with chatbots, but it does sound that like

534
00:30:06,040 --> 00:30:09,200
Speaker 1: that in the midst of this staff shakeup after their

535
00:30:09,600 --> 00:30:13,640
Speaker 1: their unionization effort, it does sound like this chatbot was

536
00:30:13,680 --> 00:30:16,640
Speaker 1: the only resource that they had to offer the public.

537
00:30:17,160 --> 00:30:19,959
Speaker 1: So they might say that they weren't intending to replace

538
00:30:20,040 --> 00:30:23,800
Speaker 1: humans with this bot, but subsequently that's like kind of

539
00:30:23,840 --> 00:30:25,680
Speaker 1: what happened, whether that was their intention or not.

540
00:30:26,760 --> 00:30:29,600
Speaker 2: Yet, it's again hard to know what to make of

541
00:30:29,640 --> 00:30:32,440
Speaker 2: the fat the idea that like, they didn't intend to

542
00:30:32,520 --> 00:30:36,160
Speaker 2: do the thing that their organization did, Like, where did

543
00:30:36,280 --> 00:30:38,560
Speaker 2: that come from? Was TESLA calling the shots at the

544
00:30:38,600 --> 00:30:43,680
Speaker 2: board meeting? It seems a little I don't know, it

545
00:30:43,720 --> 00:30:47,440
Speaker 2: just feels a little sketchy to me, honestly, Like, yeah,

546
00:30:47,440 --> 00:30:51,040
Speaker 2: the unionization part, they're like they According to the NPR

547
00:30:51,200 --> 00:30:53,440
Speaker 2: article that we read it as linked in the show

548
00:30:53,520 --> 00:30:57,920
Speaker 2: notes here, it's such an internal email at any DA

549
00:30:58,080 --> 00:31:01,680
Speaker 2: that points to increase legal risks about crisis handling and

550
00:31:01,880 --> 00:31:04,320
Speaker 2: mandatory reporting is one of the reasons for laying off

551
00:31:04,360 --> 00:31:08,440
Speaker 2: the coaches. But then it just doesn't make sense that

552
00:31:08,440 --> 00:31:10,280
Speaker 2: they would start to me, at least, it doesn't make

553
00:31:10,280 --> 00:31:12,560
Speaker 2: sense that they would start using a chatbot as a replacement,

554
00:31:12,600 --> 00:31:15,720
Speaker 2: as if that would somehow be less risky. It just

555
00:31:15,720 --> 00:31:20,320
Speaker 2: seems odd, you know, and laying off workers within days

556
00:31:20,360 --> 00:31:25,560
Speaker 2: of a vote to unionize feels super gross. So I

557
00:31:25,640 --> 00:31:31,200
Speaker 2: think it's important to sort of separate the two different

558
00:31:31,240 --> 00:31:34,840
Speaker 2: stories here, Like the chat bot that worked as a

559
00:31:34,880 --> 00:31:39,320
Speaker 2: prevention tool to help prevent people, because that study that

560
00:31:40,280 --> 00:31:44,640
Speaker 2: Fit Simmonscraft authored demonstrates that it did really help people

561
00:31:45,400 --> 00:31:49,040
Speaker 2: with preventing eating disorders. But then it got used in

562
00:31:49,080 --> 00:31:52,560
Speaker 2: this other context where it was actually harming people. That's

563
00:31:52,600 --> 00:31:56,760
Speaker 2: interesting and worth talking about. And then also it seems

564
00:31:56,800 --> 00:31:59,120
Speaker 2: like this organization did not want to deal with the

565
00:31:59,200 --> 00:32:02,640
Speaker 2: union and so like replaced all their humans as soon

566
00:32:02,680 --> 00:32:07,080
Speaker 2: as they unionized, which is pretty disappointing for a nonprofit

567
00:32:07,200 --> 00:32:10,000
Speaker 2: organization that osensibly is trying to help people.

568
00:32:10,600 --> 00:32:12,960
Speaker 1: Yeah, and I have to say, like, when I first

569
00:32:13,480 --> 00:32:17,080
Speaker 1: chose this story to include in the roundup for today's episode,

570
00:32:17,440 --> 00:32:20,760
Speaker 1: I was like, this is a story about like what

571
00:32:20,920 --> 00:32:22,680
Speaker 1: I can I can ever say the word right shot

572
00:32:22,720 --> 00:32:25,240
Speaker 1: shot in Freuda, I always say it wrong. It's funny.

573
00:32:25,240 --> 00:32:29,280
Speaker 1: There's a one of our Patreon patrons has that as

574
00:32:29,480 --> 00:32:32,640
Speaker 1: their their like Patreon name, and I damn them, And

575
00:32:32,640 --> 00:32:33,920
Speaker 1: I was like, I can never. I have to like

576
00:32:33,960 --> 00:32:35,800
Speaker 1: look it up every time. Shot and Freuda. I think

577
00:32:35,800 --> 00:32:38,560
Speaker 1: that's how you say it. When I first heard the story,

578
00:32:38,600 --> 00:32:40,800
Speaker 1: I was like, Oh, this is what they get for

579
00:32:42,120 --> 00:32:46,280
Speaker 1: firing their unionizing humans and trying to replace the unionizing

580
00:32:46,360 --> 00:32:50,320
Speaker 1: humans with a robot like haha, dunk on them. But

581
00:32:50,760 --> 00:32:53,640
Speaker 1: as I looked into it, more like, it's really easy

582
00:32:53,680 --> 00:32:56,520
Speaker 1: to make fun of the n EDA here, but it

583
00:32:56,600 --> 00:32:58,960
Speaker 1: really does highlight what I think is a pretty serious

584
00:32:59,000 --> 00:33:01,280
Speaker 1: issue that a lot of people need access to mental

585
00:33:01,320 --> 00:33:04,440
Speaker 1: health services and support, and that access is still out

586
00:33:04,440 --> 00:33:07,440
Speaker 1: of reach for so many, which is why an organization,

587
00:33:08,080 --> 00:33:10,719
Speaker 1: you know, like an e EDA would feel like they

588
00:33:10,720 --> 00:33:12,920
Speaker 1: could they had to turn to chatbots to sort of

589
00:33:13,240 --> 00:33:16,840
Speaker 1: close that gap. So it's easy to dunk on them here,

590
00:33:17,160 --> 00:33:20,960
Speaker 1: but ultimately that is a real problem that we do

591
00:33:21,040 --> 00:33:24,720
Speaker 1: need to solve for Whether or not technology like chat

592
00:33:24,760 --> 00:33:27,880
Speaker 1: bots and AI can solve for it, I don't know.

593
00:33:27,920 --> 00:33:30,200
Speaker 1: That's not something I know about, but it is a

594
00:33:30,200 --> 00:33:32,000
Speaker 1: real problem. And Mike, I know that when you're not

595
00:33:32,080 --> 00:33:34,720
Speaker 1: producing the show, this is something that you actually work on, right.

596
00:33:35,240 --> 00:33:37,720
Speaker 2: That's right, Yeah, I When I'm not producing the show,

597
00:33:37,760 --> 00:33:40,600
Speaker 2: I do research with a nonprofit that creates free digital

598
00:33:40,640 --> 00:33:44,360
Speaker 2: tools to help support behavior, help behavior change, and help

599
00:33:44,400 --> 00:33:50,080
Speaker 2: people overcome addiction. And so I do like know a

600
00:33:50,160 --> 00:33:52,920
Speaker 2: fair a fair bit about this. This is an area

601
00:33:52,920 --> 00:33:55,560
Speaker 2: where I'm actively doing research and actively working in a

602
00:33:55,640 --> 00:34:00,760
Speaker 2: nonprofit where we run public programs that provide support for

603
00:34:00,920 --> 00:34:04,200
Speaker 2: health behavior change over the Internet. And we've been talking

604
00:34:04,560 --> 00:34:07,880
Speaker 2: a lot about chatbots lately and what role they can

605
00:34:08,000 --> 00:34:11,359
Speaker 2: or should have for providing digital health services, because, as

606
00:34:11,400 --> 00:34:16,000
Speaker 2: you mentioned, there is this huge unmet need for mental

607
00:34:16,040 --> 00:34:19,439
Speaker 2: health services as well as other kinds of behavioral health

608
00:34:19,440 --> 00:34:25,880
Speaker 2: services like smoking cessation, diabetes management. You know, there's there's

609
00:34:25,920 --> 00:34:30,840
Speaker 2: just you know, physical exercise, healthy eating, so many health

610
00:34:30,880 --> 00:34:35,680
Speaker 2: behaviors that cause a lot of medical harmor medical costs,

611
00:34:36,560 --> 00:34:40,560
Speaker 2: reduce you know, good healthy years that people live on

612
00:34:40,600 --> 00:34:43,640
Speaker 2: their lives. There's this huge unmet need for it. And

613
00:34:44,120 --> 00:34:48,200
Speaker 2: if we can use digital technology to meet that need

614
00:34:48,280 --> 00:34:51,759
Speaker 2: and connect people with resources that help them meet their

615
00:34:51,800 --> 00:34:56,040
Speaker 2: health goals and adopt healthier behaviors that they want to adopt,

616
00:34:56,120 --> 00:35:01,839
Speaker 2: but uh for various reasons, often including industries that aggressively

617
00:35:01,920 --> 00:35:04,760
Speaker 2: market them to prevent them from achieving those healthier behaviors

618
00:35:04,760 --> 00:35:08,600
Speaker 2: that they want to achieve. And so I can use

619
00:35:08,680 --> 00:35:11,759
Speaker 2: digital technology to meet that need and help support people

620
00:35:11,840 --> 00:35:15,239
Speaker 2: to adopt these healthier behaviors that they're trying to adopt.

621
00:35:16,040 --> 00:35:19,640
Speaker 2: That would be a huge win for everyone. But there's

622
00:35:19,719 --> 00:35:22,799
Speaker 2: this question of how to do it right. And with

623
00:35:23,719 --> 00:35:27,719
Speaker 2: generative AI chatbots in particular, we have no idea what

624
00:35:27,840 --> 00:35:30,360
Speaker 2: kind of advice they might give to a person looking

625
00:35:30,400 --> 00:35:35,480
Speaker 2: for help, and so there's a risk as the national

626
00:35:35,960 --> 00:35:38,799
Speaker 2: what is it, the National Eating Disorders Association found out

627
00:35:39,040 --> 00:35:44,480
Speaker 2: there's a risk that somebody who is in crisis or

628
00:35:44,560 --> 00:35:47,359
Speaker 2: who is really experiencing a problem might ask for help

629
00:35:47,560 --> 00:35:50,280
Speaker 2: and receive advice that actually turns out to be harmful

630
00:35:50,320 --> 00:35:52,800
Speaker 2: for them. And that's, you know, as somebody who designs

631
00:35:52,880 --> 00:35:57,960
Speaker 2: health interventions, that is terrifying. You know, the risk is

632
00:35:58,160 --> 00:36:01,080
Speaker 2: especially high for people in crisis. You know, for example,

633
00:36:01,120 --> 00:36:04,320
Speaker 2: people who are in suicidal or intend to harm others

634
00:36:05,160 --> 00:36:09,200
Speaker 2: because they're more vulnerable, and so we have a greater

635
00:36:09,440 --> 00:36:13,160
Speaker 2: obligation to protect those people who are the most vulnerable,

636
00:36:15,200 --> 00:36:18,360
Speaker 2: you know. I guess another related thing is that generally

637
00:36:18,360 --> 00:36:21,800
Speaker 2: in public health, we give more priority to not causing

638
00:36:21,920 --> 00:36:27,040
Speaker 2: harm compared to helping people. Right, Like, everyone is probably

639
00:36:27,040 --> 00:36:31,640
Speaker 2: familiar with the trolley problem, and again it's especially the

640
00:36:31,680 --> 00:36:35,120
Speaker 2: case when people are at risk of being harmed who

641
00:36:35,120 --> 00:36:39,120
Speaker 2: are already vulnerable. We really want to protect them and

642
00:36:39,200 --> 00:36:41,799
Speaker 2: make sure that whatever treatment we're giving to try to

643
00:36:41,880 --> 00:36:46,880
Speaker 2: help is not causing more harm. So that unknown factor

644
00:36:46,920 --> 00:36:50,160
Speaker 2: about generative AI and chatbots, I think we give a

645
00:36:50,200 --> 00:36:54,000
Speaker 2: lot of people in positions to provide them to the

646
00:36:54,000 --> 00:36:58,839
Speaker 2: public pause about using those chatbots to deliver mental health

647
00:36:58,840 --> 00:37:02,719
Speaker 2: support in particular, you know, there is a lot of

648
00:37:03,000 --> 00:37:08,360
Speaker 2: potential there, as the researcher we mentioned demonstrated, you know,

649
00:37:08,480 --> 00:37:11,879
Speaker 2: or programmed Tessa really did help some people. So there's

650
00:37:11,880 --> 00:37:14,640
Speaker 2: a lot of potential, but we creators need to be

651
00:37:14,880 --> 00:37:18,440
Speaker 2: to carefully protect against adverse outcomes and risks to users,

652
00:37:18,920 --> 00:37:22,400
Speaker 2: especially among the most vulnerable. And so it sounds like

653
00:37:23,080 --> 00:37:25,719
Speaker 2: the version of Tessa that was implemented on their website

654
00:37:25,760 --> 00:37:28,680
Speaker 2: didn't do that. And so I think it's good that

655
00:37:28,719 --> 00:37:31,239
Speaker 2: the service has been taken down until it can be

656
00:37:32,120 --> 00:37:35,360
Speaker 2: you know, changed in whatever way is needed that it

657
00:37:35,400 --> 00:37:37,640
Speaker 2: can then be demonstrated to be safe to be made

658
00:37:37,640 --> 00:37:39,000
Speaker 2: available to the public. Again.

659
00:37:40,000 --> 00:37:42,000
Speaker 1: You know who else I think might need to be

660
00:37:42,040 --> 00:37:45,080
Speaker 1: taken down until it can be demonstrated that they are

661
00:37:45,120 --> 00:37:48,360
Speaker 1: safe for the public. Elon Musk, I'll tell you I

662
00:37:48,719 --> 00:37:49,560
Speaker 1: after this quick.

663
00:37:49,400 --> 00:38:05,680
Speaker 3: Break more, after a quick break, let's get right back

664
00:38:05,719 --> 00:38:06,080
Speaker 3: into it.

665
00:38:08,640 --> 00:38:09,560
Speaker 1: Let's talk about Twitter.

666
00:38:10,760 --> 00:38:13,560
Speaker 2: It might be a while before we can demonstrate that

667
00:38:13,600 --> 00:38:15,800
Speaker 2: he is not a king to the public.

668
00:38:17,800 --> 00:38:20,840
Speaker 1: Agreed. Okay, So this is I'm going to try to

669
00:38:20,880 --> 00:38:25,080
Speaker 1: rein it in because this story makes me so fucking mard. Okay,

670
00:38:26,800 --> 00:38:29,759
Speaker 1: Over the weekend, a verified parody account on Twitter that

671
00:38:29,800 --> 00:38:32,440
Speaker 1: looks an awful lot, like the real Twitter account for

672
00:38:32,560 --> 00:38:36,920
Speaker 1: Representative Alexandria Ocazio. Cortez claimed to have a crush on

673
00:38:37,000 --> 00:38:41,200
Speaker 1: Elon Musk. Elon Musk replied with a fire emoji. Of

674
00:38:41,200 --> 00:38:44,440
Speaker 1: course it was not the real AOC. It was a

675
00:38:44,480 --> 00:38:48,680
Speaker 1: parody heavy scare quotes account. The account has been around

676
00:38:48,719 --> 00:38:51,520
Speaker 1: since twenty eighteen, but it was permanently suspended in twenty

677
00:38:51,600 --> 00:38:55,400
Speaker 1: nineteen under Jack Dorsey, the former Twitter CEO's leadership for

678
00:38:55,680 --> 00:38:58,719
Speaker 1: misleading parity content. I should say here that like this

679
00:38:58,840 --> 00:39:01,560
Speaker 1: is a real problem online and where there are accounts

680
00:39:01,600 --> 00:39:05,719
Speaker 1: that just say off the wall stuff and they say, oh,

681
00:39:05,760 --> 00:39:09,839
Speaker 1: I'm parody, but it's not really a joke. It's hard

682
00:39:09,840 --> 00:39:11,680
Speaker 1: to explain, but you know, you definitely have seen them.

683
00:39:11,680 --> 00:39:13,720
Speaker 1: You know them when you see them where they're able

684
00:39:13,800 --> 00:39:18,120
Speaker 1: to hide behind parody when you know that it's not parody. Right,

685
00:39:18,200 --> 00:39:21,640
Speaker 1: like AOC saying that she has a crush on Elon Musk,

686
00:39:21,719 --> 00:39:24,600
Speaker 1: that's not parody, that's something else. So that's neither he

687
00:39:24,719 --> 00:39:28,480
Speaker 1: nor there. So this Twitter account had been permanent suspended

688
00:39:28,560 --> 00:39:32,360
Speaker 1: by Twitter back in twenty nineteen, but Musk reinstated it

689
00:39:32,400 --> 00:39:34,400
Speaker 1: when he brought back a bunch of band accounts, and

690
00:39:34,440 --> 00:39:37,279
Speaker 1: because of the way that Musk's verification system works, which

691
00:39:37,320 --> 00:39:39,480
Speaker 1: is basically pay to play. If you pay the eight

692
00:39:39,560 --> 00:39:42,839
Speaker 1: dollars to get verified, it boosts your you know, your

693
00:39:42,880 --> 00:39:46,520
Speaker 1: content and search and on the feed, it actually boosts

694
00:39:46,560 --> 00:39:50,360
Speaker 1: the fake account because it is verified. So currently, and

695
00:39:50,400 --> 00:39:52,960
Speaker 1: I tried this right before we started recording, when you

696
00:39:53,040 --> 00:39:57,160
Speaker 1: searched AOC on Twitter, the fake AOC is ranked first

697
00:39:57,239 --> 00:40:01,400
Speaker 1: in search results, above the actual, authentic AOC's official account

698
00:40:01,680 --> 00:40:06,040
Speaker 1: because it's verified. So Musk is boosting this fake account

699
00:40:06,080 --> 00:40:08,680
Speaker 1: both because of the way that Twitter Blue works, but

700
00:40:08,800 --> 00:40:11,759
Speaker 1: he is also personally boosting this account because he is

701
00:40:11,800 --> 00:40:14,840
Speaker 1: engaging with it from his own individual Twitter account.

702
00:40:15,760 --> 00:40:18,440
Speaker 2: It's so disgusting. He's such a pathetic creep. You know,

703
00:40:18,520 --> 00:40:21,680
Speaker 2: he knows it's a fake account. He unbanned it anyway,

704
00:40:22,160 --> 00:40:26,239
Speaker 2: and now he's engaging with it and boosting with it.

705
00:40:26,360 --> 00:40:31,160
Speaker 1: Just ugh ugh is right. So the real AOC tweeted, FYI,

706
00:40:31,239 --> 00:40:34,000
Speaker 1: there's a fake account on here, impersonating me and going viral.

707
00:40:34,239 --> 00:40:37,440
Speaker 1: The Twitter CEO has engaged it, boosting visibility. It is

708
00:40:37,480 --> 00:40:40,840
Speaker 1: releasing false policy statements and gaining spread. I am assessing

709
00:40:40,880 --> 00:40:43,120
Speaker 1: with my team how to move forward. In the meantime,

710
00:40:43,200 --> 00:40:46,400
Speaker 1: be careful what you see. Soon after the real AOC

711
00:40:46,480 --> 00:40:49,839
Speaker 1: tweeted that the fake account tweeted the exact same thing

712
00:40:49,960 --> 00:40:53,520
Speaker 1: word for word. Now, parody accounts on Twitter are required

713
00:40:53,560 --> 00:40:56,520
Speaker 1: to spell out that they're parody accounts, but as of today,

714
00:40:56,840 --> 00:40:59,800
Speaker 1: when you look at the fake AOC accounts tweets on mobile,

715
00:41:00,160 --> 00:41:03,240
Speaker 1: the word parity is cut off, so there's no quick

716
00:41:03,360 --> 00:41:06,000
Speaker 1: visual way to differentiate that this is not the real AOC.

717
00:41:06,160 --> 00:41:09,760
Speaker 1: If a user is just encountering these fake AOC tweets

718
00:41:09,800 --> 00:41:12,000
Speaker 1: in their feed on mobile right like, you would have

719
00:41:12,080 --> 00:41:14,680
Speaker 1: to click all the way into the profile to see

720
00:41:14,680 --> 00:41:17,400
Speaker 1: that it is a parody. There is no visual marker.

721
00:41:17,440 --> 00:41:20,279
Speaker 1: It has her image, it is verified. If you saw

722
00:41:20,320 --> 00:41:21,960
Speaker 1: this quickly on mobile, you would think it was the

723
00:41:22,000 --> 00:41:25,640
Speaker 1: real AOC. And particularly the platforms like Twitter move so

724
00:41:25,920 --> 00:41:28,359
Speaker 1: quickly and they encourage you to move so quickly that

725
00:41:28,400 --> 00:41:30,080
Speaker 1: people aren't I don't think people are going to be

726
00:41:30,200 --> 00:41:32,040
Speaker 1: checking to see if this is the real account or not.

727
00:41:32,840 --> 00:41:35,759
Speaker 2: If only there was some visual marker that people could

728
00:41:35,880 --> 00:41:41,440
Speaker 2: use to quickly see which accounts are like actually the

729
00:41:41,480 --> 00:41:44,680
Speaker 2: people who say they are in which ones aren't. If

730
00:41:44,760 --> 00:41:48,480
Speaker 2: only such a system like that existed, but maybe it's

731
00:41:48,480 --> 00:41:50,400
Speaker 2: just impossible and I'm dreaming.

732
00:41:50,760 --> 00:41:53,600
Speaker 1: Oh my god. I mean, we did a whole episode

733
00:41:53,600 --> 00:41:56,879
Speaker 1: about verification for my project with cool Zone Media called

734
00:41:56,920 --> 00:42:01,160
Speaker 1: Internet Hate Machine, about how it's it's not just like

735
00:42:01,200 --> 00:42:03,799
Speaker 1: a vanity thing, like it really matters that people are

736
00:42:03,840 --> 00:42:06,160
Speaker 1: who they say they are. Just I think two weeks

737
00:42:06,200 --> 00:42:10,200
Speaker 1: ago here in DC, there was a tweet two tweets

738
00:42:10,200 --> 00:42:12,719
Speaker 1: from a verified account that said that there was a

739
00:42:12,840 --> 00:42:16,120
Speaker 1: massive explosion in DC by the Pentagon, and it had

740
00:42:16,160 --> 00:42:19,480
Speaker 1: a very compelling image that looked that looked like a

741
00:42:19,520 --> 00:42:22,680
Speaker 1: lot of smoke and flames in the Pentagon, and it

742
00:42:22,760 --> 00:42:26,919
Speaker 1: actually caused an IRL traffic disruption here in DC because

743
00:42:26,920 --> 00:42:29,080
Speaker 1: people thought, like, oh, don't drive by the Pentagon. And

744
00:42:29,120 --> 00:42:32,200
Speaker 1: it was fake. It was it was a verified account

745
00:42:32,320 --> 00:42:38,360
Speaker 1: spreading inaccurate information that looked real. And so I really

746
00:42:38,719 --> 00:42:44,520
Speaker 1: truly worry what happens when people at scale are buying

747
00:42:44,640 --> 00:42:48,440
Speaker 1: verifications and using it to disrupt, using it to confuse,

748
00:42:48,719 --> 00:42:50,400
Speaker 1: using it to cause chaos.

749
00:42:50,800 --> 00:42:53,360
Speaker 2: And I think that fake tweet about the fire at

750
00:42:53,400 --> 00:42:56,319
Speaker 2: the Pentagon, which was just like a fate made up thing.

751
00:42:56,600 --> 00:42:59,440
Speaker 2: I think it also like tanked the stock market for

752
00:42:59,520 --> 00:43:01,840
Speaker 2: a little while and it like cost a bunch of

753
00:43:01,840 --> 00:43:02,800
Speaker 2: people a ton of money.

754
00:43:03,400 --> 00:43:05,680
Speaker 1: Yeah, it makes me sad to say, but I always

755
00:43:05,680 --> 00:43:09,760
Speaker 1: say this when rich people and corporations had their bottom

756
00:43:09,800 --> 00:43:12,080
Speaker 1: lines impacted. I think that's the I think. I mean

757
00:43:12,360 --> 00:43:14,879
Speaker 1: this probably sounds so pessimistic. I think that is one

758
00:43:14,920 --> 00:43:19,000
Speaker 1: of the last bastions of doing something that we have

759
00:43:19,080 --> 00:43:22,360
Speaker 1: in this country. If regular people are harmed, who cares

760
00:43:23,239 --> 00:43:26,600
Speaker 1: If if somebody, if a corporation or the market is impacted,

761
00:43:26,840 --> 00:43:28,799
Speaker 1: then I think people will pay attention. It makes me

762
00:43:28,880 --> 00:43:31,200
Speaker 1: sad to say, but I do firmly believe it. That's

763
00:43:31,239 --> 00:43:34,680
Speaker 1: like the last lever that anybody with power cares about

764
00:43:34,719 --> 00:43:35,440
Speaker 1: in this country.

765
00:43:35,960 --> 00:43:37,480
Speaker 2: Yeah. I just ask Elizabeth Holmes.

766
00:43:37,719 --> 00:43:40,640
Speaker 1: Yep, just thinking about her, my girl. It's also, by

767
00:43:40,640 --> 00:43:42,320
Speaker 1: the way, it's Liz Now she rebranded. I don't know

768
00:43:42,320 --> 00:43:42,719
Speaker 1: if you saw.

769
00:43:43,160 --> 00:43:44,440
Speaker 2: I did not it's Liz.

770
00:43:44,760 --> 00:43:48,279
Speaker 1: It's Liz Now. Although but in fact she I am

771
00:43:48,320 --> 00:43:50,759
Speaker 1: so like I swear that I did not plan this.

772
00:43:51,320 --> 00:43:52,920
Speaker 1: I wanted to find a way to work in this

773
00:43:53,280 --> 00:43:56,000
Speaker 1: tidbit that Elizabeth Holmes is going to be in the

774
00:43:56,000 --> 00:43:59,160
Speaker 1: same jail as Jen Shaw from Real Housewives as Salt

775
00:43:59,200 --> 00:44:05,040
Speaker 1: Lake City. No, yes, yes, okay, So just that's information

776
00:44:05,080 --> 00:44:07,680
Speaker 1: for y'all if you visit a real overlap of shit

777
00:44:07,719 --> 00:44:13,080
Speaker 1: that I care about, housewives like Bravo Housewives and tech scams.

778
00:44:14,920 --> 00:44:18,680
Speaker 2: Yeah wow, I wonder if there's gonna be buds.

779
00:44:18,160 --> 00:44:22,799
Speaker 1: You know they there is no way that Genshaw is

780
00:44:22,840 --> 00:44:25,080
Speaker 1: not already scheming how to get this woman on her team.

781
00:44:25,080 --> 00:44:27,719
Speaker 1: I'll want to say that, I know, I know my Genshaw.

782
00:44:27,920 --> 00:44:29,759
Speaker 1: She's already scheming to get this woman on her team.

783
00:44:29,920 --> 00:44:34,600
Speaker 2: Trust boy, Liz better keep an eye out seriously.

784
00:44:35,840 --> 00:44:39,680
Speaker 1: Okay, So back to this parody account. NBC News dug

785
00:44:39,680 --> 00:44:41,640
Speaker 1: into this account and they found that, according to the

786
00:44:41,680 --> 00:44:45,680
Speaker 1: Social Blade, a social media analytics tracker, the AOC parody

787
00:44:45,680 --> 00:44:49,080
Speaker 1: account had eighty five thousand followers back in May twenty nineteen,

788
00:44:49,320 --> 00:44:51,640
Speaker 1: the same month it was suspended. After the account was

789
00:44:51,680 --> 00:44:54,880
Speaker 1: restored in May of twenty twenty three, it immediately lost

790
00:44:54,920 --> 00:44:58,480
Speaker 1: sixteen thousand followers. Then the account shot back up to

791
00:44:58,560 --> 00:45:02,200
Speaker 1: over eighty thousand followers on May twenty ninth after Musk

792
00:45:02,280 --> 00:45:04,720
Speaker 1: replied to it. On May thirtieth, the day that AOC

793
00:45:04,880 --> 00:45:07,880
Speaker 1: responded to the account, it gained over one hundred thousand followers.

794
00:45:08,120 --> 00:45:10,799
Speaker 1: It has continued to climb today. Let's just check and

795
00:45:10,840 --> 00:45:14,000
Speaker 1: see how many it has as of right now. I

796
00:45:14,080 --> 00:45:16,160
Speaker 1: meant to do this before we taped, but I forgot.

797
00:45:16,560 --> 00:45:18,920
Speaker 2: That's okay, we can do it in real time for

798
00:45:19,000 --> 00:45:19,640
Speaker 2: the listeners.

799
00:45:19,960 --> 00:45:24,120
Speaker 1: Today it has three hundred and ninety one point one

800
00:45:24,560 --> 00:45:27,319
Speaker 1: k followers. That is a lot of followers.

801
00:45:27,719 --> 00:45:30,360
Speaker 2: That's a lot of followers. That's more than like a

802
00:45:30,400 --> 00:45:34,000
Speaker 2: four hundred percent increase from before Musk replied to it.

803
00:45:34,680 --> 00:45:38,040
Speaker 1: So obviously Musk replying to it and engaging with it

804
00:45:38,080 --> 00:45:40,920
Speaker 1: has helped it grow in followers. Helped this account that

805
00:45:41,000 --> 00:45:44,520
Speaker 1: is not accurate, it's confusing, and is not easily and

806
00:45:44,560 --> 00:45:48,080
Speaker 1: clearly distinguishable as parody on mobile. He's clearly helped for

807
00:45:48,120 --> 00:45:50,879
Speaker 1: it to grow. What's even wilder is that NBC found

808
00:45:50,920 --> 00:45:53,840
Speaker 1: that the fake AOC account was run, at least initially

809
00:45:53,880 --> 00:45:57,280
Speaker 1: by a GOP operative. It was first started by Michael Morrison,

810
00:45:57,320 --> 00:45:59,440
Speaker 1: whose Twitter profile indicates him as a member of the

811
00:45:59,480 --> 00:46:02,920
Speaker 1: New York Republicans Club. Morrison posted about the suspensions on

812
00:46:03,000 --> 00:46:06,000
Speaker 1: his account on the conservative social media platform gabbed, where

813
00:46:06,000 --> 00:46:09,000
Speaker 1: he also posted from a parody of AOC's official account.

814
00:46:09,000 --> 00:46:12,080
Speaker 1: Between April twenty nineteen and July of twenty nineteen. The

815
00:46:12,120 --> 00:46:15,360
Speaker 1: post shared on gab were more sexually explicit, and the

816
00:46:15,640 --> 00:46:20,560
Speaker 1: account reshared posts from other users that contained racist slurs. Surprise, surprise,

817
00:46:21,000 --> 00:46:23,200
Speaker 1: this guy sucks God.

818
00:46:23,239 --> 00:46:28,160
Speaker 2: These people are such troglodites. Like, of course it's sexually explicit. Like,

819
00:46:28,520 --> 00:46:31,600
Speaker 2: let's remember that the original tweet that Musk replied to

820
00:46:33,000 --> 00:46:37,120
Speaker 2: with a fire emoji was the parody account pretending the

821
00:46:37,160 --> 00:46:40,920
Speaker 2: AOC liked him and had a crush on him.

822
00:46:41,800 --> 00:46:43,799
Speaker 1: I want to come back to that. I should mention

823
00:46:43,920 --> 00:46:46,560
Speaker 1: that NBC says that they reached out to Morrison and

824
00:46:46,600 --> 00:46:49,520
Speaker 1: he said that he no longer runs this AOC parody

825
00:46:49,560 --> 00:46:51,279
Speaker 1: Twitter account and that he thinks it might be run

826
00:46:51,280 --> 00:46:53,840
Speaker 1: by a team of people. Take that for what it's worth, Like,

827
00:46:54,760 --> 00:46:56,799
Speaker 1: that's that's what he told them. I'm not I don't

828
00:46:56,800 --> 00:46:59,000
Speaker 1: know if that's true or not. Who knows. So I

829
00:46:59,040 --> 00:47:02,799
Speaker 1: have talked about about verification and impersonation on Twitter with

830
00:47:02,840 --> 00:47:04,759
Speaker 1: that a whole episode of Internet Hate Machine about it.

831
00:47:05,120 --> 00:47:07,759
Speaker 1: You know, having a blue check mark is not just

832
00:47:07,880 --> 00:47:10,080
Speaker 1: a vanity thing. It really did start as a way

833
00:47:10,120 --> 00:47:14,080
Speaker 1: to help identify help users identify people are who they

834
00:47:14,160 --> 00:47:15,600
Speaker 1: say they are, and it's been a way to provide

835
00:47:15,600 --> 00:47:17,879
Speaker 1: securities for folks who are at the risk of being

836
00:47:17,920 --> 00:47:22,080
Speaker 1: impersonated online. We already know that impersonating black women and

837
00:47:22,400 --> 00:47:25,759
Speaker 1: women of color on Twitter can cause chaos, and that's

838
00:47:25,760 --> 00:47:29,000
Speaker 1: a thing that bad actors do intentionally to do that.

839
00:47:29,080 --> 00:47:32,840
Speaker 1: So it's not surprising to me that AOC is being targeted. Also,

840
00:47:32,920 --> 00:47:36,160
Speaker 1: I should say that the obvious thing, Mike, that you

841
00:47:36,239 --> 00:47:38,560
Speaker 1: brought up that we kind of can't not mention is

842
00:47:38,600 --> 00:47:42,400
Speaker 1: that this parody account is targeting a woman of color

843
00:47:42,400 --> 00:47:45,160
Speaker 1: elected official, and they're not just using this account to

844
00:47:45,200 --> 00:47:49,080
Speaker 1: like poke thon at her political or policy decisions. It

845
00:47:49,200 --> 00:47:51,600
Speaker 1: is making her say things like she has a crush

846
00:47:51,640 --> 00:47:56,080
Speaker 1: on Elon Musk, and the real Elon Musk pathetically replies,

847
00:47:56,760 --> 00:47:59,840
Speaker 1: let's be super clear. This is so obviously about you,

848
00:48:00,320 --> 00:48:03,840
Speaker 1: a woman of colors, gender, race, age, and sexuality, to

849
00:48:03,960 --> 00:48:06,600
Speaker 1: belittle her because she is a public figure. It is

850
00:48:06,640 --> 00:48:09,359
Speaker 1: about weaponizing her identity. And I think part of it

851
00:48:09,400 --> 00:48:13,040
Speaker 1: is that, like, AOC is an attractive young woman, and

852
00:48:13,120 --> 00:48:17,480
Speaker 1: so there is this thing when you are an attractive

853
00:48:17,640 --> 00:48:20,600
Speaker 1: young woman who is also in public that it listits

854
00:48:20,640 --> 00:48:23,640
Speaker 1: this really gross behavior and it's all kind of linked, right,

855
00:48:23,719 --> 00:48:27,640
Speaker 1: like hating her being obsessed with her being titillated by

856
00:48:27,640 --> 00:48:30,840
Speaker 1: her putting her down. It's all sides of the same

857
00:48:31,000 --> 00:48:33,200
Speaker 1: fucked up coin. And I have said this at one

858
00:48:33,239 --> 00:48:37,120
Speaker 1: million times. But it's also not just about AOC. It

859
00:48:37,200 --> 00:48:39,280
Speaker 1: is meant to send a message to other young women

860
00:48:39,480 --> 00:48:43,399
Speaker 1: who would be potential like civic leaders, or might run

861
00:48:43,440 --> 00:48:46,080
Speaker 1: for office, or be activists or be public in their communities,

862
00:48:46,120 --> 00:48:48,080
Speaker 1: that this is what you will have to deal with.

863
00:48:48,120 --> 00:48:50,240
Speaker 1: If you are a young woman, a woman of color,

864
00:48:50,400 --> 00:48:53,319
Speaker 1: or a black woman who speaks up and wants to

865
00:48:53,320 --> 00:48:55,920
Speaker 1: represent your community publicly, this is what you will have

866
00:48:55,960 --> 00:48:58,799
Speaker 1: to endure. The harassment will be in plain sight, and

867
00:48:58,840 --> 00:49:01,759
Speaker 1: it will be endorsed and on and enabled by the

868
00:49:01,800 --> 00:49:04,040
Speaker 1: powers that be in this case Elon Musk. So if

869
00:49:04,080 --> 00:49:06,880
Speaker 1: you run for office, or become an activist, or become

870
00:49:06,960 --> 00:49:09,520
Speaker 1: somebody who uses a public profile to advocate for things

871
00:49:09,520 --> 00:49:11,440
Speaker 1: that you care about and help your community, this is

872
00:49:11,480 --> 00:49:13,680
Speaker 1: what you have to deal with. It is systemic, It

873
00:49:13,719 --> 00:49:17,120
Speaker 1: is anti democratic. It stifles free speech because think of

874
00:49:17,239 --> 00:49:19,439
Speaker 1: all the women who will not speak up, who will

875
00:49:19,480 --> 00:49:21,759
Speaker 1: not run for office, who will not serve their communities

876
00:49:21,840 --> 00:49:23,120
Speaker 1: because they don't want to have to deal with this

877
00:49:23,200 --> 00:49:26,240
Speaker 1: kind of crap, and I don't blame them. Cheap, crass

878
00:49:26,440 --> 00:49:30,000
Speaker 1: quote jokes like this should not be the cost of

879
00:49:30,080 --> 00:49:32,880
Speaker 1: serving as an elected official, but if you're a young woman,

880
00:49:33,040 --> 00:49:35,480
Speaker 1: especially a woman of color, or a young queer person,

881
00:49:35,520 --> 00:49:39,240
Speaker 1: a young trans person, it absolutely is. And Elon Musk

882
00:49:39,440 --> 00:49:42,520
Speaker 1: obviously does not give a shit about his responsibility to

883
00:49:42,520 --> 00:49:45,879
Speaker 1: make Twitter a place where this is not commonplace. In fact,

884
00:49:46,200 --> 00:49:49,880
Speaker 1: he's so pathetic that it's probably like feeding his ego

885
00:49:49,960 --> 00:49:52,200
Speaker 1: the chance to be able to pretend, like even for

886
00:49:52,280 --> 00:49:55,319
Speaker 1: a second, that there is a reality where somebody like

887
00:49:55,360 --> 00:49:59,600
Speaker 1: AOC would even ever potentially think about him, potentially have

888
00:49:59,600 --> 00:50:01,680
Speaker 1: a chance with him. And that is the real joke here.

889
00:50:01,719 --> 00:50:04,520
Speaker 1: The real joke is on Elon Musk, because he's clearly

890
00:50:05,480 --> 00:50:08,920
Speaker 1: enjoying having his egostroked by even the idea that of

891
00:50:08,960 --> 00:50:11,719
Speaker 1: pretend AOC would ever give him the time of day,

892
00:50:11,800 --> 00:50:15,200
Speaker 1: which would never happen. So it's not all bad. I

893
00:50:15,200 --> 00:50:19,560
Speaker 1: do have a little positive news, what a little positive

894
00:50:19,600 --> 00:50:23,200
Speaker 1: news for you, and that is Apple has just unveiled

895
00:50:23,239 --> 00:50:27,240
Speaker 1: their new iPhone accessibility features, which I'm very excited about. Okay,

896
00:50:27,280 --> 00:50:30,600
Speaker 1: so May eighteenth with Global Accessibility Awareness Day, and to

897
00:50:30,640 --> 00:50:34,840
Speaker 1: celebrate Apple unveiled a suite of new features to improve cognitive,

898
00:50:35,040 --> 00:50:39,399
Speaker 1: vision and speech accessibility. We should see these features rolled

899
00:50:39,440 --> 00:50:42,320
Speaker 1: out on iPhone, iPad, and Mac later this year. Apple

900
00:50:42,360 --> 00:50:45,040
Speaker 1: created these tools based on feedback from a broad subsection

901
00:50:45,080 --> 00:50:47,480
Speaker 1: of users with disabilities. Let's take a look at what

902
00:50:47,600 --> 00:50:49,799
Speaker 1: is to come. So for users who are blind or

903
00:50:49,800 --> 00:50:52,959
Speaker 1: have low vision, they have detection Mode and Magnifier which

904
00:50:53,200 --> 00:50:56,600
Speaker 1: now offers point and Speak, which identifies texts that users

905
00:50:56,640 --> 00:50:58,400
Speaker 1: point toward and reads it out loud to help them

906
00:50:58,400 --> 00:51:01,840
Speaker 1: interact with physical objects, which just household appliances, which I

907
00:51:01,840 --> 00:51:04,879
Speaker 1: think is great. And they also have a much more

908
00:51:05,000 --> 00:51:08,520
Speaker 1: simplified grid home screen, so if you're somebody who doesn't

909
00:51:08,560 --> 00:51:11,480
Speaker 1: necessarily need a half a dozen apps on your home screen,

910
00:51:11,760 --> 00:51:13,480
Speaker 1: you just want to be able to like push one

911
00:51:13,480 --> 00:51:15,480
Speaker 1: button and make a phone call, it's a lot simpler.

912
00:51:15,719 --> 00:51:18,200
Speaker 1: So I love this because you know, I'm sure I've

913
00:51:18,200 --> 00:51:21,080
Speaker 1: talked about this on the show before, but accessibility tools

914
00:51:21,160 --> 00:51:24,440
Speaker 1: don't just benefit people with disabilities, they benefit everybody because

915
00:51:24,440 --> 00:51:26,920
Speaker 1: we're all better served when more folks can show up

916
00:51:26,960 --> 00:51:31,640
Speaker 1: online and in technology. My parents are both huge iPhone people.

917
00:51:31,840 --> 00:51:34,800
Speaker 1: They love their iPhone. They always get the newest one

918
00:51:35,080 --> 00:51:37,160
Speaker 1: my dad is disabled and after he had a stroke,

919
00:51:37,320 --> 00:51:40,719
Speaker 1: he had to kind of relearn how to do a

920
00:51:40,719 --> 00:51:42,520
Speaker 1: lot of things that he was in physical therapy to

921
00:51:42,600 --> 00:51:44,000
Speaker 1: re learn how to walk, he had to learn how

922
00:51:44,040 --> 00:51:47,920
Speaker 1: to drive. And one of the most complicated things or

923
00:51:47,960 --> 00:51:51,440
Speaker 1: difficult thing for him to relearn was technology. I don't know,

924
00:51:51,680 --> 00:51:53,560
Speaker 1: I don't know why it was so difficult, but like

925
00:51:54,000 --> 00:51:57,120
Speaker 1: it was a real challenge. Like there's so much technology

926
00:51:57,320 --> 00:51:59,600
Speaker 1: right now and it can be so complicated. And I

927
00:51:59,600 --> 00:52:04,200
Speaker 1: think they like the simplified phone grid home screen will

928
00:52:04,200 --> 00:52:06,880
Speaker 1: be a real hit with him because he's not somebody

929
00:52:06,920 --> 00:52:10,759
Speaker 1: who is, like, you know, doing a million things on

930
00:52:10,800 --> 00:52:13,040
Speaker 1: his phone. He wants to text and he wants to

931
00:52:13,080 --> 00:52:15,279
Speaker 1: make phone calls, and so having it be a lot

932
00:52:15,320 --> 00:52:18,520
Speaker 1: more simplified, I think will be really good. Technology has

933
00:52:19,080 --> 00:52:21,640
Speaker 1: really helped both of my parents just be able to

934
00:52:22,080 --> 00:52:24,560
Speaker 1: show up more. You know, my dad's Apple Watch, it

935
00:52:24,640 --> 00:52:28,640
Speaker 1: monitors his vital signs. My mom facetimes her niece every

936
00:52:28,680 --> 00:52:31,960
Speaker 1: single day, sometimes multiple times a day. I'll say, it

937
00:52:32,040 --> 00:52:36,040
Speaker 1: is not without friction in our healthhold because my mom

938
00:52:36,840 --> 00:52:40,759
Speaker 1: loves FaceTime and no longer uses just regular, good old

939
00:52:40,800 --> 00:52:44,520
Speaker 1: fashioned phone calls, and so every time that we speak.

940
00:52:44,640 --> 00:52:47,359
Speaker 1: She expects it to be FaceTime, and I don't really

941
00:52:47,400 --> 00:52:50,200
Speaker 1: like FaceTime. I don't enjoy being on FaceTime. It's not

942
00:52:50,239 --> 00:52:52,880
Speaker 1: how I want to have every conversation. So it's not

943
00:52:52,960 --> 00:52:56,439
Speaker 1: it's not without friction, but it's been it's been great

944
00:52:56,480 --> 00:52:58,960
Speaker 1: for them, And I also think it's like it's just

945
00:52:58,960 --> 00:53:03,400
Speaker 1: a good I like tech being designed with accessibility at

946
00:53:03,440 --> 00:53:05,880
Speaker 1: its core, because it is very easy to think of

947
00:53:05,880 --> 00:53:08,920
Speaker 1: technology as like just cutting it stuff for young people,

948
00:53:09,280 --> 00:53:12,360
Speaker 1: but that can be so limiting, you know, when tech

949
00:53:12,640 --> 00:53:16,400
Speaker 1: should be designed with slash four people who need it.

950
00:53:16,440 --> 00:53:19,000
Speaker 1: And I think it's really cool to see technology being

951
00:53:19,040 --> 00:53:22,239
Speaker 1: made that centers older folks and folks with accessibility needs.

952
00:53:22,239 --> 00:53:24,080
Speaker 1: I think that's really really cool, and I think it

953
00:53:24,120 --> 00:53:27,000
Speaker 1: really reminds us like who tech is supposed to serve

954
00:53:27,120 --> 00:53:30,360
Speaker 1: and who should be centered in the technology that we

955
00:53:30,480 --> 00:53:32,879
Speaker 1: use every day. So I love this. I think it's great.

956
00:53:33,440 --> 00:53:37,239
Speaker 2: This is really nice. Thank you for bringing this to

957
00:53:37,320 --> 00:53:40,920
Speaker 2: us because you're a yeah, this is such a nice story.

958
00:53:41,000 --> 00:53:42,960
Speaker 2: Thank you for sharing it. It's so nice to see

959
00:53:43,000 --> 00:53:47,399
Speaker 2: technology doing what it does when it's at its best,

960
00:53:47,440 --> 00:53:51,200
Speaker 2: which is helping people who either have a disability or

961
00:53:51,960 --> 00:53:56,120
Speaker 2: some sort of need meet that need and be able

962
00:53:56,160 --> 00:53:58,399
Speaker 2: to use it as a tool to help them do

963
00:53:58,440 --> 00:54:01,400
Speaker 2: the things that they want to do. That is uh,

964
00:54:01,719 --> 00:54:03,120
Speaker 2: that's about as good as it gets.

965
00:54:02,880 --> 00:54:07,280
Speaker 1: With tech and added bonus with a simplified iPhone screen,

966
00:54:07,400 --> 00:54:09,840
Speaker 1: maybe my parents won't be calling me every five seconds

967
00:54:09,880 --> 00:54:12,480
Speaker 1: asking me to do things on their phone. So way

968
00:54:12,520 --> 00:54:16,080
Speaker 1: to foster a sense of independence for my parents. Specifically,

969
00:54:16,440 --> 00:54:17,240
Speaker 1: Thank you Apple.

970
00:54:19,320 --> 00:54:21,719
Speaker 2: Yeah, well that's what it's all about. That's why you've

971
00:54:21,719 --> 00:54:24,839
Speaker 2: been doing this show to get us here so your

972
00:54:24,880 --> 00:54:26,520
Speaker 2: parents stop asking you for tech support.

973
00:54:26,680 --> 00:54:29,719
Speaker 1: Yes, we did listen you. I don't know if they

974
00:54:29,760 --> 00:54:32,360
Speaker 1: give you a similar situation, but when you visit your parents,

975
00:54:32,400 --> 00:54:35,240
Speaker 1: it's like you'll have the dinner, you'll have the drink, whatever,

976
00:54:35,520 --> 00:54:38,040
Speaker 1: and then there's like a lull in the conversation and

977
00:54:38,360 --> 00:54:40,640
Speaker 1: tech support time starts where it's just like, oh, I've

978
00:54:40,640 --> 00:54:43,399
Speaker 1: got this problem with my computer, Like, oh, the the

979
00:54:43,440 --> 00:54:45,399
Speaker 1: Internet is being weird. I need you to fix it.

980
00:54:45,520 --> 00:54:48,640
Speaker 1: Our what's our Wi Fi password? XC five three four

981
00:54:48,760 --> 00:54:51,480
Speaker 1: beat beat five four, beat me blake, like make it

982
00:54:51,560 --> 00:54:57,960
Speaker 1: something you gonna remember and say.

983
00:54:55,560 --> 00:55:00,480
Speaker 2: In my with my parents, we have really establish some

984
00:55:00,560 --> 00:55:06,520
Speaker 2: good boundaries around my providing tech support. It took us

985
00:55:06,560 --> 00:55:08,839
Speaker 2: a while to get here. It was a bumpy road,

986
00:55:09,400 --> 00:55:12,439
Speaker 2: but yeah, I empathize, and so maybe Apple will solve

987
00:55:12,440 --> 00:55:14,280
Speaker 2: all those problems for you and your parents.

988
00:55:14,440 --> 00:55:16,640
Speaker 1: Let that be a lesson to you listeners. If you

989
00:55:17,520 --> 00:55:20,040
Speaker 1: are struggling with parents that need a lot of tech support,

990
00:55:20,239 --> 00:55:20,880
Speaker 1: it gets better.

991
00:55:21,360 --> 00:55:24,759
Speaker 2: Yeah, all you need to do is create a podcast

992
00:55:24,840 --> 00:55:30,040
Speaker 2: about the future technology and for seasons in Apple will

993
00:55:30,080 --> 00:55:33,040
Speaker 2: release a feature that helps your parents.

994
00:55:34,520 --> 00:55:36,719
Speaker 1: You know, it's an easy it's not easy, but you

995
00:55:36,760 --> 00:55:39,600
Speaker 1: know it's it's it's it's a template. Yeah.

996
00:55:39,640 --> 00:55:42,040
Speaker 2: Thanks, so yeah, thanks for having me here on the

997
00:55:42,120 --> 00:55:44,600
Speaker 2: last ever episode of their No Girls on the Internet.

998
00:55:44,840 --> 00:55:46,759
Speaker 2: You can back it up and go home, bop it

999
00:55:47,480 --> 00:55:48,120
Speaker 2: get all right.

1000
00:55:48,160 --> 00:55:50,560
Speaker 1: Well, thanks for being here, Mike, and thanks for listening.

1001
00:55:50,600 --> 00:55:53,399
Speaker 1: If you want to hear more content like this ad free,

1002
00:55:53,920 --> 00:55:57,279
Speaker 1: please subscribe to our patreon their stuff there. It's cool.

1003
00:55:57,360 --> 00:56:00,000
Speaker 1: I promise. I'm trying to message everybody who has subscribed

1004
00:56:00,040 --> 00:56:01,840
Speaker 1: Vie personally. If you've got a message from me, it

1005
00:56:01,920 --> 00:56:05,720
Speaker 1: is actually from me sitting in my computer in my apartment,

1006
00:56:06,120 --> 00:56:09,000
Speaker 1: thanking you for subscribing. Thank you for being there. Patreon

1007
00:56:09,040 --> 00:56:11,560
Speaker 1: dot com slash tangody. We will see you next week.

1008
00:56:16,760 --> 00:56:18,839
Speaker 1: If you're looking for ways to support the show, check

1009
00:56:18,880 --> 00:56:21,480
Speaker 1: out our merch store at tangody dot com. Slash Store.

1010
00:56:22,719 --> 00:56:24,719
Speaker 1: Got a story about an interesting thing in tech, or

1011
00:56:24,800 --> 00:56:26,680
Speaker 1: just want to say hi? You can reach us at

1012
00:56:26,680 --> 00:56:29,400
Speaker 1: Hello at tangody dot com. You can also find transcripts

1013
00:56:29,400 --> 00:56:31,799
Speaker 1: for today's episode at TENG Goody dot com. There are

1014
00:56:31,840 --> 00:56:33,279
Speaker 1: no girls on the Internet. What was created by me,

1015
00:56:33,440 --> 00:56:36,600
Speaker 1: Bridget Tod. It's a production of iHeartRadio, an unboss creative

1016
00:56:37,120 --> 00:56:40,720
Speaker 1: edited by Joey Pat Jonathan Strickland as our executive producer.

1017
00:56:41,000 --> 00:56:44,280
Speaker 1: Tari Harrison is our producer and sound engineer. Michael Amado

1018
00:56:44,360 --> 00:56:47,319
Speaker 1: is our contributing producer. I'm your host, Bridget Todd. If

1019
00:56:47,360 --> 00:56:49,080
Speaker 1: you want to help us grow, rate and review us

1020
00:56:49,080 --> 00:56:52,719
Speaker 1: on Apple Podcasts. For more podcasts from iHeartRadio, check out

1021
00:56:52,719 --> 00:56:55,680
Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.