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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 Bridget Todd, and this

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Speaker 1: is there Are No Girls on the Internet. You're listening

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Speaker 1: to their No Girls on the Internet for we explore

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Speaker 1: the intersection of technology, social media, and identity.

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Speaker 2: And this is another installment.

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Speaker 1: Of our weekly news Roundup, where we round up all

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Speaker 1: the stories online that you might have missed so you

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Speaker 1: don't have to. Producer Mike, I think we have to

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Speaker 1: gear up for a Trump tantrum incoming because Trump has

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Speaker 1: been saying for the longest time that he feels that

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Speaker 1: he deserves the Nobel Peace Prize. I guess the people

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Speaker 1: who decide who deserves a Nobel Peace Prize and who

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Speaker 1: doesn't disagreed because he didn't win.

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Speaker 3: He did not win, and also he's like the only

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Speaker 3: person on earth who is aggressively campaigning to win the

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Speaker 3: Nobel Peace Prize, like it's so shameless.

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Speaker 1: We were talking about this before we started recording, but

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Speaker 1: there was a time where most people did not care

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Speaker 1: nor even know who the Nobel Peace Prize winners were.

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Speaker 1: I looked up the list this morning of the last

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Speaker 1: the people who had won it. For the last thirty years.

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Speaker 1: I maybe I'm uninformed, but I only knew a handful

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Speaker 1: of people on that list for the last thirty years.

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Speaker 1: One of them, notably Barack Obama, did win it. I

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Speaker 1: know who Barack Obama is, so anybody who tells you

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Speaker 1: otherwise is misinformed. However, you have these people who were

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Speaker 1: acting like they were very invested in who wins the

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Speaker 1: Nobel Peace Prize all along.

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Speaker 2: No you weren't. You just want to be a Trump

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Speaker 2: sick a fan. No you weren't.

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Speaker 3: Yeah, when we looked at that list, it really underscored

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Speaker 3: to me how American centric our news bubble is. Like

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Speaker 3: I like to think of myself as a pretty informed guy,

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Speaker 3: but I didn't know like most of those people, and

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Speaker 3: Donald Trump certainly did not.

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Speaker 2: No, of course not so.

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Speaker 1: The Nobel Peace Prize is actual recipient is Venezuelan opposition

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Speaker 1: leader Maria Karinado. It owns like a very deserved win.

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Speaker 1: I did love this post on Blue Sky from Kieran Heally.

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Speaker 1: White House staffs should just make and show Trump an

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Speaker 1: AI generated video where he wins. Fly him to Duloof

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Speaker 1: and tell him it's Oslo and dare the New York

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Speaker 1: Times to say anything more than experts disagree about whether

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Speaker 1: or not mister Trump was award at the Nobel Prize.

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Speaker 3: It's such a good solution because I know he would

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Speaker 3: fall for it, and I know the New York Times

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Speaker 3: would go along with it, like they wouldn't even need

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Speaker 3: to be dared. That would just be their default thing

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Speaker 3: that they would write.

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Speaker 2: No, and absolutely one hundred percent for short.

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Speaker 1: It would turn into a conversation where who's to say

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Speaker 1: what the truth is? You know, this underscores this fractured

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Speaker 1: reality that we're living in where some people say this,

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Speaker 1: some people say that, and there's no expectation of finding

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Speaker 1: the actual truth anymore.

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Speaker 2: I genuinely feel that.

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Speaker 3: No, and there would definitely be a New York Times

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Speaker 3: opinion piece about how like the truth is somewhere in

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Speaker 3: the middle. But I do love this so much. So

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Speaker 3: as we're recording this, the news was just announced a

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Speaker 3: few hours ago, so I don't believe Trump has officially

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Speaker 3: reacted yet or posted on his social media slash crypto platform.

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Speaker 3: I can only assume he is waking up and throwing

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Speaker 3: things around in the Oval office in a rage, which

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Speaker 3: is a wonderful image. But I just love this high

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Speaker 3: level trolling from the Nobel Committee because after all of

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Speaker 3: Trump's bluster and like demands that he win, they chose

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Speaker 3: to highlight this democracy activist of a country that Trump

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Speaker 3: is like murdering their citizens and threatening and perhaps actively

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Speaker 3: gearing up for war with But like, it's really messed

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Speaker 3: up what the Trump administration is doing with Venezuela, and

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Speaker 3: it's really scary. And I love so much that the

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Speaker 3: Noble Committee has lifted up this democracy activist instead of

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Speaker 3: giving into Trump yes.

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Speaker 1: And speaking of this idea that if the White House

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Speaker 1: just told Trump that he won, you wouldn't really have

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Speaker 1: people disputing that. Probably in legacy media they would be saying, oh,

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Speaker 1: some people say he did, some people say he didn't.

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Speaker 1: That reminds me so much of this new open AI

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Speaker 1: platform sora ai, their tool that lets anybody make hyper

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Speaker 1: realistic AI videos.

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Speaker 2: From text prompts.

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Speaker 1: It's kind of like Google's platform vo three, which we've

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Speaker 1: talked about extensively on the podcast. Have you seen any

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Speaker 1: of these sora ai videos on social media or have

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Speaker 1: you played with it yourself yet?

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Speaker 3: I have not played with it myself. I'm actually curious

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Speaker 3: to but because we make this show, but I believe

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Speaker 3: it's still invite only, so they're using that exclusivity to gatekeep.

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Speaker 3: And so I've seen a few videos but have not

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Speaker 3: directly used it myself yet.

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Speaker 1: So I have also not used it myself, and generally

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Speaker 1: I try to not give my opinion on consumer tech

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Speaker 1: that I have not personally played with. I've seen a

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Speaker 1: couple of videos floating around. I will say this, some people,

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Speaker 1: for whatever reason, do seem to like it. They seem

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Speaker 1: to be enjoying it. I've seen people creating videos using

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Speaker 1: Sora and.

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Speaker 2: Then posting it on social media.

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Speaker 1: And something that makes Sora unique is that it has

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Speaker 1: a feature where people scan in their face or their friends'

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Speaker 1: faces and then put them in these AI.

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Speaker 2: Generated scenarios, which we'll talk more about in a second.

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Speaker 1: So I have a few friends, people that I trust,

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Speaker 1: who have posted Sora ai content and they were like, Oh,

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Speaker 1: it's so fun.

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Speaker 2: I'm having so much fun on this platform. So I

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Speaker 2: don't know.

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Speaker 1: I have not played with it myself, and maybe I

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Speaker 1: will eat my words, but I have seen people say

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Speaker 1: this platform is so fun and it's such a joyful

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Speaker 1: experience that it's going to rock social media.

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Speaker 2: And I just don't see it. I really just don't

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Speaker 2: see it again. Maybe I'll eat my words.

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Speaker 1: I'm happy to take this back if it turns out

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Speaker 1: that I'm incorrect. But it really reminds me of a

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Speaker 1: few years ago when a lot of folks are posting

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Speaker 1: those AI generated futuristic looking selfies on Facebook a few

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Speaker 1: years ago. I think the reason why people who are

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Speaker 1: using it are saying it's so fun, they're just excited

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Speaker 1: to see themselves and their friends in AI generated scenarios.

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Speaker 2: Do you know what I mean?

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Speaker 1: I don't think that that necessarily means that the kind

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Speaker 1: of content that is coming out of these platforms are interesting, unique,

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Speaker 1: the kind of thing that's actually going to hold someone's attention.

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Speaker 1: I just think people are excited because, hey, that's me

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Speaker 1: and my friend in a scenario. It almost reminds me

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Speaker 1: of you might remember.

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Speaker 2: This, do you ever?

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Speaker 3: Yik Yak I do from like the early Internet, Like

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Speaker 3: we're talking early two thousands, right, Yeah, So for.

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Speaker 1: Folks who don't remember yik yak, was this app from

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Speaker 1: I want to say twenty thirteen.

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Speaker 2: Oh apparently it just relaunched in twenty twenty one, so

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Speaker 2: maybe we can get back on it.

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Speaker 1: But you would take a picture of your face and

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Speaker 1: you can put it in these animated cards. So I

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Speaker 1: remember making one for Christmas where it was my face

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Speaker 1: and my best friend Kristen's face in the in like

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Speaker 1: dancing animated elf. So it looks it looks like our

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Speaker 1: faces were on the bodies of elves and we were dancing,

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Speaker 1: and we sent it to all of our friends and

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Speaker 1: we thought it was just hilarious. That is genuinely what

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Speaker 1: this reminds me of. I think people are just excited

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Speaker 1: to see their own faces and the faces of their

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Speaker 1: friends in these AI generated scenarios. When people post them

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Speaker 1: on social media, they post in the handful of posts

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Speaker 1: that I've seen people post them saying oh this is

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Speaker 1: so incredible?

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Speaker 2: Can you believe this? Can you believe this?

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Speaker 1: And it just reminds me of when people post super

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Speaker 1: hyper specific to them content on social media, and of

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Speaker 1: course they like it because it's about them.

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Speaker 2: But will other people like it? I'm not so sure.

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Speaker 3: Yeah, I think you're absolutely right that it probably doesn't

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Speaker 3: have a lot of staying power because, for one thing,

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Speaker 3: it's just like the novelty of like, oh look, I

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Speaker 3: can put myself in an AI generated video. It reminds

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Speaker 3: me of something that one of my friends, who is

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Speaker 3: a visual artists, told me about self portraits one time,

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Speaker 3: how he considers them to be like one of the

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Speaker 3: laziest forms of art. You can probably guess which friend

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Speaker 3: this is, because it's just it's just a subject that

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Speaker 3: is always there.

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Speaker 2: If you don't have any other.

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Speaker 3: Ideas, you can do it. It's just like yourself. And

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Speaker 3: I feel that that's a pretty good analogy for this

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Speaker 3: kind of tech that is just like, hey, look it's

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Speaker 3: a picture of you.

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Speaker 2: This is like a mirror, but there's some.

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Speaker 3: Cool AI wizardry behind it.

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Speaker 2: Well, don't tell that to free to Collo, but sure

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Speaker 2: that's true. Yeah, it wasn't my opinion, it was his.

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Speaker 2: I know exactly the person that you're talking about. Also,

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Speaker 2: I know that's true.

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Speaker 3: Should next time I see him, I'm going to ask

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Speaker 3: him about freedom.

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Speaker 2: Anyway, we digress.

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Speaker 1: So I've actually seen tech tools that both remove and

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Speaker 1: add a Sora ai watermark to any video, whether or

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Speaker 1: not it's AI and whether or not it's made with

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Speaker 1: sora Because right now, when you make these Sora ai videos,

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Speaker 1: there's a watermark that kind of moves around that's kind

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Speaker 1: of meant to be a way for anybody to see

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Speaker 1: it and say, hey, that's AI, even though it has

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Speaker 1: not stopped people from commenting on videos that have that

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Speaker 1: Sora AI watermark as if these videos are real, which again,

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Speaker 1: I think it goes to show that it's not just

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Speaker 1: that these AI videos are hard to tell that they

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Speaker 1: are AI, which it is hard to tell.

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Speaker 2: And it's not just that they're everywhere, it's that.

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Speaker 1: We live in this climate that is completely eroded our

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Speaker 1: trust in everything that we see online.

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Speaker 2: And I know people are having fun with it. I

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Speaker 2: get it. I don't want to be a party pooper,

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Speaker 2: but I just don't love it.

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Speaker 3: I mean, I'll be a party pooper. I feel that

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Speaker 3: it's like kind of my role on this show.

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Speaker 2: Yeah.

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Speaker 3: I think all those problems you mentioned are also compounded

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Speaker 3: by the add nature of social media, where everything is

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Speaker 3: like fast cuts, super short form videos. A lot of

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Speaker 3: videos are only a few seconds long, and no surprise,

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Speaker 3: people when they're scrolling through videos are not taking the

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Speaker 3: time to critically evaluate is this real or not before commenting,

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Speaker 3: which is unfortunate and we should do that, but let's

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Speaker 3: be honest, a lot of people don't. I'm certainly guilty

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Speaker 3: of the same, and the algorithms in a lot of cases,

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Speaker 3: in the platforms the way they're designed, do everything they

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Speaker 3: can to encourage people to not critically evaluate. They just

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Speaker 3: want to connect with feelings and emotions to keep people

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Speaker 3: clicking and engaged.

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Speaker 1: Yes, and you know who is really raising the alarm

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Speaker 1: about that right now? Family members are famous dead people

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Speaker 1: because Sara lets anybody use the likeness of anyone public

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Speaker 1: figures copyrighted characters whoever. So to back up a little bit,

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Speaker 1: open AI's first position was that anybody who objected needed

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Speaker 1: to opt out of having their likeness used in this

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Speaker 1: way on Sora, which to me is wild and truly

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Speaker 1: not how consent works in any of their context, where

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Speaker 1: you would have to object and opt out otherwise is

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Speaker 1: just understood that you've given your consent. But eventually, as

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Speaker 1: ours technical reports, open Ai was forced to change the

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Speaker 1: way that Sora handles fictional copyrighted works. Open Ai CEO

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Speaker 1: Sam Autman wrote this weekend that copyright holders now have

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Speaker 1: to opt in to allow their characters in Sora to videos,

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Speaker 1: rather than opting out as it was when the service

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Speaker 1: first launched, and those folks might share in some of

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Speaker 1: the revenue from any Sora videos of their characters. So

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Speaker 1: open ai calls these cameos, which I do think is

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Speaker 1: a pretty kind of genius bit of marketing. This lets

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Speaker 1: living users, including public figures, opt into Sora's cameo feature

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Speaker 1: by scanning their own face with their phone to drop

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Speaker 1: themselves into any Sora scene with remarkable fidelity. Open ai

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Speaker 1: says that cameo users are quote in control of your

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Speaker 1: likeness end to end, and the feature is designed quote

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Speaker 1: to ensure that your audio and image likeness are used

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Speaker 1: with your consent. Folks might have seen that Logan Paul

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Speaker 1: opted in, and since then there's been.

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Speaker 2: A blood of ai videos of.

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Speaker 1: Him coming out as gay, which he actually spoke up

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Speaker 1: and said, oh, I don't love these videos. They're kind

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Speaker 1: of causing me issues, but I guess he opted into them,

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Speaker 1: So what are you gonna do. Obviously, dead public figures

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Speaker 1: cannot consent to opt into this future, and they're all

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Speaker 1: over the platform. When OpenAI was asked about this, they

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Speaker 1: basically said, yeah, that's the situation. So already I have

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Speaker 1: seen Sora ai generated videos of things like Stephen Hawking

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Speaker 1: in his wheelchair, but he's doing skateboard tricks on a

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Speaker 1: skateboard ramp or Kurt Cobain from Nirvana eating a bunch

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Speaker 1: of KFC chicken fingers. I don't know why one of

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Speaker 1: the early use cases in this is bringing back dead celebrities,

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Speaker 1: but there you have it, and that's also how you

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Speaker 1: end up with content like AI generated Martin Luther King

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Speaker 1: giving his I have a dream speech at a podium,

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Speaker 1: but instead of words, he's making racist monkey noises. I

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Speaker 1: have seen eight ton of MLK AI generated content being

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Speaker 1: made with Sora. I saw content where he was eating

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Speaker 1: a crab boil with Malcolm X. I saw content where

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Speaker 1: instead of giving his speech, he gives that do you

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Speaker 1: know that meme of the kid who is saying, do

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Speaker 1: you ever believe you think you dream that you think

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Speaker 1: you could do anything?

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Speaker 4: Have you ever had a dream that you you had

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Speaker 4: your you you could, You'll do?

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Speaker 2: You want you you could do so, you you'll do?

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Speaker 2: You can?

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Speaker 5: You you want?

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Speaker 2: You want them?

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Speaker 1: Where when Martin Luther King gets up to give this speech,

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Speaker 1: that's what he says. And the wildest thing to me

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Speaker 1: is that Bernie's King. Mlk's daughter says that people send

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Speaker 1: her these videos not necessarily to try to troll her,

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Speaker 1: because they don't like her, they send her these videos

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Speaker 1: because they think she might like it. And Zelda Williams,

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Speaker 1: daughter of the actor Robin Williams, who died by suicide,

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Speaker 1: shared that she also doesn't like this, that people will

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Speaker 1: send her these AI generated videos of her late father,

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Speaker 1: maybe thinking that she will like it or get a kick.

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Speaker 2: Out of it.

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Speaker 1: In a statement, she said, please just stop sending me

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Speaker 1: AI videos of dad. Stop believing I want to see it,

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Speaker 1: or that I'll understand. I don't and I won't if

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Speaker 1: you're trying to troll me. I've seen way worse. I'll

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Speaker 1: restrict and move on. But please, if you've got any decency,

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Speaker 1: just stop doing this to him and to me, to everyone,

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Speaker 1: even full stop.

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Speaker 2: It's dumb.

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Speaker 1: It's a waste of time and energy. And believe me,

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Speaker 1: it's not what he would want to watch. The legacies

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Speaker 1: of real people be condensed down to this vaguely looks

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Speaker 1: and sounds like them.

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Speaker 2: So that's enough.

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Speaker 1: Just so people can turn out horrible TikTok slop puppeteering

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Speaker 1: them is maddening. You're not making art, You're making disgusting

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Speaker 1: overprocessed hot dogs out of the lives of human beings.

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Speaker 1: Out of history and art and music and then shoving

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Speaker 1: them down someone else's throat hoping.

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Speaker 2: They'll give you a little thumbs up. And like it.

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Speaker 1: Growth AI is just badly recycling and regurgitating the past

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Speaker 1: to be consumed.

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Speaker 2: You are taking in the human centipede of content. And

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Speaker 2: from the very very end of the line, all of

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Speaker 2: wall folks that front laugh and laugh, consume and consume.

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Speaker 2: That is a.

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Speaker 3: Powerful condemnation of AI. And she's not wrong. It's really

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Speaker 3: pretty gross that this is like one of the big

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Speaker 3: use cases of this technology. And you mentioned that in

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Speaker 3: response to threatened copyright lawsuits, Open ai change their policy

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Speaker 3: to require copyrighted characters to be opted in. And you know,

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Speaker 3: that seems all well and good for the owners of

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Speaker 3: the copyrights, but that policy is doing nothing to protect

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Speaker 3: the descendants of these recently deceased public figures like Zelda

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Speaker 3: Williams or Steve Irwin's kids. And I think that's a

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Speaker 3: pretty good example of the inhumanity of these tech companies

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Speaker 3: and AI companies in particular, that they'll go out of

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Speaker 3: their way to protect copyrighted assets before protecting real humans.

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Speaker 3: I can't help but think of that new law they

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Speaker 3: have in Denmark to try to push back against this

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Speaker 3: that grants every living person in the country copyright to

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Speaker 3: their likeness as a continues to blur the line between fiction.

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Speaker 2: And real life. I remain hopeful.

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Speaker 3: I guess that that kind of solution to protecting people

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Speaker 3: is gonna be viable.

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Speaker 1: Yeah, and I have to add this is a little

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Speaker 1: bit of a tangent, but when my mom passed away

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Speaker 1: last year, was my mom was somewhat of a public figure.

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Speaker 2: She was definitely a public figure in our town.

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Speaker 1: And I don't know, I think that people really need

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Speaker 1: to understand what it feels like when you are grieving someone,

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Speaker 1: someone that you had an intimate, private relationship with that

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Speaker 1: nobody else in the world had. Even though people might

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Speaker 1: think that they had an intimate relationship with that person,

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Speaker 1: they didn't. They you know, they might have liked them,

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Speaker 1: or enjoyed their work, or respected them, whatever. When your

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Speaker 1: loved one, your deceased loved one, when their image and

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Speaker 1: likeness and story is taken in that way, it just

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Speaker 1: really hurts.

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Speaker 2: And it's so confusing.

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Speaker 1: And the fact that there's a dynamic where the people

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Speaker 1: who do this and the people that profit from it

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Speaker 1: think that you might like it just disgusts me. So

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Speaker 1: I've certainly never experienced this on the level of Zelda

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Speaker 1: Williams or Bernice King, but I know a little bit

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Speaker 1: about how invasive that feels, and then the feeling of

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Speaker 1: it being out of your control, right, the feeling of

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Speaker 1: I cannot stop this, I cannot stop the Internet. So

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Speaker 1: you know how they say like, once something is on

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Speaker 1: the Internet, it's on the Internet forever. Imagine that feeling

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Speaker 1: when the intimacy and the privacy of somebody that you

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Speaker 1: are grieving, somebody that you have lost, is taken and

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Speaker 1: you know there's nothing that you could do about it.

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Speaker 1: It's just out there in the ether because people decided

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Speaker 1: it belonged to them, and it doesn't. I think there's

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Speaker 1: really something so wrong about this. And then hearing people say, oh,

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Speaker 1: but the platform is a lot of fun I can

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Speaker 1: see myself and my friends, look, I made a video

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Speaker 1: where it looks like we're doing a funny thing. I

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Speaker 1: don't think that's a fair trade off, But I also

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Speaker 1: understand that we don't have a culture that asks people

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Speaker 1: to understand the stakes of that trade off, only understand

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Speaker 1: that if it happens to them, and I hope it

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Speaker 1: doesn't because it fucking sucks. So yeah, it's a little

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Speaker 1: hard for me to see people just talking about how

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Speaker 1: they don't care about any of those issues that you

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Speaker 1: just brought up, Mike, because this platform it's fun for

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Speaker 1: a laugh and it gives them a dopamine hand and

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Speaker 1: they can make a little bit of content with their

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Speaker 1: friends and get twenty likes on it or whatever.

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Speaker 3: Boy, Yeah, thanks for sharing that. You mentioned consent a

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Speaker 3: little bit earlier in this discussion, and I do think

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Speaker 3: that's a good framework to understand this where I get

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Speaker 3: the sense that a lot of these videos that people

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Speaker 3: are making are just for laughs or for trolling, which

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Speaker 3: is messed up in its own way. But it's really

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Speaker 3: interesting that some of these people sending these AI slop

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Speaker 3: videos to people that feature their deceased parents thinking that

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Speaker 3: they're going to like it. It just seems really interesting,

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Speaker 3: and that seems like something different than just fun and lolls.

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Speaker 3: It seems like this technology it's enabling the creators of

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Speaker 3: these view videos to be in relationship with the deceased person.

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Speaker 3: And you know, it's certainly not new that celebrities have

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Speaker 3: had fans where the fans want to be in relationship

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Speaker 3: with the person. And I think that's a big you know,

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Speaker 3: it's related to why people listen to podcasts. People like

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Speaker 3: to connect with the hosts that they like, and you know,

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Speaker 3: it can feel like a bit of a parasocial relationship,

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Speaker 3: and hosts like to connect with their fans as well,

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Speaker 3: but there's a separation there. I think there's a way

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Speaker 3: to look at this technology where it is a way

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Speaker 3: to non consensually break down that separation and be like, no,

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Speaker 3: we are in relationship whether you or your deceased parent

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Speaker 3: consent to it or not. And it's gross and people

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Speaker 3: shouldn't do.

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Speaker 2: It, and not for nothing.

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Speaker 1: I promise you that if Zelda Williams or Bernice King

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Speaker 1: wanted to use AI to make this kind of content

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Speaker 1: about their dead fathers.

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Speaker 2: They could.

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Speaker 1: You don't need to send it to They have access

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Speaker 1: to the same Internet that everybody else has access to.

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Speaker 2: If they wanted to make these videos, they could.

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Speaker 3: Yes, and making them with AI strips the uniqueness of

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Speaker 3: these figures that made them the exceptional public figures that

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Speaker 3: they are. Every time you run something through AI, it

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Speaker 3: just makes it feel right. Based on all of the

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Speaker 3: other data that was used to train the model and

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Speaker 3: strips away a little of that uniqueness. So in using

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Speaker 3: these people, these deceased figures, it diminishes them, which is

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Speaker 3: another part of what's I don't know, not great about it.

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Speaker 1: That's what when Zelda says that it's the human centipede

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Speaker 1: of content, that's exactly what she's referring to. I think,

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Speaker 1: and it's Yeah, now I've changed my mind. I don't

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Speaker 1: think Zora is good. I don't mind being a party

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Speaker 1: pooper about this one.

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Speaker 2: Not good. I don't like it.

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Speaker 5: Yeah, let's take a quick break at our back.

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Speaker 3: What else don't you like? Bridget Well, I don't love.

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Speaker 1: What Google is allegedly doing to their women in Tech initiatives.

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Speaker 1: So this is a little bit of a developing story.

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Speaker 1: Is Google dropping their Women Tech Makers initiatives? So I

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Speaker 1: saw this post in the Women in Tech sub read

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Speaker 1: it on Reddit it reads. I was part of Google's

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Speaker 1: initiative Women Tech Makers or WTM for short. Our job

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Speaker 1: was to organize events and help other women find their

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Speaker 1: way in the tech sphere. Occasionally we will be invited

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Speaker 1: to Google events with travel costs covered and merched. Nothing big,

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Speaker 1: but it was nice to be acknowledged while also supporting

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Speaker 1: women in STEM since the end of last year. Beginning

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Speaker 1: of this one, support was dwindling. First they stopped hosting webinars,

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Speaker 1: then made us request funding pre approved and would only

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Speaker 1: pay us back what they thought was reasonable and that

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Speaker 1: would be it spent our own money out of pocket.

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Speaker 1: The final blow is when they invited us to the

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Speaker 1: event in Europe and provided zero travel cost funding for us,

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Speaker 1: while Google Developers Group their other initiative, received what one

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Speaker 1: hundred percent of their travel paid for. Like that wasn't

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Speaker 1: bad enough, they didn't allow us to come to the

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Speaker 1: community mixer.

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Speaker 2: Women tech Makers was.

442
00:22:23,640 --> 00:22:27,160
Speaker 1: The only community not allowed to join the community event.

443
00:22:27,680 --> 00:22:31,120
Speaker 1: Now they straight kick us out of the platform, removing

444
00:22:31,160 --> 00:22:34,199
Speaker 1: all access to the resources and new home us to

445
00:22:34,240 --> 00:22:37,600
Speaker 1: some nonprofit painting it as a fantastic opportunity and how

446
00:22:37,600 --> 00:22:40,359
Speaker 1: cool it would be. Seven years of being an ambassador,

447
00:22:40,400 --> 00:22:43,399
Speaker 1: curated and organized more than forty events, including huge IT

448
00:22:43,720 --> 00:22:46,879
Speaker 1: conferences with four hundred plus attendees a team of thirteen women,

449
00:22:47,200 --> 00:22:50,320
Speaker 1: everything is now in shambles and demoralized because the Big

450
00:22:50,359 --> 00:22:53,000
Speaker 1: G decided that the community that was promoting and supporting

451
00:22:53,000 --> 00:22:56,320
Speaker 1: women in STEM is two DEI for their brand, so

452
00:22:56,400 --> 00:22:58,720
Speaker 1: that sounded pretty messed up to me. A lot of

453
00:22:58,760 --> 00:23:02,040
Speaker 1: the comments were essentially saying, yes, this has been the

454
00:23:02,119 --> 00:23:05,400
Speaker 1: vibe you know they have been, their support has been dwindling.

455
00:23:05,640 --> 00:23:07,280
Speaker 1: So I did some digging and I found a piece

456
00:23:07,320 --> 00:23:10,720
Speaker 1: that explains that Google officially did hand over their women

457
00:23:10,800 --> 00:23:14,800
Speaker 1: tech makers to a nonprofit called Technovation, a nonprofit that

458
00:23:14,840 --> 00:23:17,920
Speaker 1: says that they've met nearly twenty years empowering young innovators

459
00:23:17,920 --> 00:23:21,240
Speaker 1: to solve real world problems through tech and entrepreneurship. I

460
00:23:21,280 --> 00:23:25,040
Speaker 1: want to be clear that Technovation sounds good like this

461
00:23:25,119 --> 00:23:27,200
Speaker 1: is not to be smirch them at all. They seem

462
00:23:27,680 --> 00:23:31,320
Speaker 1: genuinely interested in supporting women tech makers, and it is

463
00:23:31,440 --> 00:23:33,560
Speaker 1: entirely possible that they're capable of doing more with this

464
00:23:33,600 --> 00:23:36,960
Speaker 1: program and taking this program further than Google ever did

465
00:23:37,040 --> 00:23:40,280
Speaker 1: or ever would, So I'm not trying to be smirch

466
00:23:40,400 --> 00:23:45,440
Speaker 1: them at all. But even the name Technovation, like taking

467
00:23:45,520 --> 00:23:49,359
Speaker 1: identity out of it specifically, is telling to me. And

468
00:23:49,480 --> 00:23:53,399
Speaker 1: I also really couldn't find this explained anywhere. If this

469
00:23:53,520 --> 00:23:57,359
Speaker 1: was really meant to be this fantastic thing, this fantastic

470
00:23:57,440 --> 00:24:01,200
Speaker 1: partnership with these great opportunities, like Google told the women

471
00:24:01,240 --> 00:24:03,520
Speaker 1: Tech Makers ambassadors that it was going to be for them.

472
00:24:03,920 --> 00:24:07,200
Speaker 1: I would then expect Google to make some comment about

473
00:24:07,240 --> 00:24:10,440
Speaker 1: it somewhere or announce it in on the Google blog. However,

474
00:24:11,160 --> 00:24:13,879
Speaker 1: it seems like Google is keeping this move a little

475
00:24:13,880 --> 00:24:16,920
Speaker 1: bit quiet. When you go to the Women Tech Makers site,

476
00:24:17,000 --> 00:24:20,520
Speaker 1: it now just says Women tech Makers is now with Technovation,

477
00:24:20,720 --> 00:24:24,080
Speaker 1: a trusted organization dedicated to empowering girls and women in STEM,

478
00:24:24,359 --> 00:24:27,359
Speaker 1: and then redirects you to the Technovation site in a

479
00:24:27,359 --> 00:24:30,439
Speaker 1: piece called Google Women tech Makers moves to Technovation. But

480
00:24:30,560 --> 00:24:35,240
Speaker 1: not everyone is cheering, it explains quote. For many longtime ambassadors,

481
00:24:35,280 --> 00:24:38,320
Speaker 1: this transition feels less like a celebration and more like

482
00:24:38,359 --> 00:24:41,800
Speaker 1: closure for something that had already been quietly fading. Several

483
00:24:41,840 --> 00:24:44,080
Speaker 1: members say the signs were there as early as late

484
00:24:44,080 --> 00:24:47,800
Speaker 1: twenty twenty four, with dwindling support, fewer sponsored events, and

485
00:24:47,880 --> 00:24:51,840
Speaker 1: an overall sense that Google's enthusiasm for the program was waning.

486
00:24:52,840 --> 00:24:57,080
Speaker 3: That is disappointing and just the way that this fits

487
00:24:57,119 --> 00:25:00,399
Speaker 3: so neatly with the larger trends of companies that aren't

488
00:25:00,440 --> 00:25:04,320
Speaker 3: just retreating from diversity commitments that they have made and

489
00:25:04,359 --> 00:25:09,639
Speaker 3: have supported for years, but seemingly like performatively running from

490
00:25:09,680 --> 00:25:13,479
Speaker 3: them to demonstrate that the Trump administration and their allies

491
00:25:13,520 --> 00:25:18,679
Speaker 3: how anti woke they are. It's just so disappointing and depressing.

492
00:25:18,880 --> 00:25:24,080
Speaker 3: But I guess it really shouldn't be surprising that, you know,

493
00:25:24,160 --> 00:25:29,640
Speaker 3: these giant tech companies were not as firmly behind those

494
00:25:29,680 --> 00:25:33,560
Speaker 3: commitments as they claimed to be. Back when that was

495
00:25:33,600 --> 00:25:35,000
Speaker 3: the in vogue thing to do.

496
00:25:35,680 --> 00:25:38,360
Speaker 1: Yes, and when it was the in vogue thing to do,

497
00:25:38,560 --> 00:25:41,720
Speaker 1: they did it with such fanfare, such pr pushes. They

498
00:25:41,760 --> 00:25:44,439
Speaker 1: really wanted a lot of attention for doing this, and

499
00:25:44,520 --> 00:25:47,080
Speaker 1: now that they're retreating, they want to do it quietly,

500
00:25:47,440 --> 00:25:50,600
Speaker 1: And I just I don't think that's fair. If you

501
00:25:51,119 --> 00:25:53,080
Speaker 1: said it with your full chest when it was in

502
00:25:53,200 --> 00:25:55,280
Speaker 1: vogue that you were doing this, say it with your

503
00:25:55,280 --> 00:25:58,680
Speaker 1: full chest that you are abandoning these commitments that you no.

504
00:25:58,680 --> 00:26:00,960
Speaker 2: Longer want to do that, and that's what you're going

505
00:26:01,040 --> 00:26:03,560
Speaker 2: to be doing. I don't like that they're doing it quietly.

506
00:26:03,600 --> 00:26:04,960
Speaker 2: It feels a bit sneaky to me.

507
00:26:05,400 --> 00:26:07,840
Speaker 1: And Google can say whatever they want, but I do,

508
00:26:08,000 --> 00:26:10,560
Speaker 1: as you said, think it's a clear symbol of how

509
00:26:10,920 --> 00:26:14,840
Speaker 1: vocally championing women and other marginalized folks in technology is

510
00:26:14,880 --> 00:26:18,840
Speaker 1: so easily and callously discarded. By some of these big

511
00:26:18,840 --> 00:26:23,719
Speaker 1: companies and institutions, especially given that tech layoffs are rampant

512
00:26:23,800 --> 00:26:28,159
Speaker 1: right now and that they have disproportionately impacted marginalized people

513
00:26:28,280 --> 00:26:30,840
Speaker 1: like women. One study by eight full Ai found that

514
00:26:30,880 --> 00:26:33,239
Speaker 1: women are more than sixty five percent more likely than

515
00:26:33,280 --> 00:26:35,640
Speaker 1: men to lose their jobs in tech layoffs. THO Women

516
00:26:35,720 --> 00:26:38,359
Speaker 1: Tech Council reported that women were one point six times

517
00:26:38,400 --> 00:26:40,240
Speaker 1: more likely to be let go than their mill peers.

518
00:26:40,560 --> 00:26:43,719
Speaker 1: Another study from Revelio Labs back in twenty twenty two

519
00:26:43,840 --> 00:26:46,520
Speaker 1: found that black workers make up six point zho five

520
00:26:46,600 --> 00:26:49,879
Speaker 1: percent of the tech workforce, but accounted for seven point

521
00:26:49,920 --> 00:26:52,240
Speaker 1: forty two percent of all tech layoffs, so they were

522
00:26:52,359 --> 00:26:54,879
Speaker 1: laid off at a higher rate than their proportion of

523
00:26:54,920 --> 00:26:58,040
Speaker 1: tech employment. Same is true for Latino We're LATINX workers

524
00:26:58,119 --> 00:27:01,480
Speaker 1: who are definitely overrepresented in tech layoffs. And so I think,

525
00:27:01,560 --> 00:27:05,840
Speaker 1: against that backdrop, Google also backing away from all of

526
00:27:05,880 --> 00:27:08,960
Speaker 1: these commitments that they made for marginalized people in STEM

527
00:27:09,000 --> 00:27:11,920
Speaker 1: and tech, it's just craven to be so sneaky about

528
00:27:11,920 --> 00:27:12,800
Speaker 1: it against that.

529
00:27:12,720 --> 00:27:17,920
Speaker 3: Backdrop, Yes, and if the mo here is to do

530
00:27:17,960 --> 00:27:21,960
Speaker 3: it quietly, you know, and we still notice some of

531
00:27:21,960 --> 00:27:26,399
Speaker 3: these big things, like you know, supporting an initiative to

532
00:27:26,520 --> 00:27:29,840
Speaker 3: organize conferences for women in tech. When the funding for

533
00:27:29,840 --> 00:27:32,280
Speaker 3: that goes away, people notice there's no way to do

534
00:27:32,320 --> 00:27:35,280
Speaker 3: that like completely behind closed doors because it was a

535
00:27:35,280 --> 00:27:38,600
Speaker 3: public basing event. But things like employment and who gets

536
00:27:38,680 --> 00:27:41,520
Speaker 3: laid off or not, that's so much harder to see.

537
00:27:41,640 --> 00:27:45,359
Speaker 3: And so if they're doing the publicly visible stuff quietly

538
00:27:45,480 --> 00:27:47,840
Speaker 3: on the down low, it just makes you have to

539
00:27:47,880 --> 00:27:52,920
Speaker 3: wonder what is going on with things that are less visible,

540
00:27:53,040 --> 00:27:56,800
Speaker 3: Like you just mentioned those employment or those layoff statistics,

541
00:27:57,280 --> 00:28:02,399
Speaker 3: and also those layoff statistics they're all for you know,

542
00:28:02,240 --> 00:28:06,280
Speaker 3: you mentioned that women are being more likely to be

543
00:28:06,359 --> 00:28:09,919
Speaker 3: laid off during this round of tech layoffs black workers

544
00:28:09,920 --> 00:28:13,240
Speaker 3: where Latin X workers are, and those are independent effects.

545
00:28:14,320 --> 00:28:20,240
Speaker 3: I have to suspect, based on decades of evidence, that

546
00:28:20,359 --> 00:28:27,080
Speaker 3: the interactive effects, the intersectional effects are even greater, right

547
00:28:27,119 --> 00:28:30,560
Speaker 3: where Like you can have the independent effect of if

548
00:28:30,600 --> 00:28:33,560
Speaker 3: you're a black tech worker you're more likely being laid off,

549
00:28:33,760 --> 00:28:38,040
Speaker 3: as that Revelio Lab study found, if you're a woman,

550
00:28:38,160 --> 00:28:41,200
Speaker 3: you're more likely to be laid off than your male peers,

551
00:28:41,320 --> 00:28:44,920
Speaker 3: as that Women Tech Council report found. I don't have

552
00:28:44,960 --> 00:28:47,840
Speaker 3: the numbers in front of me. But I would bet

553
00:28:47,880 --> 00:28:52,600
Speaker 3: good money that even above those independent effects, being a

554
00:28:52,640 --> 00:28:55,880
Speaker 3: black woman in tech right now puts one at even

555
00:28:56,000 --> 00:29:00,240
Speaker 3: greater risk of layoff than just the independ and an

556
00:29:00,280 --> 00:29:03,440
Speaker 3: additive effects of being black and a woman. We've seen

557
00:29:03,440 --> 00:29:09,120
Speaker 3: that so many times in analyzes of labor statistics.

558
00:29:09,600 --> 00:29:12,960
Speaker 1: Oh, absolutely right, they I mean, I can I can

559
00:29:13,000 --> 00:29:16,080
Speaker 1: confirm they probably wanted to give rid of us first,

560
00:29:16,120 --> 00:29:18,320
Speaker 1: you know, but let's use this tech climate to get

561
00:29:18,320 --> 00:29:18,960
Speaker 1: her out of here.

562
00:29:19,360 --> 00:29:21,720
Speaker 2: And I think, to put all of this back into.

563
00:29:21,480 --> 00:29:24,040
Speaker 1: Perspective, even if you don't work at one of these companies,

564
00:29:24,560 --> 00:29:25,920
Speaker 1: it is better for all of us.

565
00:29:26,000 --> 00:29:27,240
Speaker 2: It is safer for all of us.

566
00:29:27,280 --> 00:29:31,080
Speaker 1: Our tech landscape is better when the people who are

567
00:29:31,120 --> 00:29:33,360
Speaker 1: making the decisions about the technology that we all use

568
00:29:33,640 --> 00:29:36,840
Speaker 1: look like the people who will use that technology. And yeah,

569
00:29:36,880 --> 00:29:39,160
Speaker 1: it's not just about I know, I've said this a

570
00:29:39,200 --> 00:29:42,840
Speaker 1: million times. Inclusion at tech companies is not just the

571
00:29:42,960 --> 00:29:46,000
Speaker 1: right thing to do ethically, which it is. It also

572
00:29:46,160 --> 00:29:49,760
Speaker 1: makes technology in the entire tech landscape better safer for

573
00:29:49,920 --> 00:29:50,520
Speaker 1: all of us.

574
00:29:50,600 --> 00:29:51,120
Speaker 2: Everyone.

575
00:29:51,520 --> 00:29:55,000
Speaker 3: We just talked about SORA. From a consent framework, right

576
00:29:55,040 --> 00:29:57,800
Speaker 3: how it's you know, from a certain perspective, it is

577
00:29:58,280 --> 00:30:01,000
Speaker 3: violating consent in a way that is hard people. You

578
00:30:01,120 --> 00:30:03,800
Speaker 3: have to suspect that if most of the people working

579
00:30:03,880 --> 00:30:08,400
Speaker 3: on that project were women, they might be waiting consent

580
00:30:08,560 --> 00:30:10,840
Speaker 3: a little bit higher in their policies.

581
00:30:11,160 --> 00:30:11,360
Speaker 2: Oh.

582
00:30:11,400 --> 00:30:13,080
Speaker 1: It reminds me so much of the story we talked

583
00:30:13,080 --> 00:30:17,120
Speaker 1: about when Instagram rolled out that feature that displayed all

584
00:30:17,160 --> 00:30:21,520
Speaker 1: of your stories on an interactive map. I think that

585
00:30:21,920 --> 00:30:24,719
Speaker 1: how they rolled that out would be totally different if

586
00:30:24,720 --> 00:30:28,040
Speaker 1: they had more women making decisions about that kind of technology.

587
00:30:28,120 --> 00:30:30,280
Speaker 2: The way they I really will take this to the bank,

588
00:30:30,560 --> 00:30:31,800
Speaker 2: The way they rolled.

589
00:30:31,560 --> 00:30:34,640
Speaker 1: Out a tool that allows you to see on a

590
00:30:34,680 --> 00:30:35,520
Speaker 1: map where.

591
00:30:35,280 --> 00:30:36,240
Speaker 2: Photos were taken.

592
00:30:36,600 --> 00:30:38,440
Speaker 1: I think if they had more women making decisions, they

593
00:30:38,440 --> 00:30:40,320
Speaker 1: would have rolled that out a little bit differently, Because

594
00:30:40,480 --> 00:30:42,960
Speaker 1: most women will tell you, Yeah, I don't really love

595
00:30:43,000 --> 00:30:45,320
Speaker 1: the idea, even if it's not sow it tracking my

596
00:30:45,400 --> 00:30:48,200
Speaker 1: real time location. I don't love the idea of giving

597
00:30:48,400 --> 00:30:52,640
Speaker 1: the Internet or whoever a visual map of my frequent locations.

598
00:30:53,240 --> 00:30:53,880
Speaker 2: Yeah.

599
00:30:54,000 --> 00:30:58,080
Speaker 3: I mean, this conversation is getting pretty close to the

600
00:30:58,120 --> 00:31:03,520
Speaker 3: core essential premise of this show, which is that women

601
00:31:04,240 --> 00:31:08,160
Speaker 3: and girls are so often the subject of the Internet,

602
00:31:08,720 --> 00:31:13,840
Speaker 3: but often left out of decisions about how it is

603
00:31:13,880 --> 00:31:15,240
Speaker 3: shaped and how it functions.

604
00:31:15,760 --> 00:31:18,080
Speaker 1: Yeah, that reminds me of this thread post that I

605
00:31:18,080 --> 00:31:22,320
Speaker 1: saw from Kento Morita that says, us, we can make anyone,

606
00:31:22,440 --> 00:31:24,800
Speaker 1: any being in the world with this new technology. They

607
00:31:24,800 --> 00:31:27,040
Speaker 1: can be genderless, they can be a mystic creature. What

608
00:31:27,080 --> 00:31:30,200
Speaker 1: should we do first, developers, make a woman that does

609
00:31:30,240 --> 00:31:31,080
Speaker 1: what we tell them?

610
00:31:31,360 --> 00:31:33,560
Speaker 2: References Tillie Norwood.

611
00:31:33,080 --> 00:31:35,760
Speaker 1: That AI actress we talked about last week, Alexa Sirie,

612
00:31:35,800 --> 00:31:40,760
Speaker 1: Google assistant Eliza Like. The way that consuming and controlling

613
00:31:40,960 --> 00:31:44,280
Speaker 1: women and girls is at the core of our entire

614
00:31:44,440 --> 00:31:45,440
Speaker 1: tech ecosystem.

615
00:31:46,040 --> 00:31:48,360
Speaker 2: It just is. You see it everywhere.

616
00:31:49,160 --> 00:31:52,840
Speaker 3: Yeah, and yeah. We see it in the content that

617
00:31:52,960 --> 00:31:58,320
Speaker 3: is produced. We see it in the boards and leadership

618
00:31:58,400 --> 00:32:03,200
Speaker 3: of tech companies, and we see it in the images

619
00:32:03,280 --> 00:32:06,280
Speaker 3: and the language that makes up the Internet.

620
00:32:06,560 --> 00:32:08,160
Speaker 1: So it's funny that you bring this up because I

621
00:32:08,200 --> 00:32:12,240
Speaker 1: was reading this study that basically suggests that on the Internet,

622
00:32:12,320 --> 00:32:16,560
Speaker 1: in technology, all the grown women are actually girls. This

623
00:32:16,600 --> 00:32:19,480
Speaker 1: is according to new research from Soleine Delacort, who is

624
00:32:19,520 --> 00:32:22,200
Speaker 1: an assistant professor at UC Berkeley's Hash School of Business

625
00:32:22,200 --> 00:32:24,680
Speaker 1: and the co author of this study published in Nature,

626
00:32:25,000 --> 00:32:29,280
Speaker 1: where researchers looked at images and text across some of

627
00:32:29,280 --> 00:32:33,280
Speaker 1: the most well trafficked spots on the Internet, chatbt, Google, Wikipedia,

628
00:32:33,320 --> 00:32:37,920
Speaker 1: IMDb and found that women are regularly depicted as younger

629
00:32:37,920 --> 00:32:41,600
Speaker 1: than men and devalued in the real world in real

630
00:32:41,640 --> 00:32:43,080
Speaker 1: world spaces because of it.

631
00:32:43,520 --> 00:32:49,280
Speaker 3: This was such a cool study and just staggeringly impressive

632
00:32:49,480 --> 00:32:53,000
Speaker 3: in its methods and its scope and its ambition. I

633
00:32:53,080 --> 00:32:55,040
Speaker 3: just wanted to shout that out before you get into it.

634
00:32:55,160 --> 00:33:01,440
Speaker 3: They had over six thousand human coders over six hundred

635
00:33:01,440 --> 00:33:05,440
Speaker 3: and fifty thousand images. It's like a breathtaking amount of

636
00:33:05,520 --> 00:33:09,360
Speaker 3: data and its orders of magnitude more data than many

637
00:33:09,440 --> 00:33:11,320
Speaker 3: studies that look at similar topics.

638
00:33:11,360 --> 00:33:13,160
Speaker 2: We'll link to it in the show notes, and.

639
00:33:13,760 --> 00:33:15,360
Speaker 3: Also I want to give them credit for like it

640
00:33:15,440 --> 00:33:18,880
Speaker 3: is a pretty accessible read as far as studies go.

641
00:33:19,320 --> 00:33:20,480
Speaker 3: I found it quite accessible.

642
00:33:20,640 --> 00:33:24,360
Speaker 1: Yeah, as our resident podcast scientist, I knew that you

643
00:33:24,360 --> 00:33:27,280
Speaker 1: would be excited about to talk about the methodology.

644
00:33:27,520 --> 00:33:28,960
Speaker 2: How did I know that you were going to.

645
00:33:29,000 --> 00:33:31,920
Speaker 1: Have a little big up to the methodology used in

646
00:33:31,920 --> 00:33:34,480
Speaker 1: the study. But I read a summary of the study

647
00:33:34,480 --> 00:33:37,320
Speaker 1: in Mother Jones called It's true the Internet skews the

648
00:33:37,360 --> 00:33:40,160
Speaker 1: reality of women and men in the workforce, and it

649
00:33:40,240 --> 00:33:44,200
Speaker 1: really is quite interesting. So basically the researchers analyzed over

650
00:33:44,320 --> 00:33:47,200
Speaker 1: half a million images from Google Search in which women

651
00:33:47,240 --> 00:33:50,280
Speaker 1: consistently appeared younger than men. This serves as a measure

652
00:33:50,320 --> 00:33:52,640
Speaker 1: of cultural bias because it's basically trying to give you

653
00:33:52,680 --> 00:33:54,920
Speaker 1: content that you are most likely to click on. That

654
00:33:55,080 --> 00:33:58,000
Speaker 1: is from co author Douglas school Bolt, who's an associate

655
00:33:58,040 --> 00:34:00,720
Speaker 1: professor at Stanford. He said that has a way of

656
00:34:00,760 --> 00:34:03,400
Speaker 1: being prone to bias because it ends up just amplifying

657
00:34:03,400 --> 00:34:06,920
Speaker 1: whatever most people click on. So across the Internet, women

658
00:34:07,080 --> 00:34:10,560
Speaker 1: are most commonly shown in their twenties, while men are

659
00:34:10,680 --> 00:34:12,040
Speaker 1: usually shown in their.

660
00:34:11,880 --> 00:34:12,960
Speaker 2: Forties or fifties.

661
00:34:13,320 --> 00:34:16,600
Speaker 1: And it's not just that women in these images look younger.

662
00:34:17,000 --> 00:34:21,000
Speaker 1: Often they are younger on IMDb, in Wikipedia. The researchers

663
00:34:21,000 --> 00:34:23,880
Speaker 1: were able to collect information about the actual ages of

664
00:34:23,880 --> 00:34:26,080
Speaker 1: the people in these photos that were all over the Internet,

665
00:34:26,320 --> 00:34:31,359
Speaker 1: and that trend really persisted. So importantly, this trend does

666
00:34:31,480 --> 00:34:36,120
Speaker 1: not accurately reflect reality in both online images and text.

667
00:34:36,400 --> 00:34:39,680
Speaker 1: The researchers found similar skewed depictions of men and women

668
00:34:39,880 --> 00:34:43,240
Speaker 1: in thousands of occupations and other categories, but in census

669
00:34:43,280 --> 00:34:46,440
Speaker 1: data across most of those fields examined, there were no

670
00:34:46,680 --> 00:34:50,120
Speaker 1: age differences among men and women in the few professions

671
00:34:50,120 --> 00:34:52,960
Speaker 1: where an age gap existed. In the census data, the

672
00:34:53,000 --> 00:34:56,400
Speaker 1: women tended to be older on average than men, But

673
00:34:56,520 --> 00:35:00,680
Speaker 1: the online images presented an inverted picture pattern we see

674
00:35:00,719 --> 00:35:02,680
Speaker 1: in the data just does not match reality.

675
00:35:02,680 --> 00:35:03,839
Speaker 2: Devil Court said, the.

676
00:35:03,840 --> 00:35:06,360
Speaker 1: Average woman in the US and actually in the world,

677
00:35:06,480 --> 00:35:09,680
Speaker 1: has a higher light expectancy. The average woman is older.

678
00:35:09,880 --> 00:35:12,319
Speaker 1: So what we see in online images and text and

679
00:35:12,400 --> 00:35:15,959
Speaker 1: videos is wrong. So the Internet is really out here

680
00:35:16,160 --> 00:35:19,440
Speaker 1: making us think that the only women around are younger

681
00:35:19,480 --> 00:35:23,200
Speaker 1: than we actually are, even in situations where the data confirms, okay,

682
00:35:23,239 --> 00:35:26,360
Speaker 1: women in these specific positions do tend to be older.

683
00:35:26,640 --> 00:35:29,640
Speaker 1: Even in those situations, the women are still reflected as

684
00:35:29,680 --> 00:35:33,080
Speaker 1: younger than we actually are, not just in the United States, globally,

685
00:35:33,520 --> 00:35:35,879
Speaker 1: and since, as the study said, this is all being

686
00:35:36,000 --> 00:35:39,040
Speaker 1: shaped by our biases, I guess this is just anti

687
00:35:39,160 --> 00:35:43,239
Speaker 1: older woman bias being reflected back at us via the Internet.

688
00:35:43,840 --> 00:35:49,400
Speaker 3: It speaks to this assumption that underlies so many of

689
00:35:49,400 --> 00:35:53,680
Speaker 3: these large language models and AI tools. There's this assumption

690
00:35:53,719 --> 00:35:57,239
Speaker 3: that the training data right, They're scraping data from the Internet,

691
00:35:57,880 --> 00:36:01,760
Speaker 3: all the different corners of the these huge corpuses of text

692
00:36:01,800 --> 00:36:05,120
Speaker 3: and images, and I think there's often an assumption that

693
00:36:05,120 --> 00:36:11,080
Speaker 3: that data accurately represents the real world, the natural world,

694
00:36:11,640 --> 00:36:16,840
Speaker 3: but it definitely does not. It reflects what people want

695
00:36:16,880 --> 00:36:20,000
Speaker 3: to see on the Internet, what platforms want to serve

696
00:36:20,120 --> 00:36:22,080
Speaker 3: up to people on the Internet.

697
00:36:22,600 --> 00:36:23,879
Speaker 2: And so it.

698
00:36:23,920 --> 00:36:30,400
Speaker 3: Is not at all surprising that the distribution of women's

699
00:36:30,600 --> 00:36:34,240
Speaker 3: ages in photos on the Internet is not a perfect

700
00:36:34,320 --> 00:36:39,040
Speaker 3: match for the distribution of women's ages among the living

701
00:36:39,080 --> 00:36:42,440
Speaker 3: women on the world. These are two different things. The Internet,

702
00:36:42,760 --> 00:36:46,319
Speaker 3: the images that are on the Internet are serving a

703
00:36:46,360 --> 00:36:52,000
Speaker 3: particular purpose, and like, in that sense, it's not a

704
00:36:52,120 --> 00:36:56,160
Speaker 3: new idea, but I think it's very easy to forget

705
00:36:56,200 --> 00:37:00,680
Speaker 3: that fact when we're talking about training data or a

706
00:37:00,719 --> 00:37:04,040
Speaker 3: bunch of other phenomena about the Internet. And I think

707
00:37:04,320 --> 00:37:07,600
Speaker 3: they've this study has done a really good job of

708
00:37:08,200 --> 00:37:14,200
Speaker 3: numerically quantifying that gap, that difference between the real world

709
00:37:14,719 --> 00:37:16,880
Speaker 3: and the data that exists on the Internet.

710
00:37:17,320 --> 00:37:19,480
Speaker 1: Yeah, and something else about that study is that they

711
00:37:19,480 --> 00:37:22,960
Speaker 1: really make it clear that even though these images do

712
00:37:23,080 --> 00:37:26,720
Speaker 1: not reflect reality, it goes on to shape our perception

713
00:37:26,800 --> 00:37:28,080
Speaker 1: of irl reality.

714
00:37:28,520 --> 00:37:29,480
Speaker 2: This age gap.

715
00:37:29,320 --> 00:37:32,359
Speaker 1: Myth impacts how people view women in the workplace. As

716
00:37:32,400 --> 00:37:34,680
Speaker 1: part of the study, the researchers ask participants to find

717
00:37:34,719 --> 00:37:38,040
Speaker 1: photos of people working across different professions. When the participants

718
00:37:38,040 --> 00:37:40,640
Speaker 1: elected photos of women, they assume that the people with

719
00:37:40,719 --> 00:37:44,200
Speaker 1: that job would generally be younger and have less experience.

720
00:37:44,719 --> 00:37:46,920
Speaker 2: And it is funny to me, just as.

721
00:37:46,800 --> 00:37:50,680
Speaker 1: A woman of a certain age, you know, I do

722
00:37:50,960 --> 00:37:54,960
Speaker 1: think that people have this idea that when you hit

723
00:37:55,120 --> 00:37:58,640
Speaker 1: thirty as a woman, you basically dig a grave and

724
00:37:58,719 --> 00:38:00,279
Speaker 1: crawl into it and die.

725
00:38:00,320 --> 00:38:02,719
Speaker 2: You are pretty much out to pasture.

726
00:38:03,560 --> 00:38:06,759
Speaker 1: I do think that there is an impression that there

727
00:38:06,800 --> 00:38:10,359
Speaker 1: are twomen in their thirties, forties, fifty sixties out here

728
00:38:10,520 --> 00:38:13,720
Speaker 1: living our dynamic lives, that once you hit a certain

729
00:38:13,760 --> 00:38:16,120
Speaker 1: age as a woman, you just go out to a lovely,

730
00:38:16,160 --> 00:38:19,040
Speaker 1: idyllic farm and they give you some overalls, and you're

731
00:38:19,040 --> 00:38:21,480
Speaker 1: never to be seen from again. You're certainly not in public,

732
00:38:21,520 --> 00:38:24,239
Speaker 1: you're certainly not making podcasts and living your life and

733
00:38:24,280 --> 00:38:27,640
Speaker 1: on television. They just give you a pair of overalls,

734
00:38:27,680 --> 00:38:30,719
Speaker 1: a sun hat, some clogs, a thriller novel, and a

735
00:38:30,800 --> 00:38:33,960
Speaker 1: mug of cranberry tea and they send you to that pasture.

736
00:38:34,719 --> 00:38:36,319
Speaker 3: I mean, except for the part of being sent out

737
00:38:36,360 --> 00:38:38,160
Speaker 3: to pasture, that does kind of sound like something that

738
00:38:38,200 --> 00:38:40,799
Speaker 3: you would like no, being a woman of.

739
00:38:40,800 --> 00:38:43,439
Speaker 1: A certain age fucking rocks, right. You know, first of all,

740
00:38:43,480 --> 00:38:44,960
Speaker 1: you don't give a shit what anybody has to say

741
00:38:44,960 --> 00:38:46,719
Speaker 1: about you. You're like, whatever you say about me, go

742
00:38:46,719 --> 00:38:50,000
Speaker 1: ahead and say it. That's fine with me. You completely

743
00:38:50,320 --> 00:38:53,400
Speaker 1: stop giving a shit about the way that you present physically.

744
00:38:54,239 --> 00:38:56,080
Speaker 1: All of that stuff is for you, and anybody who

745
00:38:56,120 --> 00:38:59,080
Speaker 1: doesn't like it a kick rock. You basically become whoever

746
00:38:59,120 --> 00:39:01,560
Speaker 1: it was you wanted to be or were at fifteen

747
00:39:01,640 --> 00:39:04,239
Speaker 1: or sixteen. But this time around, you're totally cool with it.

748
00:39:04,360 --> 00:39:06,960
Speaker 1: You've got zero insecurity about it, and if you're lucky,

749
00:39:07,200 --> 00:39:09,200
Speaker 1: you got a little bit of coin to support whatever

750
00:39:09,239 --> 00:39:12,719
Speaker 1: that thing is. Tea, herbal tea really hits different. I

751
00:39:12,719 --> 00:39:14,760
Speaker 1: feel sometimes I'm drinking a lot less.

752
00:39:14,560 --> 00:39:16,880
Speaker 2: These days, and sometimes a good mugger. Herbal Tea really

753
00:39:16,920 --> 00:39:17,800
Speaker 2: does hit different.

754
00:39:18,160 --> 00:39:21,640
Speaker 1: Aging is awesome, and anybody who tells you otherwise just

755
00:39:21,680 --> 00:39:22,839
Speaker 1: hasn't hit that age yet.

756
00:39:23,080 --> 00:39:26,160
Speaker 3: I've heard people say that this is what they love

757
00:39:26,400 --> 00:39:31,439
Speaker 3: about the Real Housewives franchises, that they show women in

758
00:39:31,680 --> 00:39:36,600
Speaker 3: this age bracket that you're talking about, just demolishing those

759
00:39:36,600 --> 00:39:40,239
Speaker 3: stereotypes of what they're supposed to be doing with their lives.

760
00:39:40,480 --> 00:39:42,239
Speaker 1: I'm sure that you got that for me, because that

761
00:39:42,320 --> 00:39:45,040
Speaker 1: is exactly what I love about these shows. What like,

762
00:39:45,280 --> 00:39:48,480
Speaker 1: Ramona Singer is almost seventy years old. Where else are

763
00:39:48,480 --> 00:39:51,960
Speaker 1: you going to see a woman in her late sixties

764
00:39:52,200 --> 00:39:54,080
Speaker 1: throwing back box wine.

765
00:39:53,920 --> 00:39:55,759
Speaker 2: And throw an ass? Where else are you going to

766
00:39:55,800 --> 00:39:57,680
Speaker 2: see that? I want to see that? I want.

767
00:39:57,840 --> 00:40:02,080
Speaker 1: I think we need reminders that people live full, dynamic

768
00:40:02,200 --> 00:40:06,800
Speaker 1: lives into their seventies. We need to dispel this notion

769
00:40:06,920 --> 00:40:09,520
Speaker 1: that you just fall off a fucking cliff or crawl

770
00:40:09,520 --> 00:40:11,880
Speaker 1: into a grave once you hit thirty, especially as women,

771
00:40:12,120 --> 00:40:14,960
Speaker 1: and there's not really a lot of places where you

772
00:40:15,000 --> 00:40:17,600
Speaker 1: get to see what that looks like. Bravo for Better

773
00:40:17,680 --> 00:40:19,920
Speaker 1: or for Worse is giving it to us, and I

774
00:40:20,080 --> 00:40:20,359
Speaker 1: like it.

775
00:40:20,880 --> 00:40:22,839
Speaker 2: I don't know. I feel like every conversation that comes

776
00:40:22,840 --> 00:40:23,880
Speaker 2: back to Housewise.

777
00:40:23,520 --> 00:40:27,600
Speaker 3: With me, you know, I'm liking at an algorithm. I'm

778
00:40:27,600 --> 00:40:28,800
Speaker 3: just trying to give you what you want.

779
00:40:29,080 --> 00:40:32,719
Speaker 2: It's talking about housewives more.

780
00:40:32,760 --> 00:40:45,440
Speaker 4: After a quick break, let's get right back into it.

781
00:40:47,080 --> 00:40:47,319
Speaker 2: Okay.

782
00:40:47,360 --> 00:40:49,240
Speaker 1: I have to give a trigger warning to this story

783
00:40:49,239 --> 00:40:52,000
Speaker 1: because it is very disturbing, but I don't think it's

784
00:40:52,000 --> 00:40:53,800
Speaker 1: gotten a ton of coverage, So I did want to

785
00:40:53,840 --> 00:40:55,680
Speaker 1: quickly talk about it. And that is a story out

786
00:40:55,680 --> 00:40:59,680
Speaker 1: of New Jersey where seventeen year old Vincent Betalaro was

787
00:40:59,760 --> 00:41:04,560
Speaker 1: charged with intentionally hitting and killing two seventeen year old girls,

788
00:41:04,640 --> 00:41:08,600
Speaker 1: Isabelle Salas and Maria Niotis at seventy miles per hour

789
00:41:08,760 --> 00:41:11,920
Speaker 1: with his car as they rode e bikes around their house.

790
00:41:12,200 --> 00:41:16,960
Speaker 2: So, before this, Vincent shared these rambling live streams.

791
00:41:17,000 --> 00:41:20,640
Speaker 1: He was a YouTuber who would YouTube about sports, but also,

792
00:41:20,719 --> 00:41:23,200
Speaker 1: according to New Jersey Today, would also talk about his

793
00:41:23,280 --> 00:41:26,200
Speaker 1: need for vengeance against one of these girls. He would

794
00:41:26,239 --> 00:41:30,839
Speaker 1: post these rants on YouTube live about false allegations against him,

795
00:41:30,920 --> 00:41:35,799
Speaker 1: including child sexual exploitation material and this one girl from

796
00:41:35,800 --> 00:41:38,720
Speaker 1: his school who was causing him legal trouble, he said.

797
00:41:38,719 --> 00:41:40,160
Speaker 2: So he really.

798
00:41:39,920 --> 00:41:42,880
Speaker 1: Blamed seventeen year old Maria, one of the girls that

799
00:41:42,920 --> 00:41:45,240
Speaker 1: he would go on to kill and her mother for

800
00:41:45,560 --> 00:41:48,560
Speaker 1: I guess legal trouble and legal problems he was having at.

801
00:41:48,480 --> 00:41:50,920
Speaker 2: School, and he began harassing her.

802
00:41:51,000 --> 00:41:54,279
Speaker 1: According to her neighbors and family, he delivered pizza's to

803
00:41:54,320 --> 00:41:56,320
Speaker 1: her house the kai that you're meant to pay cash

804
00:41:56,360 --> 00:42:00,640
Speaker 1: for upon delivery, as some kind of a bizarre revenge

805
00:42:00,960 --> 00:42:04,200
Speaker 1: for what he says was a critical post on social

806
00:42:04,239 --> 00:42:07,080
Speaker 1: media that she made about Charlie Kirk after Kirk's death.

807
00:42:07,400 --> 00:42:09,920
Speaker 1: So after he delivered these pizza to her house, Vincent

808
00:42:10,040 --> 00:42:12,879
Speaker 1: went on YouTube and said, whenever Maria sees the pizza guy,

809
00:42:12,960 --> 00:42:15,400
Speaker 1: come better think of Charlie Kirk for making fun of

810
00:42:15,440 --> 00:42:19,080
Speaker 1: his fucking death. Stupid ass clown, just remember that. So

811
00:42:19,520 --> 00:42:22,239
Speaker 1: he started stalking her, even staking at her home in

812
00:42:22,280 --> 00:42:24,200
Speaker 1: the months before he struck and killed her and her

813
00:42:24,239 --> 00:42:26,960
Speaker 1: friend Isabella as they rode their bikes on the street.

814
00:42:27,280 --> 00:42:30,440
Speaker 1: So her mom basically had been reporting this creep to

815
00:42:30,520 --> 00:42:33,759
Speaker 1: the police for harassing her daughter for a while, and

816
00:42:33,800 --> 00:42:36,680
Speaker 1: it just genuinely sounds like nothing was done. According to

817
00:42:36,800 --> 00:42:40,239
Speaker 1: NJ Advanced, Vincent even referenced all of this, the fact

818
00:42:40,280 --> 00:42:43,240
Speaker 1: that her mom had been reporting this and nothing was done,

819
00:42:43,239 --> 00:42:46,920
Speaker 1: on his live stream. He referenced a police investigation several

820
00:42:46,920 --> 00:42:49,920
Speaker 1: times on his live stream this summer, saying, because I

821
00:42:50,040 --> 00:42:52,840
Speaker 1: got into her relationship business and now the school, and

822
00:42:52,880 --> 00:42:54,799
Speaker 1: now I'm going to tell you all this. Cops got

823
00:42:54,800 --> 00:42:57,280
Speaker 1: involved in the school, got involved in the shit somehow

824
00:42:57,320 --> 00:43:00,680
Speaker 1: they're believing this crap. They suspended me based and definitely

825
00:43:00,719 --> 00:43:02,840
Speaker 1: until they figured it out, which makes again no sense,

826
00:43:03,120 --> 00:43:05,840
Speaker 1: But by June, he said, the police told him quote

827
00:43:05,880 --> 00:43:07,480
Speaker 1: that the case is going to be dismissed and I

828
00:43:07,480 --> 00:43:09,960
Speaker 1: Am not going to be facing any charges, he said

829
00:43:10,040 --> 00:43:13,279
Speaker 1: during a live stream. He also compared himself to an

830
00:43:13,320 --> 00:43:16,759
Speaker 1: ex Dodgers pitcher, Trevor Bauer, who was suspended over sexual

831
00:43:16,800 --> 00:43:19,520
Speaker 1: assault allegations back in twenty twenty one but never faced

832
00:43:19,560 --> 00:43:22,960
Speaker 1: any criminal charges. Vincent said on his live stream, quote,

833
00:43:23,040 --> 00:43:24,640
Speaker 1: I'm basically cleard of any wrongdoing.

834
00:43:24,680 --> 00:43:26,000
Speaker 2: It says a lot. It says a lot.

835
00:43:26,160 --> 00:43:27,880
Speaker 1: If they're closing the case on me for this and

836
00:43:27,920 --> 00:43:30,080
Speaker 1: they're bringing me back to school, it says how much

837
00:43:30,120 --> 00:43:31,920
Speaker 1: of an innocent I was in the first place. Now

838
00:43:31,920 --> 00:43:34,200
Speaker 1: people are trying to treat me like Trevor Bauer to

839
00:43:34,280 --> 00:43:35,040
Speaker 1: make me feel like.

840
00:43:35,000 --> 00:43:35,720
Speaker 2: The bad person.

841
00:43:36,239 --> 00:43:38,560
Speaker 1: So I wanted to talk about this one because I

842
00:43:38,600 --> 00:43:41,560
Speaker 1: don't feel like it's gotten enough coverage that this person

843
00:43:41,719 --> 00:43:44,920
Speaker 1: who was holding out a vendetta against one of these

844
00:43:44,960 --> 00:43:47,799
Speaker 1: girls for comments she allegedly made about Charlie Kirk ended

845
00:43:47,880 --> 00:43:51,640
Speaker 1: up murdering her after harassing her both in person and online.

846
00:43:51,719 --> 00:43:53,160
Speaker 2: And I wanted to bring it up because it.

847
00:43:53,160 --> 00:43:56,120
Speaker 1: Is absolutely tail as old as time that a woman

848
00:43:56,280 --> 00:44:00,560
Speaker 1: or a girl reports dangerous behavior that typically has an

849
00:44:00,560 --> 00:44:05,680
Speaker 1: online component. Nothing is done, It is belittled, it is dismissed,

850
00:44:05,960 --> 00:44:09,480
Speaker 1: it is chalked up to just oh, it's just internet whatever.

851
00:44:09,800 --> 00:44:12,880
Speaker 1: And then when nothing is done, it leads to larger

852
00:44:13,080 --> 00:44:16,040
Speaker 1: violence that could have been prevented. This, it sounds like

853
00:44:16,080 --> 00:44:18,840
Speaker 1: to me, this was preventable, the fact that this girl's

854
00:44:18,920 --> 00:44:21,160
Speaker 1: mom had been reporting this creep for a long time

855
00:44:21,200 --> 00:44:24,640
Speaker 1: and there's a documented even he talks about this documented

856
00:44:25,360 --> 00:44:27,520
Speaker 1: history of these reports and saying, oh, they found out

857
00:44:27,560 --> 00:44:29,279
Speaker 1: I was innocent. They dismissed it was nothing there. I

858
00:44:29,320 --> 00:44:30,320
Speaker 1: was innocent. I was innocent.

859
00:44:31,080 --> 00:44:32,040
Speaker 2: Just goes to show that.

860
00:44:32,000 --> 00:44:35,160
Speaker 1: When we do not take online harms against women and

861
00:44:35,200 --> 00:44:38,280
Speaker 1: girls seriously, it has violent consequences.

862
00:44:38,760 --> 00:44:44,239
Speaker 3: Absolutely, what a tragic story. I cannot begin to imagine

863
00:44:44,920 --> 00:44:49,160
Speaker 3: the rage that that mom must feel that she clocked

864
00:44:49,160 --> 00:44:52,000
Speaker 3: this guy as a threat, she was reporting him to

865
00:44:52,040 --> 00:44:57,040
Speaker 3: the authorities, and nothing was done, and then he made

866
00:44:57,120 --> 00:44:57,920
Speaker 3: good on that threat.

867
00:44:58,880 --> 00:45:00,720
Speaker 2: It's such failure.

868
00:45:00,960 --> 00:45:03,719
Speaker 3: And I don't know enough of others. I don't I

869
00:45:03,719 --> 00:45:05,920
Speaker 3: don't want to just blame the police. I don't want

870
00:45:05,920 --> 00:45:09,239
Speaker 3: to blame the school. It's hard to know exactly who

871
00:45:09,239 --> 00:45:14,640
Speaker 3: should be responsible, and accountable here, but clearly this was

872
00:45:15,239 --> 00:45:17,120
Speaker 3: a huge failure and it's.

873
00:45:16,960 --> 00:45:22,799
Speaker 1: So tragic, tragic and preventable. Okay, well, switching gears a

874
00:45:22,840 --> 00:45:25,520
Speaker 1: little bit, will you allow me to talk about Drake

875
00:45:25,640 --> 00:45:26,200
Speaker 1: really quickly?

876
00:45:27,120 --> 00:45:27,720
Speaker 2: Yes? Please?

877
00:45:27,760 --> 00:45:30,120
Speaker 3: We need a palate cleanser after that last story.

878
00:45:30,400 --> 00:45:33,839
Speaker 1: Okay, So do you understand how deep a dis track

879
00:45:34,040 --> 00:45:37,000
Speaker 1: has to hit for the target of said distrack to

880
00:45:37,160 --> 00:45:40,959
Speaker 1: try to sue over it, because that is what Drake did.

881
00:45:41,440 --> 00:45:46,000
Speaker 1: So after Kendrick Lamar's epic distract Not Like Us that

882
00:45:46,160 --> 00:45:50,440
Speaker 1: basically was a song of the Summer where Lamar heavily

883
00:45:50,520 --> 00:45:54,080
Speaker 1: implies that Drake is a pedophile, and I mean not

884
00:45:54,160 --> 00:45:57,560
Speaker 1: for nothing. Drake did have some kind of a friendship

885
00:45:57,600 --> 00:45:59,000
Speaker 1: with Millie Bobby Brown when she.

886
00:45:59,080 --> 00:46:00,319
Speaker 2: Was fourteen and he was.

887
00:46:00,320 --> 00:46:03,440
Speaker 1: Thirty one, where Millie Bobby Brown said, oh, we're we're buds.

888
00:46:03,480 --> 00:46:05,480
Speaker 1: We text all the time and I don't know, maybe

889
00:46:05,480 --> 00:46:07,279
Speaker 1: this is just the old woman in me talking, but

890
00:46:07,520 --> 00:46:09,840
Speaker 1: what the fuck would a thirty one year old have

891
00:46:10,000 --> 00:46:10,919
Speaker 1: to be texting.

892
00:46:10,600 --> 00:46:14,240
Speaker 2: A fourteen year old about nothing good? Exactly?

893
00:46:14,320 --> 00:46:16,640
Speaker 1: So, as we like to say in the South, a

894
00:46:16,719 --> 00:46:19,799
Speaker 1: hit dog will holler I guess, And Drake was so

895
00:46:20,040 --> 00:46:21,600
Speaker 1: hit But he took it to the courts.

896
00:46:22,200 --> 00:46:27,560
Speaker 3: Damn that is sad like I grew up in the

897
00:46:27,680 --> 00:46:31,440
Speaker 3: nineties when rappers were shooting each other, right, which is

898
00:46:31,600 --> 00:46:35,000
Speaker 3: obviously like very bad, but like we have come a

899
00:46:35,040 --> 00:46:39,680
Speaker 3: long way to file a lawsuit like come on, Drake.

900
00:46:40,400 --> 00:46:43,000
Speaker 1: Well, this week the court deemed that a song that

901
00:46:43,120 --> 00:46:47,400
Speaker 1: implies someone is a sex criminal is not legally actionable.

902
00:46:47,600 --> 00:46:48,640
Speaker 2: So this is from Billboard.

903
00:46:48,960 --> 00:46:52,759
Speaker 1: A federal judge dismissed Drake's defamation lawsuit against Universal Music

904
00:46:52,800 --> 00:46:55,880
Speaker 1: Group over Kendrick Lamar's Not Like Us, ruling that a

905
00:46:56,000 --> 00:46:59,399
Speaker 1: quote war of words during a heated rap battle did

906
00:46:59,400 --> 00:47:02,040
Speaker 1: not viol the law in that some of the case

907
00:47:02,280 --> 00:47:03,959
Speaker 1: was logically incoherent.

908
00:47:04,239 --> 00:47:06,239
Speaker 2: So Drake filed this suit.

909
00:47:06,000 --> 00:47:10,759
Speaker 1: Earlier this year, claiming that UMG, the music company, defamed

910
00:47:10,840 --> 00:47:14,560
Speaker 1: him by releasing Kendrick Lamar's distrack, which tarred him as

911
00:47:14,600 --> 00:47:17,840
Speaker 1: a quote certified pedophile he likes to call himself a

912
00:47:17,840 --> 00:47:21,720
Speaker 1: certified lover boy more like a certified pedophile. Drake argued

913
00:47:21,760 --> 00:47:25,560
Speaker 1: that millions of people took that lyric literally, severely harming

914
00:47:25,560 --> 00:47:29,319
Speaker 1: his reputation, but this week the judge granted UMG's motion

915
00:47:29,400 --> 00:47:32,919
Speaker 1: to dismiss that case. Ruling that Kendrick's insulting lyrics were

916
00:47:32,960 --> 00:47:36,360
Speaker 1: the kind of hyperbole that cannot be defamatory because listeners

917
00:47:36,520 --> 00:47:39,960
Speaker 1: would not think they were statements affect The judge ruled

918
00:47:40,000 --> 00:47:43,120
Speaker 1: that quote the artist's seven track rat battle was a

919
00:47:43,160 --> 00:47:45,919
Speaker 1: war of words that was the subject of substantial media

920
00:47:45,960 --> 00:47:49,360
Speaker 1: scrutiny and online discourse. Although the accusation that the plaintiff

921
00:47:49,400 --> 00:47:51,960
Speaker 1: is a pedophile is certainly a serious one, the broader

922
00:47:52,000 --> 00:47:54,880
Speaker 1: context of a heated rat battle with insidiary language and

923
00:47:54,920 --> 00:47:58,880
Speaker 1: offensive accusations held by both participants would not incline a

924
00:47:58,920 --> 00:48:01,680
Speaker 1: reasonable listener to believe that not like us in parts

925
00:48:01,840 --> 00:48:04,080
Speaker 1: verifiable facts about the plaintiffs.

926
00:48:04,440 --> 00:48:07,920
Speaker 3: This, I mean, it reminds me of the classic example

927
00:48:08,200 --> 00:48:11,440
Speaker 3: of Bob Marley's track, right, like when he says I

928
00:48:11,520 --> 00:48:14,000
Speaker 3: shot the sheriff. No one thinks that he actually shot

929
00:48:14,000 --> 00:48:14,680
Speaker 3: a sheriff. Right.

930
00:48:14,719 --> 00:48:16,799
Speaker 2: This is singing, This is it's art.

931
00:48:17,360 --> 00:48:19,279
Speaker 1: So I do have a point that I do feel

932
00:48:19,320 --> 00:48:21,200
Speaker 1: I need to make whenever we're talking about this Drake

933
00:48:21,239 --> 00:48:24,600
Speaker 1: and Kendrick Lamar beef, which is that ultimately we are

934
00:48:24,640 --> 00:48:28,160
Speaker 1: talking about allegations of harm against women and girls, and

935
00:48:28,160 --> 00:48:30,480
Speaker 1: I think it's important that that not get lost in

936
00:48:30,640 --> 00:48:34,040
Speaker 1: all the hooplah and fanfare around disc tracks and all

937
00:48:34,040 --> 00:48:37,120
Speaker 1: of that. I think it's incredibly easy for the fact

938
00:48:37,160 --> 00:48:41,080
Speaker 1: that we're having a conversation about actual harmful allegations to

939
00:48:41,160 --> 00:48:43,279
Speaker 1: go overlook because we're talking about something that is so

940
00:48:43,480 --> 00:48:44,640
Speaker 1: amplified and big.

941
00:48:45,200 --> 00:48:47,839
Speaker 2: So I do think it's important to just name that.

942
00:48:48,360 --> 00:48:50,960
Speaker 2: But also it reminds me of.

943
00:48:51,280 --> 00:48:52,680
Speaker 1: I don't even know if I should tell that story

944
00:48:52,680 --> 00:48:55,560
Speaker 1: on the podcast, but I once got in fairly serious

945
00:48:55,600 --> 00:48:59,760
Speaker 1: trouble at a job because of a karaoke song choice

946
00:48:59,800 --> 00:49:03,760
Speaker 1: that I did during a work conference where I sang

947
00:49:04,239 --> 00:49:07,319
Speaker 1: LLL cool Jay's mama said knock you out, and I

948
00:49:07,440 --> 00:49:08,399
Speaker 1: dedicated it to.

949
00:49:09,880 --> 00:49:12,920
Speaker 2: Let's just say some public figures, and.

950
00:49:14,320 --> 00:49:17,440
Speaker 1: And then I nailed the rendition that is my song.

951
00:49:17,640 --> 00:49:19,520
Speaker 1: If you've ever heard that song, it's a it's like

952
00:49:19,560 --> 00:49:22,760
Speaker 1: a tough one to sing. But I didn't really think

953
00:49:22,840 --> 00:49:27,080
Speaker 1: about the lyrics that are actually quite violent, and I didn't,

954
00:49:27,400 --> 00:49:29,560
Speaker 1: you know, when I got up to do this karaoke song,

955
00:49:29,840 --> 00:49:32,279
Speaker 1: I wasn't thinking this is going to be taken as

956
00:49:32,320 --> 00:49:35,160
Speaker 1: a as a literal promise or a literal threat. After

957
00:49:35,200 --> 00:49:38,160
Speaker 1: the performance, I'm slapping high fives, I'm feeling good.

958
00:49:38,600 --> 00:49:40,000
Speaker 2: The next business day.

959
00:49:40,040 --> 00:49:43,000
Speaker 1: The next Monday, my manager calls me into her office

960
00:49:43,080 --> 00:49:46,160
Speaker 1: and she's got the lyrics to Mama said, knock you

961
00:49:46,239 --> 00:49:49,680
Speaker 1: Out printed out for me to look at. And I

962
00:49:49,760 --> 00:49:52,200
Speaker 1: know this song from singing it at the gym and

963
00:49:52,239 --> 00:49:55,359
Speaker 1: from being a fan of llll Cooljay, but I had

964
00:49:55,400 --> 00:49:58,560
Speaker 1: never actually sat down and looked at these lyrics, which

965
00:49:58,600 --> 00:50:02,160
Speaker 1: are quite explicitly violent, like in the song e Elle

966
00:50:02,200 --> 00:50:05,040
Speaker 1: Cool Jay explicitly says these are lyrics that will make

967
00:50:05,080 --> 00:50:08,080
Speaker 1: you call the cops, Like there's one in particular line

968
00:50:08,280 --> 00:50:11,439
Speaker 1: that he says, don't you never ever pull my lever

969
00:50:11,520 --> 00:50:14,200
Speaker 1: because I explode and my nine is easy to load.

970
00:50:14,600 --> 00:50:17,640
Speaker 1: And I remember my boss reading that to me and

971
00:50:17,680 --> 00:50:20,040
Speaker 1: thinking like, oh, is this really the kind of song

972
00:50:20,080 --> 00:50:22,480
Speaker 1: you want to be singing on stage.

973
00:50:22,120 --> 00:50:25,040
Speaker 2: With such a gusto, And you know she was right.

974
00:50:25,520 --> 00:50:28,560
Speaker 1: Looking back now, I can see how it wasn't super appropriated.

975
00:50:28,560 --> 00:50:31,200
Speaker 1: It wasn't super cool, and I understood that, but yeah,

976
00:50:31,280 --> 00:50:33,040
Speaker 1: I wish I wish I could. I wish I could

977
00:50:33,040 --> 00:50:36,560
Speaker 1: have pointed to this judge's rulings, saying, ah, a judge

978
00:50:36,600 --> 00:50:39,560
Speaker 1: has ruled that we don't take song lyrics literally, even

979
00:50:39,600 --> 00:50:40,879
Speaker 1: at a work karaoke event.

980
00:50:41,280 --> 00:50:43,000
Speaker 3: I'm so glad you told that story. I think it's

981
00:50:43,000 --> 00:50:44,920
Speaker 3: so funny. I've heard that story before, and I just

982
00:50:45,000 --> 00:50:48,520
Speaker 3: love to picture a young bridget like screaming that into

983
00:50:48,560 --> 00:50:50,759
Speaker 3: the mic. I bet you did kill it. Oh, I

984
00:50:50,840 --> 00:50:52,919
Speaker 3: literally killed anything or anybody, but like.

985
00:50:52,960 --> 00:50:55,320
Speaker 2: No one is literally wanting to kill anybody.

986
00:50:55,760 --> 00:50:57,640
Speaker 3: The other funny thing about this trade story is that

987
00:50:57,640 --> 00:51:01,000
Speaker 3: that same judge, Jeannette Varius, she's been involved in a

988
00:51:01,000 --> 00:51:04,000
Speaker 3: lot of these Trump cases and like most notably earlier

989
00:51:04,080 --> 00:51:07,320
Speaker 3: this spring, was like weighing in on whether Doge and

990
00:51:07,400 --> 00:51:12,720
Speaker 3: Elon Musk could access Treasury information. So she's been involved

991
00:51:12,719 --> 00:51:17,200
Speaker 3: in like really high profile, high stakes cases affecting like

992
00:51:17,480 --> 00:51:21,680
Speaker 3: the way that America functions. And I just find something

993
00:51:21,719 --> 00:51:23,279
Speaker 3: funny about the fact that she had to take time

994
00:51:23,320 --> 00:51:26,719
Speaker 3: out from that to weigh in on Drake's hurt feelings.

995
00:51:26,920 --> 00:51:28,880
Speaker 1: I know, I wonder if she was, like, you know

996
00:51:28,920 --> 00:51:31,360
Speaker 1: those half glasses that judges wear kind of at the

997
00:51:31,360 --> 00:51:34,800
Speaker 1: bottom of their nose. Is she pouring over these lyrics

998
00:51:34,840 --> 00:51:37,719
Speaker 1: and watching Kenja Klamar kind of wink wink at the

999
00:51:37,760 --> 00:51:41,120
Speaker 1: camera during a Super Bowl performance, saying, Oh, Drake likes

1000
00:51:41,280 --> 00:51:41,960
Speaker 1: a minor.

1001
00:51:42,360 --> 00:51:44,839
Speaker 2: You know, was she pouring over all of this. I'm

1002
00:51:44,880 --> 00:51:45,560
Speaker 2: so curious.

1003
00:51:46,120 --> 00:51:49,000
Speaker 3: Yeah, I mean she seems like a thorough judge, so

1004
00:51:49,040 --> 00:51:50,480
Speaker 3: I suspect she probably was.

1005
00:51:51,160 --> 00:51:53,960
Speaker 1: That's so funny the world we live in where one

1006
00:51:54,040 --> 00:51:57,680
Speaker 1: day you're making a ruling on high level Trump administration

1007
00:51:57,800 --> 00:52:01,080
Speaker 1: shenanigans and the next day your mediam a beef between

1008
00:52:01,200 --> 00:52:02,520
Speaker 1: Drake and Kendrick Lamar.

1009
00:52:02,640 --> 00:52:05,360
Speaker 2: What a country? Yeah, what a country?

1010
00:52:06,200 --> 00:52:08,200
Speaker 3: So, Bridget, we got to wrap it up here, but

1011
00:52:08,360 --> 00:52:12,600
Speaker 3: I wanted to promote the mail bag if that, If

1012
00:52:12,600 --> 00:52:13,200
Speaker 3: that works for.

1013
00:52:13,120 --> 00:52:15,200
Speaker 2: You, please promote the mail bag.

1014
00:52:15,680 --> 00:52:18,560
Speaker 3: Yeah, so we're gonna do a mail bag episode. We've

1015
00:52:18,560 --> 00:52:20,920
Speaker 3: gotten some great submissions, Thank you so much to everybody

1016
00:52:21,000 --> 00:52:21,839
Speaker 3: who wrote in.

1017
00:52:22,560 --> 00:52:23,440
Speaker 2: We did just like a.

1018
00:52:23,400 --> 00:52:27,360
Speaker 3: Couple more to really have like a full episode. So

1019
00:52:27,400 --> 00:52:30,320
Speaker 3: if you're listening, please write in with a mail bag question.

1020
00:52:30,480 --> 00:52:32,399
Speaker 3: It doesn't even need to be a question. It could

1021
00:52:32,440 --> 00:52:36,840
Speaker 3: just be something that you're thinking about, uh, that you

1022
00:52:36,920 --> 00:52:40,759
Speaker 3: want to get Bridget's take on. Just write in and

1023
00:52:40,840 --> 00:52:42,400
Speaker 3: let us know. Or if you do have a question

1024
00:52:42,520 --> 00:52:44,600
Speaker 3: for Bridget or for me, or about the show, or

1025
00:52:44,600 --> 00:52:47,399
Speaker 3: about anything something you want us to look into for you,

1026
00:52:47,719 --> 00:52:51,799
Speaker 3: happy to do it, but please right into Hello at

1027
00:52:51,840 --> 00:52:56,319
Speaker 3: tangote dot com and we're gonna sweeten the pot that

1028
00:52:56,480 --> 00:52:59,440
Speaker 3: if you if you want a free sticker, you get

1029
00:52:59,440 --> 00:53:02,040
Speaker 3: a free stick for writing in. We've got these cool

1030
00:53:02,080 --> 00:53:04,840
Speaker 3: new tangoty stickers. We'd love to give you one. Just

1031
00:53:04,840 --> 00:53:06,920
Speaker 3: include your address. We're not gonna use it for anything

1032
00:53:06,960 --> 00:53:09,840
Speaker 3: other than sending you a sticker. But yeah, right in

1033
00:53:09,880 --> 00:53:10,239
Speaker 3: and listen.

1034
00:53:10,280 --> 00:53:10,719
Speaker 2: No, I don't know.

1035
00:53:11,239 --> 00:53:13,960
Speaker 3: We're gonna leave it open for one more week. We're

1036
00:53:13,960 --> 00:53:15,960
Speaker 3: gonna extend it a little bit, so Bridget, do you

1037
00:53:16,040 --> 00:53:17,319
Speaker 3: have anything to add to that.

1038
00:53:17,840 --> 00:53:20,400
Speaker 2: The stickers are vinyl. You can put them on water bottles,

1039
00:53:20,600 --> 00:53:21,239
Speaker 2: which I love.

1040
00:53:21,800 --> 00:53:24,279
Speaker 3: Yeah, they're high quality stickers. These these are not the

1041
00:53:24,400 --> 00:53:26,680
Speaker 3: cheap things. Uh, they're the good ones.

1042
00:53:27,040 --> 00:53:29,279
Speaker 2: So write in let us know. We want to do

1043
00:53:29,320 --> 00:53:30,280
Speaker 2: this mailback episode.

1044
00:53:30,280 --> 00:53:33,480
Speaker 1: If we don't get enough submissions, I'll just answer the

1045
00:53:33,560 --> 00:53:36,120
Speaker 1: questions that we got. I guess to the people, to

1046
00:53:36,120 --> 00:53:39,280
Speaker 1: the two people who ask questions, I will just answer them.

1047
00:53:39,719 --> 00:53:43,000
Speaker 1: But yes, Mike, thank you for being here as always,

1048
00:53:43,000 --> 00:53:44,440
Speaker 1: and thanks to all of you for listening.

1049
00:53:44,480 --> 00:53:45,920
Speaker 2: I will see you on the internet.

1050
00:53:51,040 --> 00:53:53,000
Speaker 1: Got a story about an interesting thing in tech. I

1051
00:53:53,160 --> 00:53:55,239
Speaker 1: just want to say hi. You can be just said hello.

1052
00:53:55,280 --> 00:53:57,759
Speaker 1: At tenggoty dot com. You can also find transcripts for

1053
00:53:57,800 --> 00:54:00,560
Speaker 1: today's episode at tengoity dot com. There Are No Girls

1054
00:54:00,560 --> 00:54:02,839
Speaker 1: on the Internet was created by me bridget Toad. It's

1055
00:54:02,880 --> 00:54:06,200
Speaker 1: a production of iHeartRadio and Unbossed Creative. Jonathan Strickland is

1056
00:54:06,239 --> 00:54:09,680
Speaker 1: our executive producer. Tarry Harrison is our producer and sound engineer.

1057
00:54:10,080 --> 00:54:13,279
Speaker 1: Michael Almato is our contributing producer. I'm your host, bridget Tood.

1058
00:54:14,200 --> 00:54:16,080
Speaker 1: If you want to help us grow, rate and review.

1059
00:54:15,960 --> 00:54:17,080
Speaker 2: Us on Apple Podcasts.

1060
00:54:17,760 --> 00:54:20,600
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1061
00:54:20,640 --> 00:54:33,200
Speaker 1: Apple Podcasts, or wherever you get your podcasts.