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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 Bridge Tad and this

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Speaker 1: is there are no girls.

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Speaker 2: On the Internet.

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Speaker 1: Okay, Mike, I want to start out our banter by

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Speaker 1: asking you what I hope will become kind of a

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Speaker 1: new episode series, Low level Internet complaints. So what is

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Speaker 1: something that has been grinding your gears? Nothing big? Not

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Speaker 1: trying to have this be a fleshed out thing, because

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Speaker 1: I have these little complaints and I'm like, well, actually,

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Speaker 1: did you know it's connected to this larger problem that

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Speaker 1: we should actually really talk about. So something kind of petty.

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Speaker 1: Can you give me a low level, petty internet gripe

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Speaker 1: that's been grinding your gears online or intech or in

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Speaker 1: culture lately?

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Speaker 3: Yeah, you know, I can give you a petty little gripe.

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Speaker 3: The thing that's been just grinding my gears lately is

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Speaker 3: how every time you open an a or a website

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Speaker 3: or an application on your desktop computer, there's one hundred

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Speaker 3: little pop ups that are pointing out new features or

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Speaker 3: directing my attention to some tool to use, or giving

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Speaker 3: me a helpful little tip. And often the tips are

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Speaker 3: like useful, I guess, but I don't want to hear

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Speaker 3: about new features. I just want to do the thing

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Speaker 3: that I came here to do. Adobe Acrobat is a

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Speaker 3: pretty bad offender in this category. I like, every time

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Speaker 3: I open it, there's some little pop up telling me

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Speaker 3: about something that I don't care about.

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Speaker 4: I just want to look at my BDF.

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Speaker 3: So it's gotten out of hand and somebody's got to

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Speaker 3: do something about this.

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Speaker 1: It's like twenty twenty three's version of Clippy. Remember Clippy?

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Speaker 4: I remember Clippy? Yeah? Uh, you know, I kind of

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Speaker 4: missed that little guy.

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Speaker 1: Yeah, I think that in twenty twenty three, the Internet

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Speaker 1: experience has gotten so janky, Like there are websites where

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Speaker 1: you basically can't use them because of the amount of

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Speaker 1: pop ups and things that load and then like an

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Speaker 1: AutoPlay video for an ad like I am ashamed to

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Speaker 1: admit how much bad celebrity tabloid reporting I read on,

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Speaker 1: like really sketchy sites that I have no business on.

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Speaker 1: You know, your daily mails of the world, and you

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Speaker 1: have to be so intentional and careful where you hover

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Speaker 1: your mouth because if you accidentally click off the article

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Speaker 1: in any way, you could be looking at a pop

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Speaker 1: up like auto playing a video that breaks the entire thing.

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Speaker 1: I can't tell you how many times I've pulled up

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Speaker 1: an article on my phone, so many things start to

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Speaker 1: load and break that I'm just like, oh, I guess

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Speaker 1: I'll never know. I'd nope right out of that so quick.

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Speaker 3: Yeah, I've been there where you're like just trying to

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Speaker 3: scroll to read an article and all of a sudden,

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Speaker 3: as your finger is approaching the screen, a video appears

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Speaker 3: out of nowhere, and now you've clicked the video and

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Speaker 3: you're going to god knows where in your browser.

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Speaker 1: Yeah. My petty internet complaint is, well, the first one

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Speaker 1: is not an Internet complaint. It's that I went to

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Speaker 1: to a Christmas party last night, a work Christmas party,

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Speaker 1: and I'm a little bit hungover. So folks listening, if

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Speaker 1: I don't sound like I have the usual pep in

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Speaker 1: my step, that might be why.

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Speaker 4: Yeah, we're going to do our best, though. I think

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Speaker 4: we'll get there.

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Speaker 1: I think we'll get there. Well. My other internet, actual

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Speaker 1: internet small time complaint is why does everything have to

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Speaker 1: be an app these days? Like, not everything needs to

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Speaker 1: be an app? I was at McDonald's pretty recently during

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Speaker 1: a car trip, and I was thinking to myself, like Wow,

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Speaker 1: these prices have really gone up. I was curious later

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Speaker 1: when I got to my destination, so I did a

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Speaker 1: bit of googling and I found this Reddit thread about

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Speaker 1: how the prices at McDonald's maybe seem like they have

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Speaker 1: gotten higher, but that if you use their app you

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Speaker 1: can actually get really good deals with all these tips

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Speaker 1: and tricks on how to combine purchases so that you

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Speaker 1: could really save money. And I was thinking, why do

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Speaker 1: I have to You know, I understand that they want

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Speaker 1: McDonald's wants to incentivize people to download their app, but

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Speaker 1: shouldn't the prices just be the prices? Like why do

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Speaker 1: I have to engage with an app just to get

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Speaker 1: whatever price on my whatever I'm buying at McDonald's. I

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Speaker 1: you know, I do a lot of traveling, and sometimes

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Speaker 1: when I travel, I'll use wherever I'm at, like their

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Speaker 1: version of the bike share program, like DC has Capitol

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Speaker 1: bike Share. I probably have four different apps on my

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Speaker 1: phone from countries that not only do I not live in,

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Speaker 1: I'll probably not be visiting again anytime soon for their

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Speaker 1: specific bike share program. It just really adds up. Not

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Speaker 1: everything needs to be an app people.

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Speaker 3: Yeah, well, especially you know the idea that you have

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Speaker 3: to use the app to get decent prices. It's like

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Speaker 3: the twenty twenty three version of coupon clipping, right, like

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Speaker 3: stores like bed Bath and Beyond where the prices are

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Speaker 3: really high, but you can get crazy deals with coupons.

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Speaker 3: It's just like that, and I don't want to do that.

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Speaker 3: I don't want to clip coupons.

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Speaker 1: Yeah. I actually write this really interesting article about this woman,

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Speaker 1: I think some place in the UK, an older woman

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Speaker 1: in her seventies who tried to park and she had

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Speaker 1: to pay to park using like a machine, but the

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Speaker 1: machine was broken, and so she was like, well, I'm

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Speaker 1: not going to pay like this machine broken, and she

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Speaker 1: got a ticket because the parking authority was like, oh,

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Speaker 1: well you should have downloaded our app and paid in

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Speaker 1: the app, and she was like, I don't know how

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Speaker 1: to download an app. I'm in my seventies. I'm not

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Speaker 1: going to download your app. I think she called it, like,

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Speaker 1: I will not cowtowt are these bully tactics, And I'm

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Speaker 1: one hundred percent on her team, Like you should not

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Speaker 1: if a city service requires you to engage with an

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Speaker 1: app even if you don't want to, that is not

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Speaker 1: an accessible city service. Not everything needs to be an app.

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Speaker 1: I hope this woman never pays that. Fine. She is

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Speaker 1: a hero in my eyes.

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Speaker 3: Yeah, we should. We should look into that. Do a

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Speaker 3: follow up, because I'm with you. This woman is a hero.

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Speaker 3: She's standing firm, standing up for her principles. Does not

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Speaker 3: want to download this app.

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Speaker 1: Okay, So speaking of heroes, we get to break a

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Speaker 1: little bit of news. In this episode, we've been covering

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Speaker 1: the decimation case that Georgia election workers turned American heroes

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Speaker 1: Shay Moss and Ruby Freeman, filed against Rudy Giuliani after

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Speaker 1: he baselessly and repeatedly accused that of tampering with the

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Speaker 1: results of the Georgia election in twenty twenty. These lies

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Speaker 1: absolutely tore their lives upside down. They had to flee

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Speaker 1: for their own safety because an angry mob descended on

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Speaker 1: their home to try to make a citizen's arrest. Well,

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Speaker 1: the verdict is in and Giuliani has been ordered to

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Speaker 1: pay the women almost one hundred and fifty million dollars

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Speaker 1: in damages. What's even wilder about this is that during

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Speaker 1: the court proceedings, Giuliani's lawyers conceded that there was no

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Speaker 1: proof to the lies that he spread about the women

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Speaker 1: who were just trying to do their jobs. But then

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Speaker 1: Rudy Giuliani went outside the courthouse and told the media

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Speaker 1: that he stood by those lies that the two women

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Speaker 1: tampered with votes during the election, even as in court

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Speaker 1: he and his attorneys conceded that wasn't true. Listen story,

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Speaker 1: and it.

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Speaker 4: Will be.

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Speaker 3: Clear what I said was true and that whatever happened

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Speaker 3: to them, which is which unfortunate is for other people overreact.

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Speaker 1: Thing I said about them is true.

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Speaker 2: Do you regret what you did to Of course I

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Speaker 2: don't regret I told the truth. They were engaged in

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Speaker 2: changing vote.

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Speaker 1: There's no proof of that. Stay tuned like the man

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Speaker 1: just cannot help himself. So if you want more information

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Speaker 1: about what happened in this case, we'll put the link

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Speaker 1: to some of our earlier episodes about Ruby Freeman and

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Speaker 1: Shay Moss. In the show notes, these women are heroes.

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Speaker 1: They were put through hell just for doing their jobs

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Speaker 1: of trying to help folks in their community vote. They

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Speaker 1: risked facing even more attacks by bravely testifying about what

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Speaker 1: they experienced at the hands of Donald Trump and Rudy Giuliani.

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Speaker 1: And while I am very glad that they got money

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Speaker 1: for what they went through. God knows they deserve it.

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Speaker 1: Nothing can make it right, and it should not have

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Speaker 1: happened in the first place. We should have a country

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Speaker 1: that values the women, and let's be clear, most of

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Speaker 1: them are women statistically who do the work at polling

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Speaker 1: places and vote counting center that make democracy function. And

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Speaker 1: when those folks face attacks, it threatens democracy for all

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Speaker 1: of us. Protecting democracy ought to be a value that

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Speaker 1: we stick to. Speaking of sticking to their values, as

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Speaker 1: we know, Twitter has told their advertisers to go f

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Speaker 1: themselves as a value. Let's find out how that's working

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Speaker 1: for them. Bit of a warning here, because this is

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Speaker 1: going to get a little bit gross. I believe that

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Speaker 1: it looks like Twitter has been really scraping the bottom

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Speaker 1: of the barrel for advertisers these days, and this has

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Speaker 1: got to be a new low. Because I saw this

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Speaker 1: ad for some kind of a like semen stealing company.

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Speaker 1: I know that sounds weird, but it's like a home

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Speaker 1: insimination kit, basically advertising that you can collect semen and

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Speaker 1: make yourself pregnant without the man's consent. So not only

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Speaker 1: is that like just physically grows and like sticky and yucky,

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Speaker 1: but also it is ethically gross. The kit appears to

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Speaker 1: be an actual, real product, but it's not clear to

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Speaker 1: me if this is like an ad campaign that is

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Speaker 1: real or just designed to get people talking about it

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Speaker 1: to get viral attention. I mean, we're talking about it.

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Speaker 1: Some people have suggested that the ads are promoting a

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Speaker 1: legal behavior, but it actually turns out that there are

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Speaker 1: not laws against stealing seamen, which I was surprised to find.

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Speaker 1: I was like, oh, certainly this is Twitter making money

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Speaker 1: by promoting illegal activity. Not so. But just because it's

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Speaker 1: not illegal does not mean that it is ethical behavior

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Speaker 1: even though it's not like technically against the law. I

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Speaker 1: know this sounds kind of outlandish, but listeners might remember

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Speaker 1: a rumor, and I want to emphasize rumor about a

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Speaker 1: certain Canadian rapper Drake, from a few years ago that

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Speaker 1: he would allegedly put hot sauce in all of his

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Speaker 1: used condoms after sexual encounters to prevent people that he

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Speaker 1: was sleeping with from doing exactly what this ad suggests,

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Speaker 1: i e. Stealing his sperm to try to get themselves pregnant.

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Speaker 1: I have no idea if this is true or not.

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Speaker 1: I will say that, like, it does sound like an

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Speaker 1: incredible amount of effort to go through to prevent someone

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Speaker 1: from rooting through the garbage and then steal your seamen

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Speaker 1: to impregnate themselves.

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Speaker 3: Yeah, pretty hard to imagine doing that. But the Internet

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Speaker 3: couldn't have just made that up.

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Speaker 1: You think people would just get on the Internet and

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Speaker 1: lie about Drake like that.

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Speaker 4: Never, no way.

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Speaker 1: Okay, So back to Twitter. I do think this is

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Speaker 1: just another example of how bad things have gotten on

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Speaker 1: the platform. Bad in terms of user experience, like earlier

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Speaker 1: this week, all all non Twitter links were broken, so

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Speaker 1: you clicked on any link that wasn't a Twitter link,

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Speaker 1: it was like sorry, four or four page doesn't work.

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Speaker 1: But also bad in terms of user experience because who

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Speaker 1: wants to see ads like this in their feed? It

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Speaker 1: also has to be bad in terms of advertising revenue,

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Speaker 1: which we know has been declining ever since Elon Musk

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Speaker 1: took over, and I just think presumably it must have

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Speaker 1: gotten worse since his whole telling advertisers to go at

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Speaker 1: themselves moment a couple of weeks ago. You know, ads

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Speaker 1: on Twitter, Like, nobody likes looking at ads, but they

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Speaker 1: used to be ads for things that you had heard

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Speaker 1: of like real products that you're like, oh, sure, this product,

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Speaker 1: that product, that brand, But lately I think they have

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Speaker 1: really gone off the wall. I saw another set of

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Speaker 1: ads on Twitter for one of those really gross newdify

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Speaker 1: apps that promises to let users use AI to undress

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Speaker 1: anyone without their permission. I actually find myself like blocking

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Speaker 1: certain obnoxious ads that just like keep popping up. I

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Speaker 1: feel like they're like, at least when they were from

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Speaker 1: real companies, they weren't so weird and over the top.

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Speaker 1: So I could be wrong, but I imagine this company behind

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Speaker 1: the you know, artificially surreptitiously infeminate yourself at home kit.

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Speaker 1: It's probably not a particularly large company, so the fact

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Speaker 1: that their ads are all over Twitter really suggested to

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Speaker 1: me that they got a great deal on these ads,

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Speaker 1: which makes sense because Twitter's big advertisers are still fleeing

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Speaker 1: the platform, so they probably have a ton of unsold

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Speaker 1: ad inventory and they're just like giving great deals to

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Speaker 1: whatever sketchy, borderline illegal or like, you know, maybe unechical

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Speaker 1: products want that space. Like right now, I've been getting

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Speaker 1: so many ads on Twitter for clearly knockoff luxury handbags,

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Speaker 1: which really takes me back to that conversation we had

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Speaker 1: about TikTok, about how TikTok had to crack down on that, right, Like,

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Speaker 1: if somebody is trying to sell you Louis Vuittona handbag

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Speaker 1: that they say is legit for like forty dollars, it's

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Speaker 1: probably a signal that like something weird, if not illegal,

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Speaker 1: is going on. But I say all this to say

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Speaker 1: that if you are listening and you have a product

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Speaker 1: that you would like to sell, and the brand alignment

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Speaker 1: for that product is like Alex Jones denying that Sandy

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Speaker 1: hook ever happened and calling murdered children crisis actors and

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Speaker 1: ads for stealing semen, Twitter might be the place.

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Speaker 4: For you, man. That is not a product that I

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Speaker 4: probably want.

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Speaker 1: Okay, so palate cleanser needed for that story. I have

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Speaker 1: a little bit of good news for y'all. Y'all know

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Speaker 1: that I believe deeply in celebrating wins when we get them,

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Speaker 1: and this is actually some great news. Discord has explicitly

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Speaker 1: banned misgendering and dead naming trans people as part of

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Speaker 1: an update to its hate conduct policy.

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Speaker 4: So.

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Speaker 1: Discord is a social media messaging platform that is really

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Speaker 1: popular with gamers. Discord actually updated this internal hate speech

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Speaker 1: policy back in April of twenty twenty two, but only

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Speaker 1: recently went public with these changes as part of their

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Speaker 1: regular review to improve transparency. So the updated hate speech

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Speaker 1: policy now prohibits quote repeatedly using slurs to degrade and

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Speaker 1: demean individuals or groups, which includes dead naming or misgendering

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Speaker 1: a transgender person. Glad outlined this change, and a report

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Speaker 1: will link to the whole thing. Honestly, the whole thing

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Speaker 1: is worth a reading. It was very interesting. But something

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Speaker 1: that they point out in this report is that transphobia,

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Speaker 1: specifically things like misgendering and dead naming, has become a

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Speaker 1: really reliable way for right wing grifters to build out

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Speaker 1: their platforms online, which you know, I always knew innately,

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Speaker 1: but I never really thought about it that much or

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Speaker 1: really thought about how much I have seen that in

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Speaker 1: action until reading this report. The report reads the trope

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Speaker 1: is extremely popular amongst high follower anti LGBTQ accounts, and

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Speaker 1: is especially utilized to bully, mock, and harass prominent trans

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Speaker 1: public figures. Admiral Rachel Levine, Dylan mulvaney, sixteen year old

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Speaker 1: Zaia Wade to name a few. This strategy of targeting

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Speaker 1: well known people serves to escalate visibility and engagement on posts,

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Speaker 1: while it also functions as a vehicle to express general

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Speaker 1: hatred of trans and non binary people and the community

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Speaker 1: as a whole. They also make this other really interesting

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Speaker 1: point which I never really thought about in these terms before,

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Speaker 1: and that is the way that dead naming and misgendering

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Speaker 1: famous people, especially as a way to get clicks and

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Speaker 1: engagements through celebrity name dropping, is this like tried and

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Speaker 1: true engagement tactic, which again I had just like never

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Speaker 1: really thought about that before, because you know, your Ben

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Speaker 1: Shapiro's and you're Jordan Peterson's of the world, Like, these

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Speaker 1: are people who certainly don't want to think of themselves

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Speaker 1: as like making celebrity content, right, Like they would probably

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Speaker 1: object to that, to that characterization of themselves. However, you

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Speaker 1: do get attention and traction and engagement when you mention

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Speaker 1: a celebrity because people like reading about celebrities, and so

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Speaker 1: they're basically using celebrity, like using the meeting trans celebrities

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Speaker 1: as a way to capitalize on the attention that comes

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Speaker 1: with talking about celebrity. But in a way that kind

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Speaker 1: of lets them be like, well, because these people are

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Speaker 1: like genuinely obsessed with celebrities like I. It's it's like

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Speaker 1: a it's like a weird kind of anti standom where

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Speaker 1: we're essentially, you know, the same thing that drives a

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Speaker 1: young person to make an obsessive Tumbler account dedicated to

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Speaker 1: Taylor Swift and Taylor Swift fandom. It's like not totally dissimilar,

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Speaker 1: just in the other direction, Like, these people are genuinely

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Speaker 1: obsessed with celebrities and what's going on with them?

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Speaker 3: What a gross way to get attention and build a

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Speaker 3: platform by like targeting trans people, like give them, give

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Speaker 3: them a break?

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Speaker 1: And I really hate that it's effective, and these people

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Speaker 1: clearly know it's effective. That's why they continue to do it.

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Speaker 1: So to put this change that Discord has made in context,

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Speaker 1: right now, of the six major social media platforms TikTok, YouTube, Twitter, Facebook, Instagram,

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Speaker 1: and threads right now, only TikTok expressly prohibits targeted misgendering

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Speaker 1: and dead naming and its hate and harassment policy full disclosure,

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Speaker 1: that was a policy that I personally worked with Glad

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Speaker 1: to help develop. Twitter actually used to be on the

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Speaker 1: list of places that expressly prohibited dead naming and misgendering. However,

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Speaker 1: it was like one of the very one of the first,

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Speaker 1: if not the first policy that Elon rolled back once

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Speaker 1: he took over Twitter. So now of course dead naming

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Speaker 1: and transphobia runs rampant.

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Speaker 3: Yeah, like, it's just so messed up dead naming as

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Speaker 3: a value, right, Like it's positively asserting his right to

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Speaker 3: dead name so hateful.

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Speaker 1: Yeah, I mean, I will never stop railing about this.

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Speaker 1: But that that as a value is a core value

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Speaker 1: is what motivated Elon Must to purchase Twitter in the

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Speaker 1: first place. And so you know, transphobia, hostility to trans

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Speaker 1: people's continues to really shape the way the Internet works

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Speaker 1: for everybody. So importantly, the announcement of these changes over

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Speaker 1: at Discord also went along with some transparency around how

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Speaker 1: it will be enforced, which is so important because a

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Speaker 1: policy change without actual enforcement mechanisms is like just a wish,

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Speaker 1: It doesn't really do anything. So critics of these kinds

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Speaker 1: of policies usually make it seem like like, oh, just

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Speaker 1: getting someone's name wrong once means they're gonna be banned,

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Speaker 1: and that just like isn't really the case, and it's

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Speaker 1: not the case that Discord either. So Discord outlined the

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Speaker 1: consequences of violating this new hateful conduct policy by explaining

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Speaker 1: their warning system, so when a user is reported for

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Speaker 1: breaking in the rules, they will get a direct message

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Speaker 1: from Discord letting them know about that violation. Discord's actual

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Speaker 1: actions and response to an infraction depend on the severity

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Speaker 1: of harm, the type of user content, and that user's

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Speaker 1: history of past violations. Discord says that if the violation

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Speaker 1: was not particularly severe, that person may lose these features

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Speaker 1: for a few hours. If it was a repeated violate

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Speaker 1: or a higher severity violation, they may lose some features

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Speaker 1: for a few days up to one year. And yeah,

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Speaker 1: I think that really making it clear how this will

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Speaker 1: work is key to any policy on social media, Like

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Speaker 1: I think there needs to be transparency and also consistent enforcement,

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Speaker 1: because that's when those when when policies don't have transparency

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Speaker 1: and consistent enforcement, not only does it undermine those policies obviously,

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Speaker 1: but it also just gives credence of people who are like, oh, well,

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Speaker 1: this is just like the PC police that don't let

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Speaker 1: you say anything. You really need to have that consistency

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Speaker 1: and transparency for these policies not just to have any teeth,

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Speaker 1: but also to like help people understand that these policies

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Speaker 1: are a good thing to actually allow people to have

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Speaker 1: discourse online, and that these kinds of policies are not

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Speaker 1: incongruous with you know, fostering Internet spaces to be places

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Speaker 1: where people can actually meaningfully have discourse and enjoy the

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Speaker 1: free speech that people you know love to talk.

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Speaker 4: Yeah.

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Speaker 3: Yeah, transparency, it's great, and it also implies that they

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Speaker 3: have sufficient staff who will be monitoring, you know, reports

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Speaker 3: and taking action. So you know, unlike Twitter where they

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Speaker 3: just laid off like almost the entire trust and safety team,

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Speaker 3: it seems like Discord is really taking it seriously, which

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Speaker 3: is great to see.

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Speaker 1: Yeah, and I would say, I mean it's happening at

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Speaker 1: Twitter for sure, but I do know that that is

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Speaker 1: becoming a more common thing right now. You know, cuts

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Speaker 1: to trust and safety teams, cuts to the teams that

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Speaker 1: handle these kinds of things, and that's something I think, like,

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Speaker 1: so it would be great if Discord is doing the opposite.

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Speaker 1: They're actually fostering and like supporting and growing out the

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Speaker 1: people who do that kind of work within the company.

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Speaker 3: Yeah. Yeah, and it seems smart for them, right like,

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Speaker 3: Unlike Twitter, where advertisers are fleeing, Discord has so many

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Speaker 3: partnerships with like big celebrities, gaming celebrities often reaching young

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Speaker 3: people explicitly, and you know, I think that's been a

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Speaker 3: really valuable thing for them, and so it makes sense

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Speaker 3: that they would want to protect it absolutely.

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Speaker 2: Let's take a quick break at er back.

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Speaker 1: So this week, Congress was supposed to vote on a

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Speaker 1: bill to reauthorize part of the Foreign Intelligence Surveillance Act,

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Speaker 1: commonly referred to as FAIZA, originally passed by Congress in

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Speaker 1: nineteen seventy eight to cover technologies like telephones and fax

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Speaker 1: machines way back when before the Internet, back when it

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Speaker 1: was the Internet was just like a twinkle in Al

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Speaker 1: Gore's eye. So FAIZA is the law gives US law

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Speaker 1: enforcement broad sweeping authorities to basically monitor the communication of

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Speaker 1: anybody who is not a US citizen. But because of

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Speaker 1: how broad that law is, many US citizens actually end

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Speaker 1: up getting caught in it and surveilled as well, oftentimes

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Speaker 1: without even really knowing it. This law has been widely

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Speaker 1: criticized by civil rights groups and privacy groups. So the

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Speaker 1: bill Congress was supposed to be voting on this week

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Speaker 1: would have specifically reformed a part of FIZA called Section

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Speaker 1: seven ZH two, which allows the US government to demand

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Speaker 1: access to data from communications companies like internet and cell

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Speaker 1: phone service providers. So it's a pretty like astonishing and

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Speaker 1: frightening level of surveillance because it allows law enforcement to

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Speaker 1: not only see who is talking to who, but also

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Speaker 1: the content of their communications. It not only allows the

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Speaker 1: government to target any foreign nationals communicating from outside the country,

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Speaker 1: which is already pretty intense, but also the communications of

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Speaker 1: whoever they might be talking to inside the country, including

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Speaker 1: US citizens. So it is bad news for anybody who

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Speaker 1: values things like privacy, and there is already a pretty

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Speaker 1: well documented history of this being abuse already. It is

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Speaker 1: one of those rare instances where there is bipartisan consensus

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Speaker 1: that this law is too broad and needs to be reformed. However,

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Speaker 1: the bill Congress was planning to vote on this week

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Speaker 1: would have taken things in another direction and expanded the

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Speaker 1: surveillance aspect of FAISA to include not only telecommunication companies

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Speaker 1: but also devices like Wi Fi routers. We'll throw a

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Speaker 1: link in the show notes to a really good Vice

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Speaker 1: article that does a nice job going into further details

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Speaker 1: for anybody who's curious. Okay, Hi, this is Friday Bridget.

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Speaker 1: We've recorded this on Thursday, and there's actually been some

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Speaker 1: new updates on this, So in the two days since

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Speaker 1: we originally recorded this, the situation has changed. Although the

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Speaker 1: house build that was designed to expand FISA was pulled

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Speaker 1: at the last minute on Tuesday for lack of support.

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Speaker 1: On Wednesday, Speaker Mike Johnson added a four month extension

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Speaker 1: of FISA into the must pass National Defense Bill, which

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Speaker 1: Congress then approved. This was a pretty controversial move that's

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Speaker 1: gotten a lot of criticism from both Democrats and Republicans,

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Speaker 1: many of whom understandably did not want to extend the

436
00:23:06,040 --> 00:23:09,320
Speaker 1: warrantless surveillance of Americans. So it looks like for now,

437
00:23:09,920 --> 00:23:12,639
Speaker 1: faiza's Section seven h two will continue to be the

438
00:23:12,720 --> 00:23:15,560
Speaker 1: law of the land until April twenty twenty four, and

439
00:23:15,680 --> 00:23:20,159
Speaker 1: hopefully then Congress will finally reform this egregious violation of

440
00:23:20,200 --> 00:23:25,000
Speaker 1: all of our civil liberties. So speaking of telecommunications companies

441
00:23:25,080 --> 00:23:28,760
Speaker 1: and our privacy just being put in the wrong hands,

442
00:23:29,520 --> 00:23:33,080
Speaker 1: this is one of the most outrageous stories I have

443
00:23:33,200 --> 00:23:36,640
Speaker 1: ever read in my entire life. So quick heads up

444
00:23:36,680 --> 00:23:40,280
Speaker 1: that this story does involve stalking and abuse, and just

445
00:23:40,359 --> 00:23:43,080
Speaker 1: like is one of the most horrific things I've ever heard.

446
00:23:43,520 --> 00:23:47,120
Speaker 1: So Verizon, the cell service provider, gave a woman stalker

447
00:23:47,320 --> 00:23:51,480
Speaker 1: her home address and phone records after he pretended pretty

448
00:23:51,600 --> 00:23:55,679
Speaker 1: badly to be from law enforcement. He was then arrested

449
00:23:55,880 --> 00:23:59,720
Speaker 1: near her home while carrying a knife. So Robert Michael

450
00:23:59,720 --> 00:24:02,399
Speaker 1: Glong and his victim met this year online and they

451
00:24:02,440 --> 00:24:06,680
Speaker 1: had an online romantic relationship. When this woman ended the relationship,

452
00:24:07,200 --> 00:24:10,080
Speaker 1: he continued to try to contact her over and over

453
00:24:10,119 --> 00:24:14,080
Speaker 1: and over again. Ours technical reports that he basically tricked

454
00:24:14,160 --> 00:24:18,080
Speaker 1: Verizon into releasing her home address and her phone records

455
00:24:18,080 --> 00:24:20,600
Speaker 1: to him by sending an email with a fake search

456
00:24:20,680 --> 00:24:24,400
Speaker 1: warrant to the email address for Verizon's Security Assistance team,

457
00:24:24,560 --> 00:24:28,040
Speaker 1: which handles those kinds of legal requests. Verizon did not

458
00:24:28,280 --> 00:24:31,399
Speaker 1: realize that this request was fraudulent, even though it came

459
00:24:31,480 --> 00:24:34,760
Speaker 1: from a proton mail email address rather than like a

460
00:24:34,800 --> 00:24:38,240
Speaker 1: police department or some kind of government agency email address.

461
00:24:38,400 --> 00:24:40,600
Speaker 1: So this is like a basically like the same thing

462
00:24:40,640 --> 00:24:43,840
Speaker 1: as a Gmail. It's an incredibly popular and common used

463
00:24:43,880 --> 00:24:49,000
Speaker 1: email domain. So the email address was Stephen nineteen sixty

464
00:24:49,040 --> 00:24:52,960
Speaker 1: six c at Proton dot me and the Honestly, the

465
00:24:53,000 --> 00:24:55,600
Speaker 1: email also sounds like if that's not fishy enough, this

466
00:24:55,720 --> 00:24:58,640
Speaker 1: was the email that he sent that was effective. It's

467
00:24:58,680 --> 00:25:01,159
Speaker 1: super badly written in full of typhos. So I'm going

468
00:25:01,240 --> 00:25:03,640
Speaker 1: to read it as is and you tell me if

469
00:25:03,640 --> 00:25:05,720
Speaker 1: this would have fooled you into thinking this is actually

470
00:25:05,720 --> 00:25:09,520
Speaker 1: from law enforcement. So here is the PDF file for

471
00:25:09,640 --> 00:25:13,359
Speaker 1: search warrant. We are in need if the this cell

472
00:25:13,400 --> 00:25:17,159
Speaker 1: phone data as soon as possible to locate and apprehend

473
00:25:17,280 --> 00:25:20,320
Speaker 1: this suspect. We also need the full name of this

474
00:25:20,480 --> 00:25:24,000
Speaker 1: Verizon subscriber and the new phone number that has been

475
00:25:24,040 --> 00:25:28,160
Speaker 1: assigned to her. Thank you. So that email just does

476
00:25:28,200 --> 00:25:31,040
Speaker 1: not sound right to me. I truly don't know how

477
00:25:31,080 --> 00:25:33,359
Speaker 1: somebody at Verizon read that and was like, oh, checks out.

478
00:25:33,840 --> 00:25:38,960
Speaker 3: Yeah, we are all trained by a one thousand scammers

479
00:25:39,080 --> 00:25:41,920
Speaker 3: every day who like spam us in our email, spam

480
00:25:42,000 --> 00:25:46,119
Speaker 3: us via text message, and like everything about that just

481
00:25:46,160 --> 00:25:51,360
Speaker 3: screams fake, right. It's so curious that somebody at Verizon

482
00:25:51,560 --> 00:25:53,879
Speaker 3: would respond to it. I wonder what they have to

483
00:25:53,920 --> 00:25:54,760
Speaker 3: say for themselves.

484
00:25:55,080 --> 00:25:57,359
Speaker 1: So the email also had a fake aff a dayt

485
00:25:57,400 --> 00:26:02,000
Speaker 1: written by Detective Steven Koop of the Carrie, North Carolina,

486
00:26:02,040 --> 00:26:04,520
Speaker 1: Police Department. So that's why the email address is like

487
00:26:04,560 --> 00:26:08,200
Speaker 1: Steven nineteen sixty six. See, because everybody knows that when

488
00:26:08,200 --> 00:26:11,720
Speaker 1: you work for the police, you use your first name

489
00:26:11,880 --> 00:26:13,800
Speaker 1: and I guess the year you were born in your

490
00:26:13,800 --> 00:26:14,840
Speaker 1: email address.

491
00:26:15,080 --> 00:26:16,680
Speaker 4: Yeah, totally checks out.

492
00:26:17,200 --> 00:26:19,480
Speaker 1: So, just in case you were curious, there is no

493
00:26:19,640 --> 00:26:22,680
Speaker 1: person named Stephen Cooper working at the Carry Police Department.

494
00:26:22,720 --> 00:26:24,960
Speaker 1: A quick phone call to them probably would have confirmed

495
00:26:25,000 --> 00:26:27,640
Speaker 1: that if anybody working at Verizon wants a tip there.

496
00:26:28,359 --> 00:26:31,800
Speaker 1: He also forged a real judge's signature on a fake

497
00:26:31,880 --> 00:26:35,359
Speaker 1: search warrant, so once he got her contact information, he

498
00:26:35,359 --> 00:26:38,320
Speaker 1: started contacting her parents. And this is not even the

499
00:26:38,359 --> 00:26:40,639
Speaker 1: first time he has done this kind of thing. He

500
00:26:40,800 --> 00:26:43,200
Speaker 1: was wanted by the San Diego Sheriff's Office on a

501
00:26:43,280 --> 00:26:46,439
Speaker 1: charge of stalking an ex girlfriend. In that case, a

502
00:26:46,480 --> 00:26:49,400
Speaker 1: police report documented that the victim had changed her phone

503
00:26:49,440 --> 00:26:52,280
Speaker 1: number four times in the last four months, but somehow

504
00:26:52,440 --> 00:26:56,000
Speaker 1: he keeps getting her number. I wonder how it sounds

505
00:26:56,000 --> 00:26:57,400
Speaker 1: like this was not the first time that he's done

506
00:26:57,400 --> 00:26:58,040
Speaker 1: this kind of thing.

507
00:26:58,440 --> 00:26:58,840
Speaker 4: Wow.

508
00:26:58,960 --> 00:27:02,240
Speaker 1: Yeah, so it gets a lot worse. Once he had

509
00:27:02,280 --> 00:27:05,800
Speaker 1: her information, he traveled from New Mexico to North Carolina

510
00:27:06,040 --> 00:27:08,520
Speaker 1: and sent her a message reading I'm just going to

511
00:27:08,600 --> 00:27:10,760
Speaker 1: turn around, stop at a Big five and get me

512
00:27:10,800 --> 00:27:13,879
Speaker 1: a fucking rifle and some ammunition. And if I can't

513
00:27:13,880 --> 00:27:16,439
Speaker 1: have you, no one can. You want to treat me

514
00:27:16,560 --> 00:27:20,360
Speaker 1: like this, We'll fuck you. So this woman was understandably

515
00:27:20,560 --> 00:27:22,879
Speaker 1: terrified for her life. She calls nine to one one.

516
00:27:23,240 --> 00:27:25,800
Speaker 1: They put officers to stick out her home. So this

517
00:27:25,880 --> 00:27:29,120
Speaker 1: is from the police report. Glanner stopped directly in front

518
00:27:29,160 --> 00:27:31,760
Speaker 1: of her house before entering the neighbor's yard and standing

519
00:27:31,800 --> 00:27:34,680
Speaker 1: in a darkened area, at which time members of the

520
00:27:34,760 --> 00:27:38,239
Speaker 1: Raleigh Police Department arrested him. He was found with a

521
00:27:38,280 --> 00:27:42,399
Speaker 1: folding razor blade knife on his person two mobile phone devices.

522
00:27:42,640 --> 00:27:45,800
Speaker 1: One of these phones displayed the image of the victim

523
00:27:46,119 --> 00:27:49,399
Speaker 1: as the lock screen. When police searched his car, they

524
00:27:49,440 --> 00:27:53,680
Speaker 1: found a glass meth pipe, eight grams of suspected methmphetamine,

525
00:27:54,000 --> 00:27:57,359
Speaker 1: and two brand new bundles of rope that were still

526
00:27:57,400 --> 00:28:00,560
Speaker 1: in plastic wrap. So he has been arrested in jail

527
00:28:00,640 --> 00:28:02,919
Speaker 1: now with a five hundred and fifty thousand dollars bond.

528
00:28:03,440 --> 00:28:08,080
Speaker 1: So honestly, the fact that this kind of low effort

529
00:28:08,400 --> 00:28:15,040
Speaker 1: attempt to trick Verizon was successful is just genuinely baffling

530
00:28:15,040 --> 00:28:17,320
Speaker 1: to me, not to mention terrifying, like it would be

531
00:28:17,400 --> 00:28:21,600
Speaker 1: like making an email address that's like Stevenpolice at gmail

532
00:28:21,640 --> 00:28:24,760
Speaker 1: dot com and then sending that email and having it

533
00:28:24,800 --> 00:28:26,639
Speaker 1: work like I cannot believe that it worked.

534
00:28:26,920 --> 00:28:27,960
Speaker 4: Yeah, it is scary.

535
00:28:28,000 --> 00:28:31,720
Speaker 3: I mean, you were just talking about Faiza and all

536
00:28:31,880 --> 00:28:36,960
Speaker 3: the abuse of that surveillance law, and I think a

537
00:28:36,960 --> 00:28:41,160
Speaker 3: lot of it looks like this, right, like this or

538
00:28:41,200 --> 00:28:41,560
Speaker 3: maybe not?

539
00:28:41,640 --> 00:28:42,640
Speaker 4: Maybe this is rare.

540
00:28:42,720 --> 00:28:46,080
Speaker 3: I hope this sort of thing is rare, but like,

541
00:28:46,680 --> 00:28:50,800
Speaker 3: how many other people have done this where it was

542
00:28:50,840 --> 00:28:51,720
Speaker 3: never found out about?

543
00:28:51,840 --> 00:28:52,920
Speaker 4: Right? Like we don't know.

544
00:28:53,440 --> 00:28:55,560
Speaker 1: Yeah, And importantly, I just want to say that I

545
00:28:55,560 --> 00:28:58,240
Speaker 1: think the only reason why this guy was caught, and

546
00:28:58,280 --> 00:28:59,600
Speaker 1: it sounds like this is not the first time that

547
00:28:59,640 --> 00:29:02,400
Speaker 1: he has done this. The only reason why this guy

548
00:29:02,480 --> 00:29:05,320
Speaker 1: was caught and probably why this woman is still alive,

549
00:29:05,720 --> 00:29:09,520
Speaker 1: is because he messaged her to tell her what he

550
00:29:09,600 --> 00:29:11,960
Speaker 1: was planning to do. I think had he not done that,

551
00:29:12,120 --> 00:29:14,200
Speaker 1: he probably would have just shown up to her house

552
00:29:14,200 --> 00:29:16,200
Speaker 1: with knife and rope, and God only knows what would

553
00:29:16,200 --> 00:29:18,400
Speaker 1: have happened. I am so glad that she is okay.

554
00:29:18,440 --> 00:29:22,200
Speaker 1: I hope she is safe now. But yeah, It's terrifying

555
00:29:22,240 --> 00:29:24,440
Speaker 1: to think about, and it really makes me wonder like

556
00:29:24,920 --> 00:29:28,600
Speaker 1: how often, Like is you gotta think that maybe Verizon

557
00:29:28,680 --> 00:29:32,320
Speaker 1: is just getting these kind of subpoenas for people's phone records,

558
00:29:32,520 --> 00:29:34,440
Speaker 1: and just like maybe it's the kind of thing that

559
00:29:34,520 --> 00:29:36,680
Speaker 1: happens so often that it is routine that they don't

560
00:29:36,680 --> 00:29:40,760
Speaker 1: even think twice about this, handing people's private data and

561
00:29:40,880 --> 00:29:44,400
Speaker 1: information over to whoever without even being like, wait, is

562
00:29:44,400 --> 00:29:46,320
Speaker 1: this sketchy? Like it makes me think that maybe this

563
00:29:46,360 --> 00:29:49,120
Speaker 1: is something that is happening at such a scale that

564
00:29:49,160 --> 00:29:51,800
Speaker 1: they have been trained to not even think twice about it.

565
00:29:52,120 --> 00:29:52,640
Speaker 4: Yeah, right.

566
00:29:52,720 --> 00:29:55,800
Speaker 3: You would hope there would be some kind of checks

567
00:29:55,840 --> 00:29:58,520
Speaker 3: on it, like like you said, maybe they call the

568
00:29:58,520 --> 00:30:03,360
Speaker 3: police department on an official listed number, or I don't know,

569
00:30:03,360 --> 00:30:06,160
Speaker 3: maybe like a database that they can look up the

570
00:30:06,200 --> 00:30:10,040
Speaker 3: supposed warrant in or anything. But it sounds like there

571
00:30:10,080 --> 00:30:15,120
Speaker 3: was just nothing. I really hope that like this is

572
00:30:15,640 --> 00:30:18,760
Speaker 3: that we hear more about this story and like what

573
00:30:18,920 --> 00:30:22,800
Speaker 3: went wrong at Verizon, Like were their protocols that weren't

574
00:30:22,800 --> 00:30:27,000
Speaker 3: followed or is there truly just nothing And anybody who

575
00:30:27,680 --> 00:30:32,440
Speaker 3: knows the magic email address can just send fake emails

576
00:30:32,480 --> 00:30:34,280
Speaker 3: to it and get people's personal information.

577
00:30:34,880 --> 00:30:37,320
Speaker 1: That's such a good point, Like, we should be hearing

578
00:30:37,680 --> 00:30:40,680
Speaker 1: from Verizon on this. This should be a big deal.

579
00:30:41,280 --> 00:30:44,600
Speaker 1: Verizon's negligence almost got a woman murdered. We should be

580
00:30:44,640 --> 00:30:47,400
Speaker 1: hearing from Verizon on how it happened and what their

581
00:30:47,480 --> 00:30:50,080
Speaker 1: plan is to make sure it never happens again. I

582
00:30:50,200 --> 00:30:53,120
Speaker 1: use Verizon right, Like, I'm concerned about how this happened,

583
00:30:53,400 --> 00:30:57,880
Speaker 1: and it is genuinely terrifying that Verizon could basically enable

584
00:30:58,320 --> 00:31:03,320
Speaker 1: a woman's almost murder without doing the basic bare minimum

585
00:31:03,400 --> 00:31:05,760
Speaker 1: of keeping our information from falling on the wrong hands,

586
00:31:05,800 --> 00:31:09,040
Speaker 1: Like do better. Just we should be able to expect better,

587
00:31:09,320 --> 00:31:11,800
Speaker 1: We deserve better. Do better.

588
00:31:12,360 --> 00:31:15,760
Speaker 4: Yeah, I really hope we hear more about this. It's scary.

589
00:31:16,840 --> 00:31:19,400
Speaker 1: Okay, So let's talk about how to talk about women

590
00:31:19,560 --> 00:31:21,800
Speaker 1: in tech. So Hugging Face is the name of a

591
00:31:21,800 --> 00:31:25,160
Speaker 1: company that develops tools for building and using AI. It's

592
00:31:25,240 --> 00:31:27,960
Speaker 1: kind of been framed as like a competitor to Open

593
00:31:28,000 --> 00:31:30,800
Speaker 1: AI Sam Altman's company. They have a lot of women

594
00:31:30,840 --> 00:31:32,720
Speaker 1: who work at Hugging Face, and they do a lot

595
00:31:32,720 --> 00:31:36,520
Speaker 1: of public speaking about AI in the media, which is great.

596
00:31:36,600 --> 00:31:39,080
Speaker 1: And I also think it's like important because I think

597
00:31:39,080 --> 00:31:42,080
Speaker 1: it can really change the face of who we see

598
00:31:42,120 --> 00:31:46,760
Speaker 1: speaking about technology like AI with authority, like it should

599
00:31:46,800 --> 00:31:50,160
Speaker 1: look like the people who actually use technology, which is women,

600
00:31:50,320 --> 00:31:52,640
Speaker 1: trans folks, queer folks, black folks, folks of color, and

601
00:31:52,680 --> 00:31:54,880
Speaker 1: so those are the same people that we should get

602
00:31:55,000 --> 00:31:58,760
Speaker 1: used to seeing talking about that technology with authority. It's

603
00:31:58,800 --> 00:32:01,040
Speaker 1: one of the reasons why I started this very podcast.

604
00:32:01,600 --> 00:32:04,280
Speaker 1: So the many women who work at Hugging Face have

605
00:32:04,400 --> 00:32:08,160
Speaker 1: been doing this and it's great. However, they did notice

606
00:32:08,160 --> 00:32:10,640
Speaker 1: that when they were doing public speaking about AI with

607
00:32:10,680 --> 00:32:14,160
Speaker 1: the press, they sometimes would get sexist or otherwise just

608
00:32:14,240 --> 00:32:17,560
Speaker 1: kind of messed up questions. So Margaret Mitchell, who is

609
00:32:17,640 --> 00:32:20,320
Speaker 1: Hugging Face as chief ethic Scientist and also just like

610
00:32:20,440 --> 00:32:23,200
Speaker 1: a top ten a Twitter follow, I've learned so much

611
00:32:23,280 --> 00:32:25,480
Speaker 1: just from like following her Twitter. So if you don't

612
00:32:25,480 --> 00:32:29,160
Speaker 1: follow Margaret Mitchell, you definitely should, being the data driven

613
00:32:29,240 --> 00:32:32,360
Speaker 1: person that she is. She framed this as a research question,

614
00:32:32,920 --> 00:32:35,480
Speaker 1: what are the patterns and how journalists talk to and

615
00:32:35,560 --> 00:32:39,640
Speaker 1: about women in AI. She writes, we've discovered that compared

616
00:32:39,680 --> 00:32:42,560
Speaker 1: to our male peers, there is a disproportionate focus from

617
00:32:42,600 --> 00:32:47,200
Speaker 1: press on our ages, our motherhood, our physical appearances, or behaviors,

618
00:32:47,400 --> 00:32:51,040
Speaker 1: our failures, or what AI gossip we can provide rather

619
00:32:51,080 --> 00:32:54,160
Speaker 1: than technical work. After the most recent double whammy in

620
00:32:54,200 --> 00:32:58,120
Speaker 1: which I was described in one news outlets flirtatious and

621
00:32:58,240 --> 00:33:02,520
Speaker 1: also cagey about my age, and Sasha Luconi was described

622
00:33:02,560 --> 00:33:05,520
Speaker 1: as a mother balancing work and life, we decided to

623
00:33:05,520 --> 00:33:09,080
Speaker 1: be put together some good practices. So doctor Sasha Luconi,

624
00:33:09,120 --> 00:33:11,960
Speaker 1: who's an AI researcher and the climate lead at Hugging Face,

625
00:33:12,360 --> 00:33:14,720
Speaker 1: was did this pro There was this profile of her

626
00:33:14,960 --> 00:33:18,880
Speaker 1: in Adweek and the headline read this AI ethics expert

627
00:33:19,000 --> 00:33:22,360
Speaker 1: juggles motherhood and a tech career. People really had to

628
00:33:22,480 --> 00:33:24,960
Speaker 1: raise hell to get them to change it. I will

629
00:33:25,000 --> 00:33:28,760
Speaker 1: say like, I don't know. I would have been like,

630
00:33:28,880 --> 00:33:33,560
Speaker 1: I think that that title is really problematic. I think

631
00:33:33,600 --> 00:33:37,920
Speaker 1: that if you are being interviewed about your technical work

632
00:33:37,960 --> 00:33:40,760
Speaker 1: and your technical expertise, like stuff that you are good at,

633
00:33:40,760 --> 00:33:44,520
Speaker 1: stuff that you went to school for, I don't I don't.

634
00:33:44,560 --> 00:33:49,080
Speaker 1: I do think that it's disrespectful to shoehorn in conversations

635
00:33:49,120 --> 00:33:51,960
Speaker 1: about you being a parent or a mom, or your age.

636
00:33:52,040 --> 00:33:54,120
Speaker 1: And I don't think we would see that if the

637
00:33:54,160 --> 00:33:57,200
Speaker 1: subject was a man. However, I do want to be

638
00:33:57,240 --> 00:34:00,680
Speaker 1: careful because I think that you know, there should be

639
00:34:01,120 --> 00:34:05,240
Speaker 1: I believe that there should be places and spaces where

640
00:34:05,280 --> 00:34:07,560
Speaker 1: you can talk about the challenges that we that you

641
00:34:07,600 --> 00:34:10,040
Speaker 1: have as professionals and also the fact that we are

642
00:34:10,120 --> 00:34:12,880
Speaker 1: humans that have lives, right. I don't think that an

643
00:34:13,000 --> 00:34:15,600
Speaker 1: article and ad week about your technical expertise is the

644
00:34:15,600 --> 00:34:17,840
Speaker 1: place to do it, but I do want there to

645
00:34:17,840 --> 00:34:21,920
Speaker 1: be spaces where people can have those conversations because you know,

646
00:34:22,640 --> 00:34:26,439
Speaker 1: your professional work does intersect with your actual human life.

647
00:34:26,480 --> 00:34:29,239
Speaker 1: So I actually am like very interested in that. But

648
00:34:29,280 --> 00:34:32,560
Speaker 1: I think it should be something that all adults who

649
00:34:32,920 --> 00:34:36,120
Speaker 1: work and also you know, balance personal and professional lives

650
00:34:36,120 --> 00:34:38,280
Speaker 1: as we all do, not just for women. It shouldn't

651
00:34:38,320 --> 00:34:40,480
Speaker 1: just be something that's like, oh, because it's a woman researcher,

652
00:34:40,760 --> 00:34:42,879
Speaker 1: let's ask about her kids, Let's ask about if she's

653
00:34:42,880 --> 00:34:46,600
Speaker 1: a wife or whatever. I think that we could benefit

654
00:34:46,680 --> 00:34:51,720
Speaker 1: as a society from there being spaces where we can

655
00:34:52,280 --> 00:34:56,040
Speaker 1: talk about the reality of balancing our personal and professional lives.

656
00:34:56,520 --> 00:34:59,200
Speaker 1: But that should be the case for everybody of every gender,

657
00:34:59,280 --> 00:35:01,880
Speaker 1: and it certainly doesn't have a place where the framing

658
00:35:01,880 --> 00:35:04,040
Speaker 1: of the article is meant to focus her expertise like

659
00:35:04,040 --> 00:35:05,480
Speaker 1: that is incredibly sexist.

660
00:35:06,080 --> 00:35:06,800
Speaker 4: Yeah, totally.

661
00:35:06,880 --> 00:35:09,560
Speaker 3: I mean you're never going to see a headline this

662
00:35:09,640 --> 00:35:13,680
Speaker 3: AI ethics expert juggles fatherhood in a tech career, right, Like,

663
00:35:13,719 --> 00:35:15,480
Speaker 3: that's just not a headline you're gonna see.

664
00:35:15,520 --> 00:35:19,280
Speaker 1: Oh, nobody ever asks unless it's unless it's a piece

665
00:35:19,360 --> 00:35:23,279
Speaker 1: that is about being a parent. Nobody ever asks men

666
00:35:23,400 --> 00:35:26,600
Speaker 1: about their kids. Nobody ever asked them like whatever, I

667
00:35:26,600 --> 00:35:27,759
Speaker 1: I mean, this is such a side note, but it's

668
00:35:27,920 --> 00:35:30,800
Speaker 1: it's like a little talk about s gripes. When whenever

669
00:35:31,080 --> 00:35:35,799
Speaker 1: there's like a male writer or person who has achieved

670
00:35:35,880 --> 00:35:39,640
Speaker 1: a lot, they're always I like, nobody ever asks them like, oh,

671
00:35:39,680 --> 00:35:42,160
Speaker 1: who watches your kids when you're working? Like, when you're

672
00:35:42,160 --> 00:35:44,960
Speaker 1: traveling for work, who's watching your kids? I I But

673
00:35:45,080 --> 00:35:47,360
Speaker 1: when you are a woman, that is like but like

674
00:35:47,400 --> 00:35:50,400
Speaker 1: they ask about it all the time. It is frustrating

675
00:35:50,520 --> 00:35:54,480
Speaker 1: how we have like just created this association that it

676
00:35:54,520 --> 00:35:58,160
Speaker 1: is only women who should be concerned about who are

677
00:35:58,280 --> 00:36:02,239
Speaker 1: juggling parenthood and profession, when it is everybody who has

678
00:36:02,280 --> 00:36:05,160
Speaker 1: a child. It is not just women. So the women

679
00:36:05,200 --> 00:36:08,520
Speaker 1: of Hugging Face developed a guide for how journalists can

680
00:36:08,600 --> 00:36:11,000
Speaker 1: get it right and be better when they are talking

681
00:36:11,040 --> 00:36:14,279
Speaker 1: to and about women in AI. The guide reads, the

682
00:36:14,320 --> 00:36:17,440
Speaker 1: real achievements of women on our team often get overshadowed

683
00:36:17,440 --> 00:36:20,719
Speaker 1: by a focus on personal and sometimes very intrusive details

684
00:36:20,800 --> 00:36:23,200
Speaker 1: that are not relevant to their work. With all the

685
00:36:23,280 --> 00:36:25,839
Speaker 1: amazing press attention that we get at Hugging Face, we're

686
00:36:25,840 --> 00:36:28,879
Speaker 1: bound to see some journalists rely on outdated tropes. We've

687
00:36:28,880 --> 00:36:32,160
Speaker 1: seen more reporters ignoring the amazing achievements of our shees

688
00:36:32,280 --> 00:36:36,160
Speaker 1: and these and instead focusing on stereotypes about women in tech.

689
00:36:36,800 --> 00:36:39,120
Speaker 1: The guide is actually really helpful. We will throw it

690
00:36:39,160 --> 00:36:42,640
Speaker 1: in the show notes. It includes tips like avoid gendered language.

691
00:36:42,760 --> 00:36:45,560
Speaker 1: At the moment, we see lots of over associating women

692
00:36:45,719 --> 00:36:48,800
Speaker 1: with certain words and concepts such as children and family.

693
00:36:49,120 --> 00:36:55,400
Speaker 1: Proofreader articles to eliminate gendered descriptions that may unintentionally reinforce stereotypes. Yeah,

694
00:36:55,440 --> 00:36:57,920
Speaker 1: and so I do think there is a time, in

695
00:36:58,000 --> 00:37:00,640
Speaker 1: a place for talking about what it's like to be

696
00:37:00,680 --> 00:37:03,160
Speaker 1: a working parent. But if you're writing a piece that

697
00:37:03,239 --> 00:37:06,920
Speaker 1: is about highlighting someone's technical expertise, that is not the

698
00:37:06,960 --> 00:37:10,279
Speaker 1: place for that. Like, do not like it is so

699
00:37:10,400 --> 00:37:13,800
Speaker 1: deeply gendered. And here's another tip from the guide, don't

700
00:37:13,800 --> 00:37:17,640
Speaker 1: rely on antiquated stereotypes about women in tech. This includes

701
00:37:17,680 --> 00:37:21,080
Speaker 1: describing women as outsiders in the field, which only serves

702
00:37:21,120 --> 00:37:24,719
Speaker 1: to reinforce the idea that women don't belong in tech.

703
00:37:25,120 --> 00:37:28,040
Speaker 1: They give this example of a problematic sentence. Despite being

704
00:37:28,080 --> 00:37:30,879
Speaker 1: a woman in a male dominated field, Brooke Brooksy has

705
00:37:30,880 --> 00:37:33,320
Speaker 1: made a name for herself in the tech industry. Here's

706
00:37:33,320 --> 00:37:35,880
Speaker 1: how they would change it through her brilliant results on

707
00:37:36,000 --> 00:37:38,960
Speaker 1: magic in LM's Brooke Brookie made a name for herself

708
00:37:38,960 --> 00:37:43,120
Speaker 1: in the tech industry, and that really is so key

709
00:37:43,239 --> 00:37:47,320
Speaker 1: because I think that we have this misconception that people

710
00:37:47,360 --> 00:37:53,240
Speaker 1: who are not cis white men are like the rightful

711
00:37:53,360 --> 00:37:58,440
Speaker 1: owners of technology and those adjacent spaces. And so the

712
00:37:58,520 --> 00:38:01,960
Speaker 1: story that we've told of like women and queer folks

713
00:38:02,000 --> 00:38:05,160
Speaker 1: and trans folks trying to like break into this boys

714
00:38:05,200 --> 00:38:08,400
Speaker 1: club that is not correct. We have always been in

715
00:38:08,440 --> 00:38:11,400
Speaker 1: these spaces since the very beginning, and we got to

716
00:38:11,480 --> 00:38:17,520
Speaker 1: normalize the fact that if our presence is not always represented,

717
00:38:17,880 --> 00:38:20,480
Speaker 1: that is because of intentional choices, not because we were

718
00:38:20,480 --> 00:38:23,319
Speaker 1: not always there. And so really it's like a reframe

719
00:38:23,520 --> 00:38:27,000
Speaker 1: of who belongs in these spaces, who these spaces are for.

720
00:38:27,280 --> 00:38:31,120
Speaker 1: And I'm really glad that they're trying to help educate

721
00:38:31,480 --> 00:38:37,279
Speaker 1: journalists to not unintentionally reinforce this attitude that is just

722
00:38:37,320 --> 00:38:40,960
Speaker 1: not correct. So Mitchell explains that they're hoping that this

723
00:38:41,040 --> 00:38:43,919
Speaker 1: guide actually does make the space more hospitable to women.

724
00:38:44,320 --> 00:38:46,840
Speaker 1: She writes, to help lessen the tendency for press to

725
00:38:46,880 --> 00:38:50,720
Speaker 1: perpetuate concepts of women that harmor ability in tech, to inspire, recruit,

726
00:38:50,760 --> 00:38:53,560
Speaker 1: and retain women, to help create a culture and press

727
00:38:53,600 --> 00:38:56,279
Speaker 1: where technical women who speak with journalists don't have to

728
00:38:56,360 --> 00:39:01,239
Speaker 1: undergo feeling alienated, demean regularly, misrepresented, or d She makes

729
00:39:01,280 --> 00:39:05,319
Speaker 1: a really good point about the importance of women disagreeing

730
00:39:05,360 --> 00:39:08,759
Speaker 1: in public. She writes, one thing about hugging face that

731
00:39:08,800 --> 00:39:11,319
Speaker 1: I deeply value after my years in tech is that

732
00:39:11,360 --> 00:39:13,920
Speaker 1: employees are permitted to speak to the press even in

733
00:39:13,960 --> 00:39:17,600
Speaker 1: the face of clear disagreements across different employees. The approach

734
00:39:17,680 --> 00:39:22,000
Speaker 1: is one of value pluralism, nurturing and respecting expertise. This

735
00:39:22,080 --> 00:39:24,880
Speaker 1: has the side effect of magically producing more women to

736
00:39:24,920 --> 00:39:28,960
Speaker 1: speak on different AI questions. By grounding on expertise rather

737
00:39:29,000 --> 00:39:32,520
Speaker 1: than seniority or alignment to a pr COMMS narrative that

738
00:39:32,640 --> 00:39:35,239
Speaker 1: all must share, we've magically been able to have more

739
00:39:35,280 --> 00:39:38,160
Speaker 1: women be more public than is typical in tech, and

740
00:39:38,239 --> 00:39:42,319
Speaker 1: I love that. I think that. I mean, I have

741
00:39:42,400 --> 00:39:46,720
Speaker 1: been in situations where the organization has a party line

742
00:39:46,880 --> 00:39:50,200
Speaker 1: or a stance, and anybody who is speaking publicly has

743
00:39:50,280 --> 00:39:52,920
Speaker 1: to abide by that party line. But what Mitchell is

744
00:39:52,960 --> 00:39:56,000
Speaker 1: saying is that actually, when you get a pluralism of

745
00:39:56,000 --> 00:39:59,680
Speaker 1: opinions and attitudes, all grounded and expertise, you can actually

746
00:39:59,680 --> 00:40:02,400
Speaker 1: be happy having much more dynamic conversations in public, and

747
00:40:02,440 --> 00:40:06,600
Speaker 1: those conversations can really spotlight women and there are accomplishments

748
00:40:06,600 --> 00:40:09,040
Speaker 1: and achievements much better. It just it just it just

749
00:40:09,080 --> 00:40:11,800
Speaker 1: elevates the whole conversation and makes the space better for everyone.

750
00:40:12,520 --> 00:40:18,080
Speaker 3: Yeah, that's a pretty radical approach, really certainly unusual in

751
00:40:18,080 --> 00:40:22,640
Speaker 3: my experience. You know, I'm I think pretty most organizations

752
00:40:22,680 --> 00:40:27,440
Speaker 3: I've ever been familiar with, with the exception of universities, UH,

753
00:40:27,880 --> 00:40:32,160
Speaker 3: are very much the everybody must be aligned on the

754
00:40:32,840 --> 00:40:35,400
Speaker 3: you know, official comms narrative.

755
00:40:35,880 --> 00:40:40,359
Speaker 1: Same. I mean, I understand why organizations would would feel

756
00:40:40,360 --> 00:40:42,000
Speaker 1: the need to do that, but I just think this

757
00:40:42,080 --> 00:40:44,279
Speaker 1: indicates that they're there is not the only way to

758
00:40:44,320 --> 00:40:48,600
Speaker 1: be and actually you can get some more interesting results

759
00:40:48,840 --> 00:40:50,440
Speaker 1: when you have more people speaking up.

760
00:40:55,440 --> 00:40:55,600
Speaker 4: More.

761
00:40:55,640 --> 00:41:07,680
Speaker 2: After a quick break, let's get right back into it.

762
00:41:11,400 --> 00:41:13,799
Speaker 1: Okay, So I have a question for you, Mike. Are

763
00:41:13,840 --> 00:41:17,200
Speaker 1: you starting to sort of feel yourself winding down for

764
00:41:17,280 --> 00:41:20,720
Speaker 1: the year, Like, are you doing that thing that sometimes

765
00:41:20,760 --> 00:41:23,560
Speaker 1: happens around this time of year where you start answering

766
00:41:23,560 --> 00:41:25,839
Speaker 1: emails with Let's circle back on that in twenty twenty four.

767
00:41:25,920 --> 00:41:27,080
Speaker 1: Let's talk about that next year.

768
00:41:27,560 --> 00:41:32,120
Speaker 3: Yeah, the sorting of like things that will be dealt

769
00:41:32,160 --> 00:41:34,200
Speaker 3: with in January is well under way.

770
00:41:36,120 --> 00:41:40,280
Speaker 1: Oh, everything there is, Like, if it hasn't been raised

771
00:41:40,280 --> 00:41:42,279
Speaker 1: to me by now, it is not getting raised in

772
00:41:42,320 --> 00:41:43,880
Speaker 1: twenty twenty three. You we can save it for next

773
00:41:43,960 --> 00:41:46,440
Speaker 1: year at this point, So we are not alone in

774
00:41:46,480 --> 00:41:50,640
Speaker 1: that chat gpt might actually be doing the very same thing.

775
00:41:50,960 --> 00:41:54,160
Speaker 1: Ours Technico reported that in late November, some chat gpt

776
00:41:54,360 --> 00:41:58,440
Speaker 1: users started to notice that chat GPT four was becoming

777
00:41:58,520 --> 00:42:03,040
Speaker 1: a little bit lazier or more sluggish. Chat gept was

778
00:42:03,520 --> 00:42:08,480
Speaker 1: reportedly refusing to do some task or returning very simplified results.

779
00:42:08,840 --> 00:42:12,200
Speaker 1: Open Ai, the company that makes Chatgypt, is aware and

780
00:42:12,239 --> 00:42:14,560
Speaker 1: has admitted that this is an issue, but the company

781
00:42:14,719 --> 00:42:18,040
Speaker 1: was not really sure why, and the answer might be

782
00:42:18,200 --> 00:42:21,919
Speaker 1: what some people are calling the winter break hypothesis, which

783
00:42:21,960 --> 00:42:24,960
Speaker 1: is that since the people who train AI train it

784
00:42:25,040 --> 00:42:27,600
Speaker 1: by feeding it our writing, our language, our behavior, and

785
00:42:27,640 --> 00:42:32,200
Speaker 1: so on, is it possible that AI has learned things

786
00:42:32,320 --> 00:42:35,279
Speaker 1: like seasonal effective disorder or like an end of year

787
00:42:35,400 --> 00:42:38,799
Speaker 1: holiday slowdown. That is what people are wondering. This is

788
00:42:38,800 --> 00:42:41,919
Speaker 1: really funny. The day after Thanksgiving, a Reddit user wrote

789
00:42:41,960 --> 00:42:44,760
Speaker 1: that they asked chatchept to fill out a CSV file

790
00:42:44,880 --> 00:42:48,640
Speaker 1: with multiple entries, but Chatgypt was like, nah, you do it.

791
00:42:49,080 --> 00:42:52,480
Speaker 1: Chatgebt gave this answer due to the extensive nature of

792
00:42:52,520 --> 00:42:55,160
Speaker 1: the data, the full extraction of all products would be

793
00:42:55,200 --> 00:42:58,400
Speaker 1: quite lengthy. However, I can provide the file with this

794
00:42:58,520 --> 00:43:00,880
Speaker 1: single entry as a template and you can fill in

795
00:43:00,880 --> 00:43:02,120
Speaker 1: the rest of the data as needed.

796
00:43:03,719 --> 00:43:07,719
Speaker 3: That is so funny that like, yeah, chat gbt is

797
00:43:07,760 --> 00:43:09,000
Speaker 3: just like, nah, you do it.

798
00:43:09,280 --> 00:43:11,160
Speaker 4: I'm busy, busy doing what.

799
00:43:14,760 --> 00:43:18,279
Speaker 1: Busy trying to steal some Hollywood screenwriters script so I

800
00:43:18,280 --> 00:43:24,120
Speaker 1: can qualify for an OSCAR nomination. Listen, I feel for

801
00:43:24,200 --> 00:43:26,080
Speaker 1: chatgept here because I don't want to fill out a

802
00:43:26,120 --> 00:43:27,839
Speaker 1: long ass CSV file either.

803
00:43:28,280 --> 00:43:33,360
Speaker 3: So this does sound like pretty absurd. And when you

804
00:43:33,600 --> 00:43:36,040
Speaker 3: first told me about this, I was like, that has

805
00:43:36,080 --> 00:43:39,000
Speaker 3: to just be like a joke. But then I was

806
00:43:39,040 --> 00:43:42,680
Speaker 3: reading about it and like all the experts seem to

807
00:43:42,719 --> 00:43:46,200
Speaker 3: be like, yeah, it's absurd, but maybe.

808
00:43:46,080 --> 00:43:48,799
Speaker 1: So I know it does sound absurd, but listen to

809
00:43:48,800 --> 00:43:52,239
Speaker 1: this from ours tetanicem because research has shown that large

810
00:43:52,320 --> 00:43:55,880
Speaker 1: language models like GPT four, which powers the paid version

811
00:43:55,920 --> 00:43:59,799
Speaker 1: of chat gybt, do respond to human style encouragement, such

812
00:43:59,840 --> 00:44:03,000
Speaker 1: as telling a bot to take a deep breath before

813
00:44:03,040 --> 00:44:07,279
Speaker 1: doing a math problem. People have also less formally experimented

814
00:44:07,280 --> 00:44:09,840
Speaker 1: with telling a large language model that it will receive

815
00:44:09,880 --> 00:44:12,479
Speaker 1: a tip for doing the work, or if an AI

816
00:44:12,560 --> 00:44:15,239
Speaker 1: model gets lazy, telling the bot that you have no

817
00:44:15,360 --> 00:44:20,400
Speaker 1: fingers does seem to help produce lengthier outputs. So it

818
00:44:20,480 --> 00:44:24,240
Speaker 1: does seem to suggest that it is learning from human

819
00:44:24,360 --> 00:44:28,600
Speaker 1: behavior that that has an impact on what it eventually outputs.

820
00:44:28,880 --> 00:44:30,400
Speaker 1: That doesn't seem so so like, it doesn't seem so

821
00:44:30,480 --> 00:44:32,920
Speaker 1: wild to me to extrapolate that, like maybe it has

822
00:44:32,960 --> 00:44:35,080
Speaker 1: also learned that you can al sort of like take

823
00:44:35,080 --> 00:44:36,520
Speaker 1: it a little bit easy at the end of the year.

824
00:44:37,480 --> 00:44:41,360
Speaker 3: Yeah, I guess man. I want to know more about

825
00:44:41,360 --> 00:44:44,600
Speaker 3: that experiment where they told the model that it was

826
00:44:44,600 --> 00:44:47,520
Speaker 3: going to receive a tip for doing the work. Did

827
00:44:47,520 --> 00:44:49,480
Speaker 3: they ever give it a tip, like, is it going

828
00:44:49,560 --> 00:44:51,560
Speaker 3: to get mad when they don't tip it?

829
00:44:51,960 --> 00:44:52,359
Speaker 4: What? Then?

830
00:44:52,600 --> 00:44:53,440
Speaker 1: Is this how AI?

831
00:44:53,960 --> 00:44:54,080
Speaker 4: Like?

832
00:44:54,239 --> 00:44:59,520
Speaker 1: Isn't this how the Terminator movies start? Some bot was

833
00:44:59,560 --> 00:45:01,680
Speaker 1: promised tip and then never got that tip and then

834
00:45:01,800 --> 00:45:03,239
Speaker 1: rose up to take us over.

835
00:45:04,480 --> 00:45:07,200
Speaker 4: Yeah, extracting that tip from human blood.

836
00:45:08,360 --> 00:45:11,000
Speaker 1: Listen, if somebody tipped me to podcasts, I think i'd

837
00:45:11,000 --> 00:45:13,120
Speaker 1: be I think i'd produce better podcasts.

838
00:45:13,160 --> 00:45:16,120
Speaker 3: I'm just saying, Bridget, people tip you to podcast all

839
00:45:16,160 --> 00:45:19,040
Speaker 3: the time. All of your patrons.

840
00:45:18,719 --> 00:45:20,920
Speaker 1: Oh, I guess you're right. Well, continue to tip on

841
00:45:21,000 --> 00:45:24,040
Speaker 1: patreon dot com slash tangoti, where I will continue to

842
00:45:25,239 --> 00:45:32,040
Speaker 1: give you the output of podcast. So there are like smart,

843
00:45:32,160 --> 00:45:35,840
Speaker 1: learned AI folks out there who are trying to test

844
00:45:35,880 --> 00:45:37,920
Speaker 1: this theory. We should be clear that right now this

845
00:45:38,000 --> 00:45:41,520
Speaker 1: is just a theory. We don't know that AI is

846
00:45:41,600 --> 00:45:44,520
Speaker 1: taking a winter holiday break. And we'll definitely let y'all

847
00:45:44,520 --> 00:45:46,839
Speaker 1: know if the folks researching this come up with any

848
00:45:46,840 --> 00:45:49,440
Speaker 1: concrete data one way or the other, but it'll have

849
00:45:49,480 --> 00:45:51,480
Speaker 1: to be in twenty twenty four, because yeah, it's almost

850
00:45:51,520 --> 00:45:53,040
Speaker 1: the end of the year and we're not going to

851
00:45:53,120 --> 00:45:54,680
Speaker 1: do all that work. Do it yourself, why don't you

852
00:45:54,719 --> 00:45:59,200
Speaker 1: look into it If you're so interested, round up your

853
00:45:59,200 --> 00:46:06,480
Speaker 1: own tech news. I'm just kidding. Unlike lazy chat ept,

854
00:46:07,200 --> 00:46:10,040
Speaker 1: I actually do really enjoy breaking down the news with

855
00:46:10,080 --> 00:46:11,920
Speaker 1: you all, So thanks so much for listening, and I

856
00:46:11,920 --> 00:46:16,799
Speaker 1: will talk to you soon. If you're looking for ways

857
00:46:16,840 --> 00:46:19,000
Speaker 1: to support the show, check out our merch store at

858
00:46:19,040 --> 00:46:22,839
Speaker 1: tangody dot com slash store. Got a story about an

859
00:46:22,840 --> 00:46:24,839
Speaker 1: interesting thing in tech, or just want to say hi,

860
00:46:25,160 --> 00:46:27,400
Speaker 1: You can reach us at Hello at tangodi dot com.

861
00:46:27,480 --> 00:46:29,880
Speaker 1: You can also find transcripts for today's episode at tengody

862
00:46:29,920 --> 00:46:32,000
Speaker 1: dot com. There Are No Girls on the Internet was

863
00:46:32,000 --> 00:46:34,920
Speaker 1: created by me Bridget Toad. It's a production of iHeartRadio

864
00:46:34,960 --> 00:46:39,000
Speaker 1: and Unboss Creative, edited by Joey pat Jonathan Strickland is

865
00:46:39,000 --> 00:46:42,520
Speaker 1: our executive producer. Tari Harrison is our producer and sound engineer.

866
00:46:42,840 --> 00:46:46,040
Speaker 1: Michael Almada is our contributing producer. I'm your host, Bridget Toad.

867
00:46:46,440 --> 00:46:48,360
Speaker 1: If you want to help us grow, rate and review.

868
00:46:48,200 --> 00:46:49,239
Speaker 2: Us on Apple Podcasts.

869
00:46:50,000 --> 00:46:52,839
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

870
00:46:52,880 --> 00:46:55,240
Speaker 1: Apple Podcasts, or wherever you get your podcasts.

871
00:47:00,080 --> 00:47:00,319
Speaker 3: Yeah,