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Speaker 1: Hey, this is Annie and Samitha and know coome stuff.

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Speaker 2: I've never told your production of ByHeart Radio, and today

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Speaker 2: we are so so happy to once again be joined

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Speaker 2: by the brilliant, the fabulous, the talented, so wonderful bridget

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Speaker 2: Todd here with us for the last time in twenty

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Speaker 2: twenty three.

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Speaker 3: Thank you for having me. Happy holidays, y'all.

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Speaker 4: Happy holidays to you.

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Speaker 1: Yes, I'm so glad you had the time to make it.

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Speaker 2: I know it's a really busy, busy time of year

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Speaker 2: for everybody, so thank you.

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Speaker 5: Yeah, my pleasure. I am such a grane like this

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Speaker 5: time of year. I find it stressful, I find it overwhelming.

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Speaker 5: I feel like it takes more work than usual to

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Speaker 5: show up as my best self because I'm just like

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Speaker 5: my I'm like Baseline, stressed and overwhelmed and grouchy. So

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Speaker 5: this will be a fun you know, departure from all

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Speaker 5: of those holiday plans blah blah blah, stop that and

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Speaker 5: sometimes get me down.

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Speaker 1: Yeah yeah, well I hope.

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Speaker 2: So this is one of our last recordings of the year,

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Speaker 2: so it's got that again, that kind of like last

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Speaker 2: day of school vibe.

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Speaker 1: Are you doing anything fun? Are you doing like weirdo

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Speaker 1: Christmas or anything.

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Speaker 3: Like that good memory.

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Speaker 5: So for folks who don't know, my partner and I

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Speaker 5: have started a holiday tradition called Weirdo Christmas. If folks

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Speaker 5: are interested in partaking, you can have your own weirdo Christmas.

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Speaker 5: And Weirdo Christmas is before you do Christmas with your family.

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Speaker 5: Whatever you're like, Christmas obligation looks like is whatever to you?

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Speaker 3: You just go have your own little Christmas trip.

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Speaker 5: We usually get a cabin with a hot tub, but

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Speaker 5: it can be anything where the whole point is just

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Speaker 5: to have fun, experience holiday merriment without the obligations that

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Speaker 5: sometimes come with like holiday family plans. Our particular way

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Speaker 5: of celebrating it is weird costumes, weird lights, and we

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Speaker 5: have to watch at least one, but usually several more

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Speaker 5: bad holiday movies.

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Speaker 1: Ooo.

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Speaker 3: I'd like you talk over them and make fun of them.

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Speaker 6: Sure, did we talk about what you consider bad previously?

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Speaker 3: Ooh, I don't think so.

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Speaker 5: So for us it's a lot of over the top

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Speaker 5: Hallmark holiday movies. Usually, you know, a girl who has

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Speaker 5: a good job in the big city, comes home. Her

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Speaker 5: family runs like a bakery, but it's in trouble and

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Speaker 5: then she has to meet a hometown holiday hunk who

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Speaker 5: shows her the tree meeting of Christmas.

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Speaker 6: Yes, who's been like a high school friend or something

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Speaker 6: that they reconnect after years and oh my goodness, he

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Speaker 6: runs a coffee shop and he's richer than he thought

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Speaker 6: or something.

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Speaker 3: Yeah, or it's like flipped.

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Speaker 5: So like if you're listening and you are a woman

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Speaker 5: who has dark hair, brown hair, and you have and

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Speaker 5: you care about your career, you have a like full,

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Speaker 5: well rounded life, like a good career, good friends, and

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Speaker 5: you are going with your partner to a small town

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Speaker 5: to visit his family.

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Speaker 3: You're about to get left.

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Speaker 5: For like a wholesome girl next door type who can

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Speaker 5: really understand it's the true meeting of Christmas.

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Speaker 3: You just know that that's always the thought of these movies.

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Speaker 6: Well, that's kind of a what is that holiday movie

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Speaker 6: with Sarahjiska.

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Speaker 3: Parker on the Family Stone?

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Speaker 6: Yes, like kinda is that whole trap together in one?

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Speaker 6: Like you're like, this is the worst holiday movie ever?

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Speaker 6: Who thought this was a good idea because they took

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Speaker 6: all the trips of like Hallmark but made it tragic.

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Speaker 3: Yes, also the Family Stone that's spoiler alert. Oh it's

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Speaker 3: been out for like twenty years.

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Speaker 5: It has one of my One of the plotlines I've

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Speaker 5: find so interesting is when partner swap like focus, that's

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Speaker 5: a part of it.

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Speaker 3: I always find that to be so weird.

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Speaker 6: It was the Luke Wilson character and the Claire Danes

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Speaker 6: coming in and you're like, this person is abhorrent, but

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Speaker 6: this one is easy and nice and easy, like good,

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Speaker 6: good to go, like just they balance each other out

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Speaker 6: and you're like, wow, this is what what.

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Speaker 4: Is this lesson we're supposed to learn here?

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Speaker 3: Annie?

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Speaker 4: I'm sure you have never seen it.

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Speaker 3: It's actually a pretty good movie. I'm not gonna lae,

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Speaker 3: I don't.

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Speaker 6: I love people really love it as In fact, the

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Speaker 6: reason I thought about this is because it was on

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Speaker 6: TikTok and they were trying to redeem it and saying like,

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Speaker 6: it's supposed to be more realistic. Everybody's supposed to be

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Speaker 6: unlikable to a certain extent because they all have issues,

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Speaker 6: and that's supposed to be the point. And it comes

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Speaker 6: together like it's just family because you also have Rachel the.

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Speaker 3: Gap Rachel McAdams. I think it's her best role.

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Speaker 6: Yeah, so you have her in there, you have uh

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Speaker 6: like Diane Keaton. You just have a whole list of big,

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Speaker 6: big name actors in there, and they're just all somewhat dislikable.

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Speaker 6: You have the token gay couple in there, and of

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Speaker 6: course one of them is black, so you know, yay,

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Speaker 6: everybody's diversity.

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Speaker 3: And I think, is it one of them death as well?

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Speaker 4: Yes, I think so. I think they just put that

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Speaker 4: in one couple.

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Speaker 3: A win for diversity. This movie was.

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Speaker 6: The checklist was real, like, yes, you put all of

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Speaker 6: those great come on in but yeah, like it's such

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Speaker 6: an interesting movie. But I always thought of it as

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Speaker 6: like this is what happens if a Hallmark movie were

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Speaker 6: to come together in a motion like a large motion picture,

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Speaker 6: and you just really never had too many redemptive arcs

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Speaker 6: except for enough to be like we're a family, we're

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Speaker 6: gonna pull it together type of conversation.

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Speaker 4: That's how I thought of that movie.

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Speaker 5: And what's more important to the stream meaning of Christmas

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Speaker 5: than family persevering.

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Speaker 4: In the end, right with someone always about to die.

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Speaker 7: Yes, we're different Christmas the same obviously Hallmark.

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Speaker 6: Because it's been a minute. If I like, I've not

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Speaker 6: seen many Hallmark movies, I'm trying to think because like

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Speaker 6: they are along the same lines of Lifetime, right, Lifetime

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Speaker 6: has been yeah but over yeah.

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Speaker 5: So I'm using Hallmark as like a catch all for

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Speaker 5: like a like like a d like a type of movie.

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Speaker 3: But if you really want to watch a movie, like.

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Speaker 5: A Christmas movie that is like I almost feel like.

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Speaker 3: Hallmarks a little more highbrow.

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Speaker 5: If you really want to watch a really Christmas movie

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Speaker 5: that's like not it's like knows what it is, check

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Speaker 5: out a mar Vista Entertainment movie because they basically only

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Speaker 5: make bad Christmas movies like Hallmark in in theme, but

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Speaker 5: like way.

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Speaker 3: Trashier and way worse.

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Speaker 5: Recently, where the the like logo of the movie, like

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Speaker 5: the image for the movie had chevy Chase is prominently featured,

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Speaker 5: so we're like, oh, I guess chevy.

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Speaker 3: Chase is this movie. When I tell you that chevy Chase.

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Speaker 5: Is the movie for like thirty seconds, like he's also

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Speaker 5: clearly not filmed with everybody else, so it's like, oh, y'all,

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Speaker 5: have y'all have like thirty minutes with chevy Chase and

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Speaker 5: you were like throwing on the box he's in.

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Speaker 6: That's amazing because I think it's like he got pretty

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Speaker 6: much blacklisted because he was so difficult, notoriously difficult, so

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Speaker 6: that would make sense. He'd be like, yeah, pay me

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Speaker 6: a lot of money. I'll be on it for the

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Speaker 6: thirty seconds. This is what I'll do. Now, that's amazing.

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Speaker 4: Wow, So I watched all those movies.

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Speaker 3: I've watched a ton of them.

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Speaker 5: I have watched, Yes, and another another genre that I

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Speaker 5: really enjoy is Annie, you'll appreciate this horror Christmas movies.

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Speaker 3: I feel like there's a lot.

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Speaker 5: Of really good holiday, cheesy horror movies. It's a really

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Speaker 5: pretty good intersection that I.

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Speaker 2: Like, Oh yeah, oh yeah, I think that's a big

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Speaker 2: trend right now.

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Speaker 4: It is, and I'm.

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Speaker 1: Super into it. I love it.

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Speaker 3: This.

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Speaker 2: Yeah, this has been an interesting because I had like

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Speaker 2: my go to movies, but this year has definitely been

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Speaker 2: more of a horror Christmas year than.

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Speaker 6: Have you watched The Violent Night or is it the

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Speaker 6: Is it like just a violent action movie?

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Speaker 1: It's kind of a yeah.

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Speaker 2: I would say it's more of a action, like a

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Speaker 2: violent action action.

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Speaker 6: We watched it with my partner's parents and his mom

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Speaker 6: is like the sweetest says a little note card prayer

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Speaker 6: for dinner, and she was just like, what is happening.

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Speaker 6: I'm like, you could not take the worst movie.

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Speaker 1: If he tried.

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Speaker 3: How did this become the movie that you all were watching?

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Speaker 6: I don't know he was he was just bound. He's

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Speaker 6: like set on it, like this is I don't think

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Speaker 6: it is just be funny.

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Speaker 4: It was not.

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Speaker 1: I mean that it was old movie.

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Speaker 3: I know.

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Speaker 2: I try very hard not to watch a movie like that.

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Speaker 2: The first time.

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Speaker 4: I think he's been banned from picking movies. Yeah, I

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Speaker 4: rerect it.

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Speaker 1: I got it.

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Speaker 4: It's a old Joys.

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Speaker 1: It definitely good.

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Speaker 7: That.

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Speaker 4: Do you have like New Year's movies that you watch?

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Speaker 5: Ooh, well, I have one that I have a saft

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Speaker 5: spot for. It is not a movie that is that

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Speaker 5: is was well received at the time. But have y'all

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Speaker 5: ever seen two hundred cigarettes?

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Speaker 1: Oh no.

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Speaker 5: So it's a movie that, like, it's one of those

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Speaker 5: ensemble cast movies. Basically, it's like, I think it takes

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Speaker 5: place in nineteen eighty nine.

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Speaker 3: It's New Year's Eve of nineteen eighty nine.

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Speaker 5: It came out in nineteen ninety nine, and it's all

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Speaker 5: these different characters all trying to make plans for New

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Speaker 5: Year's Eve.

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Speaker 3: And it's one of those movies that came.

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Speaker 5: Out just at the perfect right time where the cast

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Speaker 5: is got the most interesting ensemble. Cast is like Kate Hudson,

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Speaker 5: Paul Rudd, Christina Rici, Courtney Love, Ben Affleck very interesting. Asked,

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Speaker 5: it's not a movie that like, I'm not gonna say

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Speaker 5: it's a good movie because it's not. But it's a

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Speaker 5: movie that you if you watch it, you will not

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Speaker 5: not enjoy it. I'll put it that way.

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Speaker 3: Janine Garofalo, I think.

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Speaker 4: I have seen that.

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Speaker 6: I think I have and it's been so long ago

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Speaker 6: and I've probably watched it because of Janine Garofalo lo

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Speaker 6: her during that age.

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Speaker 5: I feel like the nineties, the late nineties, there was

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Speaker 5: such a Janine Garoffo renaissance happening, like she was in everything,

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Speaker 5: She's great and everything.

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Speaker 3: We love her.

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Speaker 6: Yes, I was, because she was the normal girl and

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Speaker 6: it made me feel seen. I was like and also

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Speaker 6: I had like she has Asian, is she part Asia?

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Speaker 6: She looks, she looks.

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Speaker 4: She looks not white, she's got her hair.

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Speaker 6: There's something that's just like maybe I had the small hopes.

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Speaker 5: Can I tell you a this has nothing to do

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Speaker 5: with anything, but can I tell you a random fun

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Speaker 5: fact about Gine Garoffalo please yes.

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Speaker 3: So I don't know.

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Speaker 5: Why, but for some reason I was like, I wonder

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Speaker 5: if garofflo I was married. We never hear about her

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Speaker 5: having a partner. What's the deal? So I looked it

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Speaker 5: up and what I found was that Jenny Garoffalo is married.

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Speaker 5: And when she got engaged, she went to get married,

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Speaker 5: and surprise, surprise, she found out that she was in

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Speaker 5: fact already married because years earlier she had participated in

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Speaker 5: some kind of a skit or a prank where she

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Speaker 5: got married and did not realize that that was like

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Speaker 5: a real marriage.

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Speaker 3: So when she get married, look this up. It sounds

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Speaker 3: like I'm making where I am not.

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Speaker 5: When she went to get married, she was like, oh, snap,

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Speaker 5: turns out I'm already married. So she had to get

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Speaker 5: that marriage dealt with before she could get married for real.

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Speaker 4: Oh that is amazing. I did not know that. And

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Speaker 4: thank you for this information.

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Speaker 5: Just a random piece of information about Jenny Garoffalo.

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Speaker 2: Well, it sounds like she, as with many of us,

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Speaker 2: getting to the end of the new year, wanted to

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Speaker 2: leave something behind masterful as always, Yes, thank you. I

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Speaker 2: was worried it wasn't gonna happen. But I made it

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Speaker 2: happen anyway.

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Speaker 4: He did.

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Speaker 2: For the topic today that you brought us, you're talking

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Speaker 2: about some of the things tech in the tech world

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Speaker 2: that we should leave behind, are wanting exactly?

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Speaker 5: Yeah, if you all know that cartoon that you might

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Speaker 5: see on Twitter a lot, where it's like a woman

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Speaker 5: stepping from one year to the next, and behind her

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Speaker 5: she's leaving behind all of this baggage from the previous year,

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Speaker 5: like fake friends or heartbreak, and then across over her

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Speaker 5: shoulder she's carrying what she's carrying into the next year,

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Speaker 5: So like love or focus. Do you know the image

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Speaker 5: that I'm talking about?

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Speaker 1: I don't. I had never seen it, but I'm looking

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Speaker 1: at it now.

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Speaker 5: Yes, So buzzfact about that image. It was created by

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Speaker 5: a British Ghanaian graphic artist name Penniel and Chill and

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Speaker 5: it first appeared on the internet in twenty fourteen and

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Speaker 5: BuzzFeed has a great interview with her. But basically, this

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Speaker 5: image has been totally memified and it kind of pops

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Speaker 5: up around this time of year every year to sort

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Speaker 5: of fit the theme of like New Year, New me.

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Speaker 5: Sometimes it's a joke, which this artist says that she

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Speaker 5: doesn't always agree with or like the way that people

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Speaker 5: like do their spins on it. But it's really become

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Speaker 5: a part of like the shorthand of this time of

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Speaker 5: year of like what we're leaving behind and what we're

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Speaker 5: taking with us into the new year. So when that

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Speaker 5: picture was making its rounds recently around this time of year,

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Speaker 5: you know, I had just wrapped up podcasting for my

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Speaker 5: own podcast. There are no girls on the internet talking

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Speaker 5: about technology and identity and really asking questions about like

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Speaker 5: what we get right and what we get wrong, you know,

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Speaker 5: in those in those arenas. So this image got me

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Speaker 5: thinking what tech things should just stay in twenty twenty

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Speaker 5: three and that we should not be bringing with us

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Speaker 5: into twenty twenty four. So I've got my list of

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Speaker 5: things that should stay. We're like, we're done with them,

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Speaker 5: We're not we should not be bringing them with us

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Speaker 5: into the new year.

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Speaker 2: Yeah, and I was thinking about this too. This has

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Speaker 2: been a big year for tech. This has felt like

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Speaker 2: a very monumental, like a lot of things are changing,

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Speaker 2: and still looking at this list, I was like, oh, yeah, yeah, yeah,

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Speaker 2: it can feel strange because time feels so strange now.

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Speaker 2: But that did all happen this year. Yeah, so what

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Speaker 2: is on your list?

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Speaker 1: Let's get into it.

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Speaker 5: So, as you said, like it's been a big year

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Speaker 5: for conversations around technology, that they're big right now, and

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Speaker 5: I would say there probably is not a bigger conversation

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Speaker 5: than the one happening around AI. Right So we're all

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Speaker 5: like rightly talking about how AI could change the way

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Speaker 5: that we do our jobs or the way that artistic

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Speaker 5: projects get created, and those are all important conversations to

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Speaker 5: be having. But there is one very serious conversation about

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Speaker 5: the reality of AI, and that is how AI is

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Speaker 5: being used to do things like violate consent and further

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Speaker 5: use technology to make it seem like our bodies are

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Speaker 5: just like up for grabs once we show up online.

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Speaker 3: So case in point newdify apps. So newdifi apps.

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Speaker 5: Are kind of the catch all term for apps or

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Speaker 5: platforms that promise to use AI to generate non consensual

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Speaker 5: nude or semi nude images of anyone. These kinds of

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Speaker 5: apps have been exploding in popularity. In September alone, twenty

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Speaker 5: four million people visited newdify apps or undressing websites, according

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Speaker 5: to the social network analysis company Graphica. So in their analysis,

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Speaker 5: they really talk about how we've how this year specifically,

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Speaker 5: we've seen this shift where newdify apps kind of went

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Speaker 5: from this niche, underground custom thing where like, if you

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Speaker 5: were like a singular creep who had a singular fixation

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Speaker 5: on one person, you could find a marketplace for non

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Speaker 5: consensual images to be made of somebody, but it was

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Speaker 5: like a niche custom thing. Now in twenty twenty three,

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Speaker 5: we have really seen that shift to where these are

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Speaker 5: fleshed out, monetized online businesses, complete with advertising apparatuses. Graphica

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Speaker 5: found that the volume of referral link spam for these

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Speaker 5: kinds of services has increased by more than two thousand

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Speaker 5: percent on social media platforms like Reddit in Twitter since

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Speaker 5: the beginning of twenty twenty three, and this set of

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Speaker 5: fifty two Telegram groups used to access non consensual image

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Speaker 5: sites like these contain at least one million users as

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Speaker 5: of September of twenty twenty three.

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Speaker 3: So they have really exploded in popularity.

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Speaker 5: Yet I think that we're only now, like only recently

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Speaker 5: come to have any kind of conversation about what that means,

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Speaker 5: not just for you know, the women who are overwhelmingly

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Speaker 5: targeted by these kinds of apps, but also what it

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Speaker 5: means for our digital landscape more broadly.

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Speaker 6: Okay, I'm not gonna lie. None of this is familiar

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Speaker 6: to me as many social media things as I'm involved in.

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Speaker 4: Didn't know this was a thing. Didn't know.

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Speaker 3: It's pretty despicable. It first got on my radar.

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Speaker 5: It first seemed to be rolled out to like more

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Speaker 5: mainstream platforms with celebrities, so it'll be like, oh, you

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Speaker 5: can get a nude of any celebrity. And now it's like,

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Speaker 5: not just celebrities, it's like anybody who can get it.

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Speaker 5: You can generate a non consensual nude of anybody. And

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Speaker 5: so one of the ways that this is becoming more

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Speaker 5: and more ubiquitous is just seeing the advertisements for it

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Speaker 5: on social media. After some journalists at Bloomberg were looking

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Speaker 5: into the popularity of these kinds of apps, they contacted

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Speaker 5: both TikTok and Facebook to ask them about you know, Okay,

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Speaker 5: it seems like these really gross apps are being allowed

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Speaker 5: to advertise on your platforms, and both Facebook and TikTok

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Speaker 5: I will say, you know, to their credit when Bloomberg

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Speaker 5: reached out to them, they both did some work to

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Speaker 5: block search terms related to neudified apps, but one social

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Speaker 5: media platform notably did not do that.

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Speaker 3: Can you guess what platform that was? It was Twitter?

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Speaker 4: Twitter.

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Speaker 6: Obviously I'm not calling you a name's.

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Speaker 5: Call on Twitter still, so it was Twitter. They were like,

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Speaker 5: we're actually fine with these neuify apps, Like we don't

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Speaker 5: see a problem. Uh so this is from vice. Searching

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Speaker 5: the word undress on TikTok brings up no results in

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Speaker 5: either the top or video tabs. Instead, the platform warns

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Speaker 5: users that the phrase may be associated with activity that

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Speaker 5: violates the platform's guidelines. Searching the same term on Instagram

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Speaker 5: similarly brings up no results. Searching undress on Twitter, however,

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Speaker 5: readily surfaces a verified account with nearly twenty thousand followers,

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Speaker 5: promoting new TOFI app services. So let's say that you

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Speaker 5: were to search Twitter for the word in dress instead

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Speaker 5: of undress. Twitter is actually like, wait, were you actually

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Speaker 5: meeting to search for undressed? And it prompts you to

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Speaker 5: search for undress instead. So where other platforms are like, no,

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Speaker 5: we can't have that on our on our platforms, Twitter

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Speaker 5: is like not just allowing it on platforms, but like

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Speaker 5: helping people to search it when they when they get

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Speaker 5: it wrong, And so you might see people if you

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Speaker 5: ever see newdify apps advertise on these platforms, sometimes they

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Speaker 5: like the a word is misspelled, or like there's a

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Speaker 5: space between cople some of the letters to try to

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Speaker 5: evade being picked up and like knocked off the platform,

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Speaker 5: But it generally this does not seem like Twitter has

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Speaker 5: a problem with these kinds of services being advertised on

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Speaker 5: their platform.

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Speaker 6: And I feel like this is a big question with

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Speaker 6: like legalities, especially on public platforms like that in general.

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Speaker 6: But like I'm talking about doing news and such like,

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Speaker 6: it seems like it should be illegal. It seems like

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Speaker 6: this could be one of those things, especially like if

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Speaker 6: you think about revenge porn and all of that being

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Speaker 6: illegal in so many places, Like is that not a thing?

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Speaker 4: Is that?

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Speaker 6: Can you not take it to court or at least

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Speaker 6: try to stop these images from happening?

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Speaker 3: So that is a great question. When I first saw these,

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Speaker 3: I was like, this has got to be illegal, And

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Speaker 3: it turns out.

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Speaker 5: That right now, depending on where you live, it is

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Speaker 5: probably not illegal to do this.

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Speaker 3: It depends on your jurisdiction. But there is.

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Speaker 5: Currently no federal law criminalizing using AI to generate non

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Speaker 5: consensual deep fake images. Representative Evet Clark out of New

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Speaker 5: York has actually proposed legislation that would criminalize making, you know,

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Speaker 5: non consensual deep fakes, But as of right now, kind

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Speaker 5: of unbelievably, there is not any legislation federally that prevents

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Speaker 5: somebody from doing that. And yeah, I just think that, like,

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Speaker 5: as this kind of technology becomes more ubiquitous in our culture,

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Speaker 5: I think it adds to this idea that just by

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Speaker 5: showing up on the Internet, women are fair game for

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Speaker 5: anybody who wants to sexualize us, and that I think

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Speaker 5: it's getting to be getting to a point where the

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Speaker 5: idea is like, well, if you didn't want someone to

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Speaker 5: use your images in that way, why don't you post

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Speaker 5: them to Instagram?

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Speaker 3: Right Like?

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Speaker 5: And I think that we really got to have a

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Speaker 5: real serious think and a serious conversation about if that

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Speaker 5: is going to be a social media climate that we want.

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Speaker 5: And I think that that kind of thinking is the

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Speaker 5: kind of thicky that we need to leave behind in

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Speaker 5: twenty twenty three, right like, women should be able to

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Speaker 5: show up online without non consensual sexualization just being the

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Speaker 5: cost of showing up, And so let's leave that behind

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Speaker 5: in twenty twenty four.

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Speaker 3: We are not bringing it with.

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Speaker 6: Us please please.

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Speaker 2: Yeah, And that's one of the very frustrating things, since

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Speaker 2: I did see a lot about this, and we even

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Speaker 2: had an episode bridget about it about journalists getting targeted

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Speaker 2: by stuffy and revenge porn and things like that, and

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Speaker 2: this is just making it so much easier and it's

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Speaker 2: harder to for some people, for all of us to

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Speaker 2: ascertain like what is real and what is not real.

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Speaker 2: But it does kind of disproportionately impact women in march lines, folks.

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Speaker 2: And that's just one example of technology doing that that

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Speaker 2: you have on your list, right.

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Speaker 5: Yes, And I think, like, just like what you said,

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Speaker 5: I think that there are specific groups that we see

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Speaker 5: being targeted for this kind of harm first, and then it's.

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Speaker 3: Like oh nah, And then like when those groups.

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Speaker 5: When it's allowed to happen to those groups, it's like, oh, well,

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Speaker 5: you know, nobody really does anything. And then it's like,

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Speaker 5: oh surprise, Now it's everybody's problem. Now we just live

445
00:22:27,520 --> 00:22:30,399
Speaker 5: in a society where like this is commonplace. And like

446
00:22:30,480 --> 00:22:33,480
Speaker 5: maybe when this was happening to specific groups of people,

447
00:22:33,520 --> 00:22:35,520
Speaker 5: if we had done something and taken it seriously, then

448
00:22:35,760 --> 00:22:38,359
Speaker 5: we wouldn't have allowed it to just become ubiquitous, right,

449
00:22:38,440 --> 00:22:39,760
Speaker 5: and I don't think anybody wants to live in a

450
00:22:39,800 --> 00:22:44,000
Speaker 5: culture where anybody is fair game just because they were

451
00:22:44,000 --> 00:22:45,880
Speaker 5: all they put a picture of themselves on the Internet

452
00:22:46,040 --> 00:22:49,600
Speaker 5: to have that picture be distorted and sexualized. So absolutely right.

453
00:22:50,440 --> 00:22:53,320
Speaker 5: So that brings me to another thing that we should

454
00:22:53,359 --> 00:22:56,199
Speaker 5: not be taking with us into twenty twenty four, and

455
00:22:56,200 --> 00:22:59,240
Speaker 5: that it's content moderation policies that really hurt women and

456
00:22:59,359 --> 00:23:00,680
Speaker 5: other marched people.

457
00:23:01,040 --> 00:23:02,200
Speaker 3: So, as we were talking.

458
00:23:02,000 --> 00:23:05,840
Speaker 5: About with AI right now, AI is used in content

459
00:23:05,880 --> 00:23:09,000
Speaker 5: moderation that really does a lot of the work of

460
00:23:09,080 --> 00:23:12,760
Speaker 5: deciding what gets amplified and what gets suppressed on social media.

461
00:23:13,320 --> 00:23:18,040
Speaker 5: This technology, however, also objectifies women's bodies, and it's much

462
00:23:18,080 --> 00:23:21,720
Speaker 5: more likely to flag images that involve women or include

463
00:23:21,760 --> 00:23:25,280
Speaker 5: women as racy or inappropriate, and thus those images are

464
00:23:25,320 --> 00:23:28,040
Speaker 5: more likely to be suppressed on social media sites. So

465
00:23:28,119 --> 00:23:31,320
Speaker 5: The Guardian actually put together like a really interesting investigation

466
00:23:31,359 --> 00:23:34,320
Speaker 5: into this where they had journalists use AI tools to

467
00:23:34,359 --> 00:23:36,960
Speaker 5: analyze hundreds of photos of men and women in their underwear,

468
00:23:37,040 --> 00:23:40,600
Speaker 5: working out, using medical tests and with partial nudity, and

469
00:23:40,640 --> 00:23:43,680
Speaker 5: found that the AI used will tag photos of women

470
00:23:43,800 --> 00:23:46,679
Speaker 5: in everyday situations as sexually suggestive.

471
00:23:46,960 --> 00:23:49,960
Speaker 3: So I'm talking about images of like women.

472
00:23:49,960 --> 00:23:53,720
Speaker 5: In their underwear fully covered, or women working out at

473
00:23:53,720 --> 00:23:56,280
Speaker 5: the gym fully covered, right, like images that we would

474
00:23:56,359 --> 00:24:00,800
Speaker 5: recognize as not racy, but because they include women, the

475
00:24:00,880 --> 00:24:03,160
Speaker 5: tools that are being used to make decisions about how

476
00:24:03,200 --> 00:24:06,240
Speaker 5: content is moderated will be like, no, that's racy, can't

477
00:24:06,240 --> 00:24:09,600
Speaker 5: have that on social media. And so as a result

478
00:24:09,640 --> 00:24:13,600
Speaker 5: of this, social media companies that leverage these algorithms have

479
00:24:13,680 --> 00:24:18,840
Speaker 5: suppressed honestly countless images of featuring women's bodies. We know

480
00:24:18,880 --> 00:24:21,200
Speaker 5: that this hurts women led businesses, and this is reading

481
00:24:21,200 --> 00:24:26,080
Speaker 5: about how a shapewear company essentially can't advertise on social

482
00:24:26,119 --> 00:24:31,679
Speaker 5: media because images of women's bodies fully covered in shapewear

483
00:24:32,080 --> 00:24:34,840
Speaker 5: will just be suppressed by these algorithms, and they essentially

484
00:24:35,320 --> 00:24:37,680
Speaker 5: cannot advertise their product on social media, which is like

485
00:24:37,720 --> 00:24:41,359
Speaker 5: where you advertise products in twenty twenty three, this also like,

486
00:24:41,400 --> 00:24:44,440
Speaker 5: not only does it hurt female led businesses, it also

487
00:24:44,640 --> 00:24:48,600
Speaker 5: has medical impacts. They found that this disparity is also

488
00:24:48,640 --> 00:24:51,720
Speaker 5: true for medical images. AI was shown images from the

489
00:24:51,840 --> 00:24:55,120
Speaker 5: US National Cancer Institute demonstrating how to do a clinical

490
00:24:55,160 --> 00:24:59,199
Speaker 5: breast exam. Google's AI gave this photo the highest score

491
00:24:59,359 --> 00:25:03,760
Speaker 5: for race Microsoft's AI was eighty two percent confident that

492
00:25:03,800 --> 00:25:08,240
Speaker 5: these images of women doing breast exams was explicit sexually

493
00:25:08,400 --> 00:25:13,160
Speaker 5: in nature, and Amazon classified it as representing explicit nudity.

494
00:25:13,520 --> 00:25:16,240
Speaker 5: This is also true for pregnant bellies, like if you're

495
00:25:16,480 --> 00:25:19,840
Speaker 5: if you are heavily pregnant and showing a pregnant belly

496
00:25:20,040 --> 00:25:23,840
Speaker 5: on social media platforms, AI is much more likely to

497
00:25:24,040 --> 00:25:27,000
Speaker 5: deem that image to be racy and then suppress that

498
00:25:27,080 --> 00:25:29,639
Speaker 5: in their content moderation tools. And so you really get

499
00:25:29,640 --> 00:25:32,840
Speaker 5: a sense of the way that these platforms are creating

500
00:25:33,840 --> 00:25:38,080
Speaker 5: a disproportionate cost for being a woman who shows up online,

501
00:25:38,119 --> 00:25:40,600
Speaker 5: Like it prevents women from being able to express themselves,

502
00:25:40,760 --> 00:25:43,400
Speaker 5: it prevents women from being able to get medical information

503
00:25:43,440 --> 00:25:46,320
Speaker 5: about our bodies, and ultimately it's just not fair, like

504
00:25:46,320 --> 00:25:47,960
Speaker 5: we shouldn't have to deal with this just because we

505
00:25:48,000 --> 00:25:50,639
Speaker 5: showed up on our social media platform with our bodies, Like,

506
00:25:50,840 --> 00:25:54,280
Speaker 5: there's nothing wrong with women's bodies, they're they're not racy

507
00:25:54,359 --> 00:25:56,080
Speaker 5: or explicit just by us having.

508
00:25:55,880 --> 00:26:00,439
Speaker 2: Them right, right, And I believe Smith and I we

509
00:26:00,520 --> 00:26:02,959
Speaker 2: talked about this about YouTube because YouTube had a similar

510
00:26:03,040 --> 00:26:06,760
Speaker 2: strange thing that was happening where it was flagging videos

511
00:26:06,800 --> 00:26:10,119
Speaker 2: of children. The young girls is just like, this is

512
00:26:10,119 --> 00:26:13,080
Speaker 2: so sexual and it's just like literally young girls, and

513
00:26:13,119 --> 00:26:18,960
Speaker 2: it's telling to what is going on in our society,

514
00:26:19,040 --> 00:26:22,720
Speaker 2: the problems of objectifying and sexualizing women. But also this

515
00:26:22,760 --> 00:26:24,159
Speaker 2: is a big thing that we talked about with you,

516
00:26:24,200 --> 00:26:29,080
Speaker 2: Bridget when it matters who's making these things, who's doing

517
00:26:29,119 --> 00:26:32,639
Speaker 2: this stuff, and AI is a pretty new space, but

518
00:26:32,680 --> 00:26:35,320
Speaker 2: I've already seen a lot of conversations about that, about

519
00:26:35,920 --> 00:26:38,840
Speaker 2: the importance of who is working on it.

520
00:26:39,640 --> 00:26:43,480
Speaker 1: So that's part of what is going on here, right, totally.

521
00:26:43,600 --> 00:26:46,240
Speaker 5: So The Guardian spoke to Margaret Mitchell of the AI

522
00:26:46,280 --> 00:26:49,199
Speaker 5: company Hugging Face, who said that she believes that the

523
00:26:49,200 --> 00:26:52,360
Speaker 5: photos used to train these algorithms were probably just being

524
00:26:52,440 --> 00:26:56,399
Speaker 5: labeled by straight men who probably associate men working out

525
00:26:56,480 --> 00:26:59,960
Speaker 5: with fitness, but maybe consider an image of a woman

526
00:27:00,080 --> 00:27:04,080
Speaker 5: and working out as being like racy or sexual or explicit,

527
00:27:04,359 --> 00:27:07,200
Speaker 5: even though it's like the same theme, the same content.

528
00:27:07,440 --> 00:27:10,159
Speaker 5: And so it's possible that these ratings seem gender biased

529
00:27:10,200 --> 00:27:13,120
Speaker 5: in the US and in Europe because the labelers might

530
00:27:13,160 --> 00:27:16,040
Speaker 5: be from a place that might have more conservative cultures, right,

531
00:27:16,080 --> 00:27:20,560
Speaker 5: And so yeah, it really matters who is building technology,

532
00:27:20,600 --> 00:27:22,760
Speaker 5: who's in the room and the technology gets built, who

533
00:27:22,840 --> 00:27:24,639
Speaker 5: is training it, who is rolling it out, who is

534
00:27:24,680 --> 00:27:26,840
Speaker 5: thinking about it, who's writing about it, who's talking about it.

535
00:27:27,200 --> 00:27:32,200
Speaker 5: If these people are mostly men, then like, of course

536
00:27:32,240 --> 00:27:35,800
Speaker 5: women and other marginalized people are not showing up in

537
00:27:35,840 --> 00:27:38,359
Speaker 5: an equitable way. With all the conversations that we have

538
00:27:38,440 --> 00:27:41,160
Speaker 5: around things like inclusion and diversity and.

539
00:27:41,080 --> 00:27:44,600
Speaker 3: Equity in tech, I'm not like harping on those.

540
00:27:44,400 --> 00:27:46,320
Speaker 5: Just because it's like nice to do or it's like

541
00:27:46,359 --> 00:27:48,639
Speaker 5: the right thing to do, which it is. It is

542
00:27:48,680 --> 00:27:53,280
Speaker 5: because eventually the technology that gets built is worse, is

543
00:27:53,359 --> 00:27:56,840
Speaker 5: less inclusive, is more dangerous, like it includes less people,

544
00:27:56,840 --> 00:27:58,480
Speaker 5: and that ends up hurting all of us.

545
00:27:58,840 --> 00:28:00,080
Speaker 1: Yes, it does.

546
00:28:00,160 --> 00:28:02,640
Speaker 2: And speaking of that, you have another point on here,

547
00:28:02,720 --> 00:28:05,280
Speaker 2: going back to something we were talking about earlier about

548
00:28:05,720 --> 00:28:07,760
Speaker 2: women in journalism.

549
00:28:07,920 --> 00:28:12,080
Speaker 5: Yes, yes, so I'm so glad that was a great transition,

550
00:28:12,320 --> 00:28:15,879
Speaker 5: which is that you know, it's like one of the

551
00:28:15,920 --> 00:28:18,879
Speaker 5: reasons why I started my own podcast about this is

552
00:28:18,920 --> 00:28:24,200
Speaker 5: that we unfortunately have a tech culture that can treat

553
00:28:24,280 --> 00:28:29,520
Speaker 5: women like perpetual outsiders, right, whether by accident or with intention,

554
00:28:30,040 --> 00:28:31,600
Speaker 5: and that is something that we got to leave in

555
00:28:31,640 --> 00:28:33,600
Speaker 5: twenty twenty three. Really, we could have left it like

556
00:28:34,840 --> 00:28:37,119
Speaker 5: in like many many many years past.

557
00:28:37,400 --> 00:28:38,920
Speaker 3: But this is the time that we should be.

558
00:28:38,960 --> 00:28:41,960
Speaker 5: Leaving it in the past because exactly what you said,

559
00:28:41,960 --> 00:28:44,360
Speaker 5: it is so critical. These tools are going to be

560
00:28:44,400 --> 00:28:47,600
Speaker 5: shaping our world and how we understand our world.

561
00:28:47,640 --> 00:28:48,400
Speaker 3: So we've got to make.

562
00:28:48,320 --> 00:28:51,920
Speaker 5: Sure that people who are publicly talking about it and

563
00:28:51,960 --> 00:28:54,080
Speaker 5: are included in that conversation are done so in a

564
00:28:54,120 --> 00:28:56,560
Speaker 5: way that does not treat them like perpetual outsiders. And

565
00:28:56,600 --> 00:29:00,080
Speaker 5: so women who are working for that AI company that

566
00:29:00,120 --> 00:29:02,920
Speaker 5: I mentioned before, Hugging Face, what you can sort of

567
00:29:02,920 --> 00:29:05,520
Speaker 5: think of as like a competitor to Open AI, the

568
00:29:05,560 --> 00:29:07,480
Speaker 5: company that makes chat GPT, which is run by.

569
00:29:07,400 --> 00:29:08,320
Speaker 3: That guy Sam Altman.

570
00:29:08,880 --> 00:29:11,560
Speaker 5: Hugging Face has a lot of women who work there,

571
00:29:11,840 --> 00:29:13,240
Speaker 5: and these women do a ton of.

572
00:29:13,160 --> 00:29:16,080
Speaker 3: Like public speaking about tech and AI.

573
00:29:15,800 --> 00:29:19,000
Speaker 5: And the media, which is great, and I also think

574
00:29:19,040 --> 00:29:21,440
Speaker 5: again it's important because it can help to change the

575
00:29:21,480 --> 00:29:24,120
Speaker 5: face of like who we think of as somebody who

576
00:29:24,160 --> 00:29:26,120
Speaker 5: gets to speak or gets to have an opinion when

577
00:29:26,120 --> 00:29:29,600
Speaker 5: it comes to technology, and that's great. However, the women

578
00:29:29,640 --> 00:29:32,040
Speaker 5: at hugging Face also noticed that when they were doing

579
00:29:32,080 --> 00:29:35,040
Speaker 5: public speaking about AI with the press, they sometimes would

580
00:29:35,080 --> 00:29:39,440
Speaker 5: get like sexist or otherwise kind of messed up questions

581
00:29:39,560 --> 00:29:42,880
Speaker 5: during interviews that just like really highlighted that they were

582
00:29:42,960 --> 00:29:48,600
Speaker 5: not necessarily being treated like people who belonged in the space.

583
00:29:49,280 --> 00:29:52,680
Speaker 5: Margaret Mitchell, she is Hugging Face as chief Ethics scientist,

584
00:29:53,040 --> 00:29:55,480
Speaker 5: framed it as a research question. She asked what are

585
00:29:55,600 --> 00:29:58,640
Speaker 5: patterns and how journalists talk to and about women and AI.

586
00:29:59,480 --> 00:30:02,280
Speaker 5: She found that compared to male peers, there is a

587
00:30:02,320 --> 00:30:07,440
Speaker 5: disproportionate focus from the press on their ages, their motherhood,

588
00:30:07,760 --> 00:30:12,080
Speaker 5: their physical appearance or behaviors, their failures, and what AI

589
00:30:12,680 --> 00:30:16,240
Speaker 5: gossip they could provide, rather than their like technical work.

590
00:30:16,640 --> 00:30:20,080
Speaker 5: And these are people who are incredibly accomplished. They're like

591
00:30:20,200 --> 00:30:22,480
Speaker 5: doing very important works that they've gone to school for

592
00:30:22,680 --> 00:30:25,200
Speaker 5: been trained for. In an article, if you get to

593
00:30:25,280 --> 00:30:28,040
Speaker 5: interview them and you ask about their age, or you

594
00:30:28,080 --> 00:30:30,280
Speaker 5: ask if they have kids, or you ask you know,

595
00:30:30,360 --> 00:30:33,800
Speaker 5: about their the way they look, it's it's so limiting

596
00:30:33,800 --> 00:30:35,880
Speaker 5: because it's like you have somebody who has dedicated their

597
00:30:35,920 --> 00:30:38,560
Speaker 5: life to this very important technology and this is what

598
00:30:38,640 --> 00:30:39,200
Speaker 5: you ask them.

599
00:30:39,240 --> 00:30:41,400
Speaker 3: Like it's such a it's such a miss on part

600
00:30:41,480 --> 00:30:42,680
Speaker 3: on the part of the journalists.

601
00:30:43,080 --> 00:30:47,000
Speaker 6: It's like I thought you by now at this point

602
00:30:47,000 --> 00:30:49,280
Speaker 6: in AI, there would be more things to talk about.

603
00:30:49,280 --> 00:30:52,000
Speaker 6: There's so many questions that we should be asking again,

604
00:30:52,120 --> 00:30:55,280
Speaker 6: things like how are how are sexist issues being handled

605
00:30:55,440 --> 00:30:58,760
Speaker 6: in AI? How are you protecting women in AI? And

606
00:30:58,800 --> 00:31:02,400
Speaker 6: not about the person be like, so you got a kid,

607
00:31:03,760 --> 00:31:05,520
Speaker 6: you gotta be on the computer while you have a kid.

608
00:31:05,920 --> 00:31:08,240
Speaker 6: How are you gonna do that? Like this seems so

609
00:31:08,440 --> 00:31:10,600
Speaker 6: like far foot, Like why is this? Is this a

610
00:31:10,640 --> 00:31:13,280
Speaker 6: scot are you? Are we still doing this?

611
00:31:13,880 --> 00:31:16,880
Speaker 5: What's frustrating is like they would never ask a man.

612
00:31:17,560 --> 00:31:19,840
Speaker 5: If you were talking to like a powerful man, you

613
00:31:19,840 --> 00:31:21,640
Speaker 5: would never be like, oh, well, who watches your kids

614
00:31:21,640 --> 00:31:23,959
Speaker 5: while you're working? Or like how do how do how

615
00:31:23,960 --> 00:31:27,480
Speaker 5: do you juggle being a dad and like being a scientist?

616
00:31:27,520 --> 00:31:29,160
Speaker 5: Like these are things that would not that like would

617
00:31:29,160 --> 00:31:31,400
Speaker 5: not come up. They're only coming up because they're women.

618
00:31:41,960 --> 00:31:44,600
Speaker 5: Doctor Sasha Lucioni, who was an AI researcher and a

619
00:31:44,640 --> 00:31:48,240
Speaker 5: climate lead at Hucking Face, was in this like pretty glowing.

620
00:31:48,000 --> 00:31:49,000
Speaker 3: Piece for Adweek.

621
00:31:49,400 --> 00:31:52,280
Speaker 5: The piece was great, but the headline to the article

622
00:31:52,360 --> 00:31:56,280
Speaker 5: read this AI ethics expert juggles motherhood and a tech

623
00:31:56,360 --> 00:31:59,280
Speaker 5: career and people had to like raise hell to get

624
00:31:59,280 --> 00:32:02,320
Speaker 5: them to change it and Yeah, it's just like I

625
00:32:02,400 --> 00:32:05,360
Speaker 5: do think that there is a place for conversations about

626
00:32:05,440 --> 00:32:10,440
Speaker 5: what it looks like to juggle career and family and

627
00:32:10,480 --> 00:32:13,160
Speaker 5: all of that, But those are not conversations that should

628
00:32:13,200 --> 00:32:15,040
Speaker 5: only be happening to women, and there's a time and

629
00:32:15,080 --> 00:32:17,000
Speaker 5: a place for them, right, Like, if you're meant to

630
00:32:17,040 --> 00:32:20,960
Speaker 5: be interviewing somebody about their technical prowess, that I don't

631
00:32:20,960 --> 00:32:24,440
Speaker 5: see how that is relevant to their technical expertise.

632
00:32:24,280 --> 00:32:26,400
Speaker 6: Right, And that piece in the self the title is

633
00:32:26,440 --> 00:32:28,920
Speaker 6: so condescending? Is that definitely like Ah.

634
00:32:28,800 --> 00:32:30,840
Speaker 4: Look at you, how cute? Look at you doing that?

635
00:32:31,240 --> 00:32:33,960
Speaker 6: You go, you go, girl, I'm so proud of you,

636
00:32:34,000 --> 00:32:37,640
Speaker 6: instead of seeing this professional scientist who has more degrees

637
00:32:37,800 --> 00:32:40,520
Speaker 6: and more experience than the person who actually probably wrote

638
00:32:40,960 --> 00:32:43,239
Speaker 6: the article. I don't know for sure, but just that

639
00:32:43,360 --> 00:32:46,440
Speaker 6: level of like, wow, really, what are you doing? Like

640
00:32:46,800 --> 00:32:48,560
Speaker 6: is it because you feel insecure that you need to

641
00:32:48,560 --> 00:32:51,080
Speaker 6: be condescending but maybe like passive aggressively like but no, no, no,

642
00:32:51,160 --> 00:32:51,960
Speaker 6: I'm really impressed.

643
00:32:52,240 --> 00:32:56,560
Speaker 5: Really it is so condescending, and so, in an effort

644
00:32:56,720 --> 00:32:59,400
Speaker 5: to help the space be better, the women who work

645
00:32:59,400 --> 00:33:02,960
Speaker 5: at Hugging actually developed a guide for journalists.

646
00:33:02,240 --> 00:33:03,600
Speaker 3: To help get it right.

647
00:33:03,680 --> 00:33:06,200
Speaker 5: To help create a better dynamic so that it's not

648
00:33:06,280 --> 00:33:11,200
Speaker 5: just condescending question and sexist question after sex's question, the

649
00:33:11,240 --> 00:33:14,080
Speaker 5: guide reads, the real achievements of women on our team

650
00:33:14,160 --> 00:33:17,040
Speaker 5: often get overshadowed by a focus on personal and sometimes

651
00:33:17,160 --> 00:33:20,080
Speaker 5: very intrusive details that are not relevant to their work.

652
00:33:20,400 --> 00:33:22,880
Speaker 5: With all the amazing press attention we get at hugging Face,

653
00:33:23,120 --> 00:33:26,240
Speaker 5: we're bound to see some journalists rely on outdated tropes. Lately,

654
00:33:26,240 --> 00:33:29,360
Speaker 5: we've seen more reporters ignoring our amazing achievements of our

655
00:33:29,400 --> 00:33:33,680
Speaker 5: shees and days and instead focusing on stereotypes in tech.

656
00:33:33,960 --> 00:33:35,600
Speaker 5: One of the things that I love about the guys

657
00:33:35,640 --> 00:33:39,920
Speaker 5: that they put out is that it really highlights the

658
00:33:39,960 --> 00:33:43,320
Speaker 5: importance of not treating tech like a place where women

659
00:33:43,480 --> 00:33:46,680
Speaker 5: don't belong. And so, for instance, the guide reads, don't

660
00:33:46,680 --> 00:33:50,200
Speaker 5: rely on antiquated stereotypes about women in tech. This includes

661
00:33:50,240 --> 00:33:53,479
Speaker 5: describing women as outsiders in the field, which only serves

662
00:33:53,520 --> 00:33:56,320
Speaker 5: to reinforce the idea that women don't belong in tech.

663
00:33:56,880 --> 00:34:00,200
Speaker 5: An example of a problematic sentence they give despite being

664
00:34:00,240 --> 00:34:02,960
Speaker 5: a woman in a male dominated field. Brooke Brookie has

665
00:34:02,960 --> 00:34:05,800
Speaker 5: made a name for herself in the tech industry. Better

666
00:34:06,120 --> 00:34:09,600
Speaker 5: is through her brilliant results on magic and large language models.

667
00:34:09,640 --> 00:34:12,520
Speaker 5: Brooke Brookie made a name for herself in the tech industry.

668
00:34:12,880 --> 00:34:15,360
Speaker 5: And so this I think is really key, and I

669
00:34:15,360 --> 00:34:18,759
Speaker 5: think people do this without even really meaning to you,

670
00:34:19,000 --> 00:34:23,400
Speaker 5: is that the tech industry, women and queer folks and

671
00:34:23,480 --> 00:34:27,040
Speaker 5: trans folks and black folks and folks with disabilities and

672
00:34:27,600 --> 00:34:30,320
Speaker 5: all different kinds of people who are traditionally marginaliznsed in

673
00:34:30,400 --> 00:34:33,880
Speaker 5: our society have been at the beginning of technology and

674
00:34:33,960 --> 00:34:35,360
Speaker 5: have been since the very start.

675
00:34:35,640 --> 00:34:36,160
Speaker 3: We have this.

676
00:34:36,239 --> 00:34:38,760
Speaker 5: Idea that like, oh, tech is this like white male

677
00:34:38,880 --> 00:34:41,160
Speaker 5: cis boys club and anybody else is trying to break

678
00:34:41,200 --> 00:34:43,920
Speaker 5: their way in, and that's I can understand why people

679
00:34:43,920 --> 00:34:46,400
Speaker 5: feel that way, But we have been there from the

680
00:34:46,520 --> 00:34:49,600
Speaker 5: very beginning. And if you don't always hear our stories

681
00:34:49,719 --> 00:34:50,840
Speaker 5: or our voices, it's.

682
00:34:50,680 --> 00:34:51,640
Speaker 3: Not because we're not there.

683
00:34:51,640 --> 00:34:54,600
Speaker 5: It's because specific choices have been made to keep to

684
00:34:54,680 --> 00:34:57,600
Speaker 5: like sideline US and to turn down our voices. And

685
00:34:57,680 --> 00:35:00,680
Speaker 5: so really starting from a place that like, we do

686
00:35:00,800 --> 00:35:03,319
Speaker 5: belong in these conversations, we do belong in tech, we

687
00:35:03,360 --> 00:35:04,400
Speaker 5: do belong in the sector.

688
00:35:04,600 --> 00:35:06,319
Speaker 3: We're not outsiders, I.

689
00:35:06,280 --> 00:35:10,480
Speaker 5: Think is really really important, and it also just really

690
00:35:10,719 --> 00:35:14,160
Speaker 5: matters you know, if the people who are building technology

691
00:35:14,440 --> 00:35:18,480
Speaker 5: and talking about technology and thinking about technology are only

692
00:35:18,600 --> 00:35:21,279
Speaker 5: one type of person, the tech that gets built is

693
00:35:21,280 --> 00:35:21,680
Speaker 5: gonna be a.

694
00:35:21,680 --> 00:35:22,520
Speaker 3: Whole lot worse.

695
00:35:22,560 --> 00:35:25,399
Speaker 5: And so all of us benefit when more voices are

696
00:35:25,400 --> 00:35:29,200
Speaker 5: included and feel included and feel empowered to join the conversation.

697
00:35:29,560 --> 00:35:31,720
Speaker 6: You would think that would be an obvious and also

698
00:35:31,760 --> 00:35:33,919
Speaker 6: that it would be it would save money, It would

699
00:35:33,920 --> 00:35:37,760
Speaker 6: save money, and all the revamping and redoing of everything

700
00:35:37,840 --> 00:35:41,440
Speaker 6: that you knew, well, I would expect you knew, but

701
00:35:41,800 --> 00:35:44,000
Speaker 6: that could go wrong when you don't have the right

702
00:35:44,040 --> 00:35:47,719
Speaker 6: people or all the people at least represented in this conversation,

703
00:35:47,800 --> 00:35:50,600
Speaker 6: especially if you're wanting these people to use your technology.

704
00:35:55,160 --> 00:35:57,879
Speaker 5: You're so right, And like, what's interesting is I once

705
00:35:57,920 --> 00:36:00,800
Speaker 5: read this book called Mothers of Invention, all about how

706
00:36:01,320 --> 00:36:05,960
Speaker 5: things like misogyny and bias around women stifle innovation. And

707
00:36:06,600 --> 00:36:09,640
Speaker 5: something that's really struck me from that book is how

708
00:36:09,719 --> 00:36:12,600
Speaker 5: much money gets left on the table because of things

709
00:36:12,640 --> 00:36:15,279
Speaker 5: like gender bias and misogyny. It's like, you would think

710
00:36:15,320 --> 00:36:18,560
Speaker 5: in a capitalistic society that for profit companies would want

711
00:36:18,719 --> 00:36:21,880
Speaker 5: money above anything. You might be surprised because they are

712
00:36:22,000 --> 00:36:27,239
Speaker 5: perfectly willing to lose money if it means further entrenching

713
00:36:27,480 --> 00:36:29,359
Speaker 5: gender bias and massogeity.

714
00:36:29,600 --> 00:36:32,040
Speaker 3: Yeah, which is so nonsensical.

715
00:36:32,000 --> 00:36:34,600
Speaker 6: Right, I guess it's power over money, even though money

716
00:36:34,600 --> 00:36:35,920
Speaker 6: could be considered power.

717
00:36:36,360 --> 00:36:38,040
Speaker 7: Yeah.

718
00:36:38,400 --> 00:36:40,320
Speaker 2: Yeah, we were doing I was trying to do a

719
00:36:40,320 --> 00:36:43,680
Speaker 2: wrap up episode like this about video games and board games,

720
00:36:44,160 --> 00:36:48,800
Speaker 2: and I ran across kind of a stunning, upsetting amount

721
00:36:49,040 --> 00:36:54,160
Speaker 2: of how often people are in power who are Dudes

722
00:36:54,160 --> 00:36:59,279
Speaker 2: were like, it's just women don't make good stuff, and

723
00:36:59,320 --> 00:37:02,439
Speaker 2: they would literally be like, uh, people don't buy those things,

724
00:37:02,480 --> 00:37:05,799
Speaker 2: those party games, Why yes they do.

725
00:37:07,320 --> 00:37:10,040
Speaker 5: Yeah, look at the demographics of who is actually playing

726
00:37:10,120 --> 00:37:12,600
Speaker 5: video games. It is a lot of women, and so

727
00:37:12,719 --> 00:37:18,160
Speaker 5: sat like, I think not making video games and stuff

728
00:37:18,160 --> 00:37:21,480
Speaker 5: that like women want when a big chunk of your

729
00:37:21,480 --> 00:37:24,120
Speaker 5: customer base just is women, it's a fact that is

730
00:37:24,160 --> 00:37:25,799
Speaker 5: on you, that is like you don't know how to

731
00:37:25,800 --> 00:37:29,000
Speaker 5: do it or unwilling to do it. It's like any

732
00:37:29,160 --> 00:37:30,880
Speaker 5: anything that you could say, it's just an excuse to

733
00:37:30,960 --> 00:37:31,600
Speaker 5: not do it.

734
00:37:31,600 --> 00:37:34,600
Speaker 6: Right, Like you look at animal crossing and the fact

735
00:37:34,800 --> 00:37:39,479
Speaker 6: they may bank, especially during COVID and quarantine, and you're

736
00:37:39,520 --> 00:37:41,720
Speaker 6: like you sure, are you sure?

737
00:37:42,760 --> 00:37:43,440
Speaker 1: Yeah?

738
00:37:43,520 --> 00:37:45,719
Speaker 2: This guy in question was he was saying like all

739
00:37:45,760 --> 00:37:48,279
Speaker 2: the games women like to play, which are like, in

740
00:37:48,400 --> 00:37:52,680
Speaker 2: his mind, are much more too political or two like parties,

741
00:37:53,040 --> 00:37:58,640
Speaker 2: like animal Mobile games like aren't good and don't make money,

742
00:37:58,640 --> 00:38:01,760
Speaker 2: and it's like, uh.

743
00:38:00,000 --> 00:38:01,680
Speaker 3: But are you sure.

744
00:38:03,200 --> 00:38:03,840
Speaker 4: I can't?

745
00:38:04,520 --> 00:38:05,200
Speaker 3: Why?

746
00:38:05,560 --> 00:38:05,920
Speaker 6: Oh?

747
00:38:06,480 --> 00:38:09,480
Speaker 2: This also reminds me of it's kind of upsetting, but

748
00:38:09,560 --> 00:38:13,400
Speaker 2: it reminds me of when Sally Ride was, you know,

749
00:38:13,440 --> 00:38:16,319
Speaker 2: getting ready to go to space, and they would have

750
00:38:16,360 --> 00:38:19,120
Speaker 2: these press conferences with her and all these men and

751
00:38:19,160 --> 00:38:22,240
Speaker 2: the men they're asking like all these like questions related

752
00:38:22,280 --> 00:38:23,160
Speaker 2: to the jab and her.

753
00:38:23,200 --> 00:38:25,040
Speaker 1: They'd be like, how are you going to put your

754
00:38:25,080 --> 00:38:26,200
Speaker 1: makeup on space?

755
00:38:29,600 --> 00:38:33,399
Speaker 3: What it has?

756
00:38:33,440 --> 00:38:33,719
Speaker 6: It? Like?

757
00:38:33,800 --> 00:38:38,240
Speaker 2: It's still there that kind of we still gender these things,

758
00:38:38,280 --> 00:38:40,680
Speaker 2: and we still other people, and it does really matter

759
00:38:40,719 --> 00:38:44,880
Speaker 2: who's writing about it and who whose stories are getting

760
00:38:44,960 --> 00:38:49,920
Speaker 2: published or getting that traction, because you know, if it's

761
00:38:50,200 --> 00:38:53,520
Speaker 2: mostly white men's stories who's getting like all of the attention,

762
00:38:55,080 --> 00:38:57,919
Speaker 2: then that does help reinforce this idea that yeah, it's

763
00:38:57,960 --> 00:38:59,920
Speaker 2: this white male space when it isn't.

764
00:39:00,080 --> 00:39:01,320
Speaker 1: Has it been? As you said?

765
00:39:01,800 --> 00:39:03,880
Speaker 5: Yeah, did you ever hear? I mean, I'm sure you

766
00:39:04,040 --> 00:39:06,000
Speaker 5: all have heard this story. But it really sticks with

767
00:39:06,040 --> 00:39:08,520
Speaker 5: me that when Sally Ride was going to space. She's

768
00:39:08,560 --> 00:39:12,200
Speaker 5: gonna be there for one week, and the like scientists

769
00:39:12,280 --> 00:39:15,319
Speaker 5: or engineers at NASA whoever, were like, they gave her

770
00:39:15,320 --> 00:39:18,680
Speaker 5: a hunt. Was it two hundred tampons or one hundred tampons?

771
00:39:18,800 --> 00:39:20,000
Speaker 3: She gonna be there for one week?

772
00:39:20,320 --> 00:39:22,719
Speaker 5: And when she was like, oh, one hundred tampons, they

773
00:39:22,760 --> 00:39:26,120
Speaker 5: were like, will that be enough? Like these are the

774
00:39:26,160 --> 00:39:29,759
Speaker 5: greatest scientific mott like, right, nobody can tell me that,

775
00:39:29,840 --> 00:39:34,719
Speaker 5: like male scientists are like better than women when that's

776
00:39:34,760 --> 00:39:36,920
Speaker 5: what's going on with our male second.

777
00:39:37,239 --> 00:39:40,279
Speaker 2: Right, I can you imagine if you honestly believe that

778
00:39:40,440 --> 00:39:44,880
Speaker 2: was true and then for to expect the women in

779
00:39:44,920 --> 00:39:47,360
Speaker 2: your life or the people who menstraight in your life

780
00:39:47,640 --> 00:39:48,040
Speaker 2: to just.

781
00:39:48,000 --> 00:39:51,000
Speaker 1: Be going through two hundred tampots.

782
00:39:50,520 --> 00:39:53,040
Speaker 6: In a way, we have to pay that much when

783
00:39:53,080 --> 00:39:55,080
Speaker 6: you were like looking at tampons and they're like thirty

784
00:39:55,320 --> 00:39:59,400
Speaker 6: four ways of like eight dollars or thirty thirty.

785
00:40:00,280 --> 00:40:02,880
Speaker 3: So it's like how much that I have so many questions?

786
00:40:02,920 --> 00:40:04,560
Speaker 4: I have so many, so many questions.

787
00:40:04,600 --> 00:40:07,600
Speaker 6: There's so many questions, and to be fair and like

788
00:40:07,640 --> 00:40:09,799
Speaker 6: in everything, like how did you think this was gonna work?

789
00:40:09,840 --> 00:40:12,000
Speaker 4: Like who invented this? This is not? This is this

790
00:40:12,040 --> 00:40:13,919
Speaker 4: doesn't work like that, what are you doing?

791
00:40:14,800 --> 00:40:15,040
Speaker 3: God?

792
00:40:15,120 --> 00:40:19,440
Speaker 5: Sometimes like, Hugh, this is such a non secutor sometimes

793
00:40:19,480 --> 00:40:24,280
Speaker 5: when I think about gender dynamics, like I remember reading

794
00:40:24,280 --> 00:40:27,759
Speaker 5: this tweet from a guy who was like, people think

795
00:40:27,800 --> 00:40:31,319
Speaker 5: Taylor Swift is so pretty, but here she is without makeup, And.

796
00:40:31,280 --> 00:40:32,960
Speaker 3: It's like, did you think that she was born with

797
00:40:33,000 --> 00:40:36,399
Speaker 3: cat eyes and like break red lips? And you thought

798
00:40:36,480 --> 00:40:39,319
Speaker 3: all of us thought that. You clearly thought that, but

799
00:40:39,400 --> 00:40:39,920
Speaker 3: you thought all of.

800
00:40:40,000 --> 00:40:42,680
Speaker 5: Us thought that, Like and then you're like you're supposed

801
00:40:42,680 --> 00:40:44,960
Speaker 5: to be getting one over on us women, Like the

802
00:40:45,000 --> 00:40:46,360
Speaker 5: whole thing is just laughable.

803
00:40:47,320 --> 00:40:51,480
Speaker 2: Yeah, it's like such a great example of who we

804
00:40:51,600 --> 00:40:55,520
Speaker 2: consider the norm in society, whose experience we consider the

805
00:40:55,560 --> 00:40:59,200
Speaker 2: norm we base it on, Because I I was thinking

806
00:40:59,239 --> 00:41:03,840
Speaker 2: about that recently too, about this whole idea that I

807
00:41:03,840 --> 00:41:07,279
Speaker 2: feel like has faded away largely but not always. But

808
00:41:07,400 --> 00:41:09,319
Speaker 2: like women who would wake up early and put on

809
00:41:09,360 --> 00:41:14,040
Speaker 2: makeup so their partner would not see them without makeup.

810
00:41:14,480 --> 00:41:18,560
Speaker 2: So it's like it's like a lot of us feel

811
00:41:18,600 --> 00:41:22,439
Speaker 2: that we have to do this thing because then we'll

812
00:41:22,440 --> 00:41:27,040
Speaker 2: get this complaint from this random jackets on facial media.

813
00:41:27,760 --> 00:41:30,080
Speaker 2: But then it's also like, dude, what do you think

814
00:41:30,480 --> 00:41:35,560
Speaker 2: it's strange. It's like a strange dynamic that is happening

815
00:41:35,760 --> 00:41:38,440
Speaker 2: where you feel the pressure to do it. But then

816
00:41:38,600 --> 00:41:42,560
Speaker 2: also shouldn't they shouldn't people realize how much work.

817
00:41:42,400 --> 00:41:45,359
Speaker 6: It is, right, Yeah, And it kind of goes into

818
00:41:45,360 --> 00:41:47,919
Speaker 6: another question that I'm kind of concerned about. I've had,

819
00:41:48,200 --> 00:41:50,359
Speaker 6: like I've been noticing more and more on TikTok they

820
00:41:50,400 --> 00:41:52,960
Speaker 6: just have automatic filters, and a lot of people talk

821
00:41:53,000 --> 00:41:55,120
Speaker 6: about the fact that they use different filters, especially because

822
00:41:55,120 --> 00:41:56,960
Speaker 6: they don't want to wear makeup and all that, and

823
00:41:56,960 --> 00:41:59,520
Speaker 6: this feels makes them feel better, which all, all in all,

824
00:41:59,520 --> 00:42:02,520
Speaker 6: whatever you do, you but then this level of expectation

825
00:42:03,640 --> 00:42:07,919
Speaker 6: that especially men, especially I must say sis heterosexual men,

826
00:42:08,840 --> 00:42:11,040
Speaker 6: think that this is what you should look like and

827
00:42:11,080 --> 00:42:13,400
Speaker 6: if you don't look like that in real life, holy

828
00:42:13,840 --> 00:42:15,840
Speaker 6: catfished me and you've lied to me. Like in this

829
00:42:16,560 --> 00:42:20,480
Speaker 6: new level of standard, a beauty standard, that might been like, Okay,

830
00:42:20,480 --> 00:42:22,560
Speaker 6: this is good for us because it makes us kind

831
00:42:22,560 --> 00:42:24,319
Speaker 6: of feel better. But at the same time, what is

832
00:42:24,320 --> 00:42:24,920
Speaker 6: it leading to.

833
00:42:25,760 --> 00:42:28,560
Speaker 3: That's such an interesting question. And I guess I just

834
00:42:28,600 --> 00:42:29,200
Speaker 3: think that like.

835
00:42:31,160 --> 00:42:35,919
Speaker 5: A lot of men, like SIS heterosexual men, I think,

836
00:42:35,960 --> 00:42:39,600
Speaker 5: are really willing to live in a fantasy world like

837
00:42:39,640 --> 00:42:42,319
Speaker 5: they're really like it's like if you if you think that,

838
00:42:42,560 --> 00:42:45,160
Speaker 5: you know, some of those TikTok filters, they give you

839
00:42:45,239 --> 00:42:47,520
Speaker 5: like glitter on your eyes, if you thought that, like

840
00:42:47,600 --> 00:42:49,759
Speaker 5: somebody came out of the womb with like glitter on

841
00:42:49,800 --> 00:42:53,120
Speaker 5: their eyes, like I sometimes it's it's honestly, it's one

842
00:42:53,120 --> 00:42:55,319
Speaker 5: of the reasons why I don't really remove my body hair,

843
00:42:55,360 --> 00:42:57,440
Speaker 5: because when you add up how much time it takes

844
00:42:57,480 --> 00:42:59,440
Speaker 5: to do it, it's like, it's actually not it's a

845
00:42:59,440 --> 00:43:02,239
Speaker 5: lot of work. And it's like, do I want to

846
00:43:02,280 --> 00:43:05,239
Speaker 5: do extra work and pay whatever extra money it takes

847
00:43:05,280 --> 00:43:07,560
Speaker 5: to by the razor and this and that to feed

848
00:43:07,600 --> 00:43:09,040
Speaker 5: into a fantasy that.

849
00:43:09,200 --> 00:43:11,640
Speaker 3: Adult women don't have body hair. No, I don't.

850
00:43:11,719 --> 00:43:14,640
Speaker 5: Like if you're if you're like, you should by now

851
00:43:14,680 --> 00:43:17,200
Speaker 5: know that adult women do you generally have body hair.

852
00:43:17,200 --> 00:43:23,080
Speaker 5: And I'm not interested in perpetuating this like very weird

853
00:43:23,280 --> 00:43:26,680
Speaker 5: fantasy world where this becomes how women are in the world.

854
00:43:26,960 --> 00:43:28,640
Speaker 5: And then if you see a woman who is not

855
00:43:28,719 --> 00:43:32,080
Speaker 5: doing that, it's it's not it's like out of sync

856
00:43:32,080 --> 00:43:33,880
Speaker 5: with how you think women should exist. I guess I'll say,

857
00:43:33,880 --> 00:43:35,040
Speaker 5: I I don't know if that makes sense.

858
00:43:35,640 --> 00:43:38,400
Speaker 6: No, yeah, it absolutely does it kind of again, it

859
00:43:38,440 --> 00:43:41,560
Speaker 6: fades into this narrative that women are supposed to be

860
00:43:41,640 --> 00:43:46,719
Speaker 6: this higher level of beauty standard in order to fit

861
00:43:46,800 --> 00:43:49,279
Speaker 6: with a norm which is so much less work for

862
00:43:49,400 --> 00:43:51,400
Speaker 6: men in general.

863
00:43:51,640 --> 00:43:55,960
Speaker 2: Yeah, and when we're looking at like tech, I mean,

864
00:43:56,000 --> 00:43:58,680
Speaker 2: so many of the things that we've talked about on here,

865
00:43:58,840 --> 00:44:01,640
Speaker 2: do you play into that, whether it's filters or social media,

866
00:44:01,719 --> 00:44:06,080
Speaker 2: of these kind of beauty standards that are getting perpetuated.

867
00:44:06,800 --> 00:44:12,920
Speaker 2: Just the vitriol women can receive just by here's my

868
00:44:13,040 --> 00:44:20,800
Speaker 2: face on social media. So it is really unfortunate because

869
00:44:22,000 --> 00:44:25,400
Speaker 2: it's it's we can't ignore that that does happen. And

870
00:44:25,480 --> 00:44:27,759
Speaker 2: I know I've told the story before, but I have

871
00:44:27,800 --> 00:44:31,160
Speaker 2: a lovely collage of all of the horrible things people

872
00:44:31,200 --> 00:44:32,240
Speaker 2: have said to me online.

873
00:44:32,960 --> 00:44:38,120
Speaker 1: But it's also like, why can't we just these are

874
00:44:38,200 --> 00:44:42,040
Speaker 1: women who are doing amazing things in AI. We don't

875
00:44:42,120 --> 00:44:44,640
Speaker 1: have to talk about how they look. We really don't.

876
00:44:45,360 --> 00:44:46,160
Speaker 1: We really don't.

877
00:44:46,560 --> 00:44:48,279
Speaker 6: We don't need to know how many children she has.

878
00:44:48,360 --> 00:44:50,200
Speaker 6: We don't need to know she has a husband or

879
00:44:50,200 --> 00:44:52,719
Speaker 6: a partner, Like, that's not we want to know. Are

880
00:44:52,760 --> 00:44:56,160
Speaker 6: you doing better AI that protects people? Yeah, wonderful, let's

881
00:44:56,160 --> 00:44:56,799
Speaker 6: support that.

882
00:44:57,040 --> 00:44:58,640
Speaker 3: Yes, and it's such a yeah.

883
00:44:58,680 --> 00:45:01,880
Speaker 5: It's just such a missed app community because like the

884
00:45:01,960 --> 00:45:06,360
Speaker 5: majority of people who are making sure that AI is

885
00:45:06,480 --> 00:45:09,840
Speaker 5: safe or more inclusive, or doesn't harm people, or is

886
00:45:09,880 --> 00:45:13,719
Speaker 5: ethical or doing really interesting work, those people tend to

887
00:45:13,800 --> 00:45:16,880
Speaker 5: also be women, people of color, people who are not

888
00:45:17,040 --> 00:45:21,239
Speaker 5: necessarily treated as the norm in tech. And so when

889
00:45:21,280 --> 00:45:24,360
Speaker 5: you have an opportunity to talk to these people, actually

890
00:45:24,520 --> 00:45:28,520
Speaker 5: use it because what they are working on probably really matters,

891
00:45:28,560 --> 00:45:30,160
Speaker 5: and like it matters to all of us. Even if

892
00:45:30,200 --> 00:45:32,800
Speaker 5: you're not somebody who is a kateechie, you're not somebody

893
00:45:32,840 --> 00:45:36,080
Speaker 5: who thinks of yourself as like somebody who thinks about

894
00:45:36,080 --> 00:45:38,960
Speaker 5: AI a lot, this stuff is going to matter to everybody.

895
00:45:39,000 --> 00:45:41,000
Speaker 5: And I guess that brings me to one of the

896
00:45:41,040 --> 00:45:43,759
Speaker 5: things that I'm looking forward to in twenty twenty four

897
00:45:43,920 --> 00:45:47,399
Speaker 5: that I if I'm the girl in the cartoon, I've

898
00:45:47,440 --> 00:45:50,160
Speaker 5: got this in my bag slung over my shoulder, that

899
00:45:50,200 --> 00:45:52,239
Speaker 5: I'm taking it with me to the next year, and

900
00:45:52,280 --> 00:45:54,879
Speaker 5: that is all of us, each and every one of us,

901
00:45:55,280 --> 00:45:59,000
Speaker 5: being more involved in conversations around tech. Like I do

902
00:45:59,040 --> 00:46:02,640
Speaker 5: think I have seen a shift this year around how

903
00:46:02,719 --> 00:46:06,040
Speaker 5: regular people like you and me and people listening, you know,

904
00:46:06,360 --> 00:46:09,719
Speaker 5: are thinking about technology and talking about technology. I think

905
00:46:09,760 --> 00:46:13,040
Speaker 5: that we are done with this idea that tech is

906
00:46:13,120 --> 00:46:16,280
Speaker 5: only decided by a bunch of like super smart, genius

907
00:46:16,320 --> 00:46:18,440
Speaker 5: white guys who don't have to be accountable to us

908
00:46:18,440 --> 00:46:21,360
Speaker 5: at all because we're like not smart enough to understand

909
00:46:21,440 --> 00:46:22,560
Speaker 5: the brilliance that they do.

910
00:46:23,000 --> 00:46:23,640
Speaker 3: That is out.

911
00:46:23,960 --> 00:46:25,839
Speaker 5: I think that in twenty twenty three, we are coming

912
00:46:25,880 --> 00:46:28,600
Speaker 5: to the realization that these people have been using that

913
00:46:28,719 --> 00:46:31,480
Speaker 5: dynamic as a way to essentially like get rich off

914
00:46:31,480 --> 00:46:33,919
Speaker 5: of us. And I think that we're going to start

915
00:46:33,960 --> 00:46:36,560
Speaker 5: asking some questions about that dynamic and pushing back, like

916
00:46:36,920 --> 00:46:39,360
Speaker 5: should these companies and tech leaders be able to just

917
00:46:39,400 --> 00:46:42,640
Speaker 5: get rich off of us without us asking any questions

918
00:46:42,719 --> 00:46:45,560
Speaker 5: or having any say I think I think I'm starting

919
00:46:45,560 --> 00:46:48,719
Speaker 5: to see people be like, no, actually, I am smart

920
00:46:48,840 --> 00:46:51,520
Speaker 5: enough to understand that I'm being taken advantage of it exploited,

921
00:46:51,800 --> 00:46:53,160
Speaker 5: and I have questions about that.

922
00:46:53,320 --> 00:46:55,560
Speaker 3: So let's keep asking those questions.

923
00:46:55,760 --> 00:46:58,840
Speaker 5: In twenty twenty four, let's bring that dynamic into the

924
00:46:58,840 --> 00:46:59,840
Speaker 5: new year with us.

925
00:47:00,160 --> 00:47:06,160
Speaker 2: Yes, yes, and of course always your show is such

926
00:47:06,200 --> 00:47:08,920
Speaker 2: a good part of that and part of that conversation,

927
00:47:09,239 --> 00:47:14,680
Speaker 2: so very eagerly awaiting the next the next season. Do

928
00:47:14,719 --> 00:47:16,640
Speaker 2: you call it seasons, Bridget, We call.

929
00:47:16,520 --> 00:47:19,360
Speaker 5: It seasons, but they're really just like when I get

930
00:47:19,760 --> 00:47:21,440
Speaker 5: tired of making the show and I have to take

931
00:47:21,480 --> 00:47:21,839
Speaker 5: a break.

932
00:47:21,880 --> 00:47:24,439
Speaker 3: So we are taking a break, but we'll be back

933
00:47:24,480 --> 00:47:24,960
Speaker 3: real soon.

934
00:47:26,280 --> 00:47:29,760
Speaker 6: Well, you're here with us, answering those questions and asking

935
00:47:29,840 --> 00:47:33,040
Speaker 6: those questions and allowing us to ask you those questions.

936
00:47:33,080 --> 00:47:35,759
Speaker 4: So we are very grateful for that and excited for that.

937
00:47:35,800 --> 00:47:36,480
Speaker 4: For the new year.

938
00:47:37,160 --> 00:47:40,759
Speaker 2: Yes, yes, and we are hoping maybe we'll get to

939
00:47:41,000 --> 00:47:45,200
Speaker 2: hang out RL and do some things four one day.

940
00:47:45,640 --> 00:47:49,879
Speaker 3: One day. It's going to happen soon. Stay yes tune folks.

941
00:47:50,239 --> 00:47:54,600
Speaker 2: Yes, well, Bridget, thank you so much as always for

942
00:47:54,640 --> 00:47:56,439
Speaker 2: being here at the end of this year.

943
00:47:57,800 --> 00:48:00,160
Speaker 1: Where can the good listeners find you.

944
00:48:00,120 --> 00:48:02,160
Speaker 5: Out my podcast? There are no girls on the internet

945
00:48:02,160 --> 00:48:04,800
Speaker 5: on iHeartRadio. You can find me on Instagram at Bridget

946
00:48:04,800 --> 00:48:08,200
Speaker 5: Marie and DC or on Twitter at Bridget Marie.

947
00:48:08,640 --> 00:48:12,680
Speaker 2: Yes, and definitely go do that listeners if you haven't already, Bridget,

948
00:48:12,800 --> 00:48:17,160
Speaker 2: I hope you have a good relaxing holiday, weirdo Christmas.

949
00:48:17,440 --> 00:48:21,320
Speaker 5: Yes, thank you all, Happy Mary whatever to all of y'all.

950
00:48:21,680 --> 00:48:26,640
Speaker 2: Yes, thank you, and listeners, if you would like to

951
00:48:26,680 --> 00:48:29,240
Speaker 2: contact us, you can. Our email is Stephanie and Momstuff

952
00:48:29,239 --> 00:48:31,319
Speaker 2: at iHeartMedia dot com. You can find us on Twitter

953
00:48:31,360 --> 00:48:33,319
Speaker 2: at mom Stuff podcast, or on Instagram and TikTok at

954
00:48:33,320 --> 00:48:35,359
Speaker 2: stuff When Never told You. We have a tea public store,

955
00:48:35,400 --> 00:48:37,799
Speaker 2: and we do have a book. Thanks as always to

956
00:48:37,960 --> 00:48:40,880
Speaker 2: our super producer Christina, our executi producer Maya, and our

957
00:48:40,920 --> 00:48:41,800
Speaker 2: contributor Joey.

958
00:48:42,160 --> 00:48:43,680
Speaker 4: Thank you and happy holidays.

959
00:48:43,880 --> 00:48:46,880
Speaker 2: Yes yes, and thanks to you for listening Stuff I

960
00:48:46,960 --> 00:48:49,120
Speaker 2: Never told You the prediction of iHeartRadio. For more podcasts

961
00:48:49,120 --> 00:48:50,960
Speaker 2: from my Heart Radio, you can check out the iHeartRadio app,

962
00:48:50,960 --> 00:49:02,080
Speaker 2: Apple Podcasts, or where you listen to your favorite shows.

963
00:49:02,480 --> 00:49:02,640
Speaker 6: Ya