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Speaker 1: Hey, this is Sanny and Samantha.

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Speaker 2: I'm welcome to Steffan never told you a production of

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Speaker 2: iHeart Ratio, and today we are once again so happy

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Speaker 2: to be joined by the incredible, the irreplaceable Bridget Todd.

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Speaker 2: Welcome Bridget, thanks for having me so lovely to be back. Yes,

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Speaker 2: thank you so much for being back. You and I

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Speaker 2: were chatting about how stressful these times can be, so

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Speaker 2: the holidays, those times being so thank you for making

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Speaker 2: the time. We always love talking to you. How are you, Brigid,

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Speaker 2: How have you been?

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Speaker 3: I'm not bad. We were talking off Mike about how

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Speaker 3: it is a known issue with me.

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Speaker 4: I'm a grinch. I don't like the holidays. When people ask,

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Speaker 4: you know, this time of year, people are like, what.

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Speaker 3: Are you doing for the holiday? I like my fur.

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Speaker 3: I can feel my fur go up. You know, I

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Speaker 3: just don't like it.

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Speaker 4: My heart beats a little bit faster, and I just

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Speaker 4: I just find the holiday season extremely stressful. And especially

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Speaker 4: when you're someone who is self employed, it's like, hey,

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Speaker 4: you know the work that you usually do, What if

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Speaker 4: you did that work plus a week's worth of work

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Speaker 4: on top of it, and then that'll buy you a

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Speaker 4: week off what will you be doing with that time?

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Speaker 4: Going to see your family? Notably not stressful activity. It's

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Speaker 4: like I just find it like a stress sandwich. Essentially,

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Speaker 4: I can't handle it. I cannot hang with the holidays.

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Speaker 2: Well, as I said, Samantha, you and I would agree

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Speaker 2: on this. I mean I also think they're very stressful.

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Speaker 2: I yesterday we had a we went to the office

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Speaker 2: for a Sminty team meeting. We go, like Samantha and

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Speaker 2: I go once a year, and our our producer Christina was.

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Speaker 3: Like, how are you? And I was like, I'm so

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Speaker 3: tired just a deep sigh. Yeah.

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Speaker 4: And then it's the end of the year. It just

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Speaker 4: I just always feel like it's like everything catching up

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Speaker 4: with you all of the year. It's been a heavy year.

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Speaker 4: It's something about like by the time it hits December,

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Speaker 4: I'm just so freaking done, just done, spent.

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Speaker 2: Yeah, and then you have the new year barreling in

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Speaker 2: on you and you're like, well, it's still gonna be here,

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Speaker 2: but as all these expectations of maybe something will change

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Speaker 2: and not really Yeah, the.

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Speaker 4: New year I'm actually okay with because you get to

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Speaker 4: have those couple of days where you buy a new

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Speaker 4: journal and some new pens and if you for a

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Speaker 4: brief moment you're like, this is gonna be my year,

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Speaker 4: this is the year, then it all I get it

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Speaker 4: all together. It doesn't usually last for me. I usually

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Speaker 4: get about a week out of that. But you get

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Speaker 4: that week, which is not bad. And you and Smanita

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Speaker 4: did Bondo.

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Speaker 1: For last time.

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Speaker 5: Oh yeah, I got a new set. I was gifted

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Speaker 5: a new set, Bridget, just to give you an.

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Speaker 3: Update, and how are they writing.

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Speaker 5: I had to get used to it because it was

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Speaker 5: a lot free flowing, a lot more free flowing than

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Speaker 5: my previous one. So I had to practice with them

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Speaker 5: so I didn't mess up my tiny coloring books.

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Speaker 4: A good set of pans, a good set of markers.

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Speaker 4: It really will have you believing this is going to

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Speaker 4: turn everything around.

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Speaker 5: It's for a split second I was like, I can

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Speaker 5: color within the lines, and then I was like, no,

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Speaker 5: I cannot.

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Speaker 2: Well, the topic you've bought today for us, Bridget is

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Speaker 2: so so timely. I've been thinking about this because I mean,

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Speaker 2: it's hard to escape. But also you get to the

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Speaker 2: end of the year and you have, like Miriam Webster

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Speaker 2: chooses their word of the year and it's slop as

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Speaker 2: an AI slop and then you have time chooses their

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Speaker 2: persons a year and it's like the architects of AI

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Speaker 2: and it's like all dudes. And yesterday when we were

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Speaker 2: at the office, we were having kind of a team

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Speaker 2: meeting and it came up we were talking about AI

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Speaker 2: and our jobs and what does that look like?

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Speaker 1: So what are we talking about specifically today, bridget.

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Speaker 4: Today you're talking about the reality that women are using

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Speaker 4: AI tools a lot less than men in the workplace.

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Speaker 4: There's a definite, well studied, well documented gender gap when

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Speaker 4: it comes to the usage of AI. And I feel

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Speaker 4: you because I think as a podcaster especially, but any

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Speaker 4: kind of creative the first conversation that comes up when

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Speaker 4: it's a bunch of podcasters together is are you using

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Speaker 4: AI and your work? How are you seeing AI show up?

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Speaker 4: Or people asking are you afraid of AI? Do you

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Speaker 4: see AI taking your job?

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Speaker 3: One day? Like you won't have a job.

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Speaker 4: People will be listening to AI podcasts all of that, right,

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Speaker 4: Those conversations are everywhere, but and I think often time

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Speaker 4: those conversations can be grounded in a kind of hype.

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Speaker 4: And so I think the the act that AI is

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Speaker 4: not being adopted equally by people of all genders at

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Speaker 4: the same rates.

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Speaker 3: Is pretty interesting.

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Speaker 4: So I wanted to talk through what we know about

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Speaker 4: the data when it comes to the gender gap in AI.

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Speaker 4: But I think I might be coming at this from

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Speaker 4: a little bit of a different place, right. You know,

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Speaker 4: in a lot of the research, they automatically assume two

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Speaker 4: points that I want to call out early that I'll

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Speaker 4: return to one, women adopting AI less is going to

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Speaker 4: be bad for women. I'll talk about this later, but

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Speaker 4: I don't know about treating that like a foregone conclusion.

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Speaker 4: And then this is kind of an offshoot of something

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Speaker 4: that I hear a ton when talking about AI. AI

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Speaker 4: is inevitable. If you don't get with the program, you're

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Speaker 4: going to be left behind. I have people who tell

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Speaker 4: me that not being into AI right now is like

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Speaker 4: not using email. You know, it will be the same

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Speaker 4: kind of thing in a few years, and you know,

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Speaker 4: if women are adopting it less, the idea that we're

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Speaker 4: going to be less employable, less competitive, making less money overall.

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Speaker 4: And so I want to be clear that I don't

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Speaker 4: really have the answer here, but and I want to

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Speaker 4: look at what the research says, but I don't like

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Speaker 4: this idea that frames widespread AI adoption as an inevitable

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Speaker 4: thing that everyone is going to be doing, because it

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Speaker 4: really doesn't leave a lot of room for criticism away

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Speaker 4: around the way that AI is being adopted. It's just

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Speaker 4: repeating it's inevitable, it's inevitable, it's inevitable, get on board.

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Speaker 4: I do think that when it comes to adopting any

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Speaker 4: new technology in this way, we shouldn't just be being

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Speaker 4: told to get on board it's inevitable, just fram it

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Speaker 4: down people's throats, especially when people are saying, hey, maybe

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Speaker 4: we don't actually like this technology.

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Speaker 3: Hey, maybe this technology isn't good.

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Speaker 2: Right.

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Speaker 4: I love technology. I'm an advocate for knowing the basics

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Speaker 4: of all kinds of technology. But if a technology is

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Speaker 4: showing itself to be ineffective, biased, problematic, full of lies,

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Speaker 4: actually adds work to your plate. I don't know that

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Speaker 4: we should be just to be telling people get on

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Speaker 4: board with that it's inevitable if that's actually what they're experiencing,

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Speaker 4: you know.

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Speaker 2: Yes, yes, And this has come up a lot at

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Speaker 2: our office of the at least right now, it seems

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Speaker 2: to be adding more work. It's not like actually helping,

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Speaker 2: but that you're told like, no, it's going to really

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Speaker 2: help you out and streamline things. I have to say,

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Speaker 2: I had not considered this gender gap in AI usage.

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Speaker 2: So I'm really interested about the research.

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Speaker 1: That you found.

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Speaker 3: Yeah, let's get into it.

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Speaker 4: So I took a look at some of the studies

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Speaker 4: and they all basically confirm that women are using AI

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Speaker 4: in the workplace a lot less than our mail counterparts.

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Speaker 4: Let's look at a meta analysis called Global Evidence on

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Speaker 4: Gender Gaps in Generative AI published in the Harvard Business Review.

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Speaker 4: So they took a look at eighteen surveys and studies

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Speaker 4: covering one hundred and forty three thousand plus individuals across

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Speaker 4: many countries, sectors, and occupations. They also combined this survey

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Speaker 4: data with Internet traffic and mobile app download data for

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Speaker 4: major generative AI platforms like Quad and chackbut platforms that

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Speaker 4: have one hundred of millions of users. They found a large,

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Speaker 4: nearly universal gender gap. Here's what they found. They found

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Speaker 4: that the gender gap in generative AI usage is global, persistent,

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Speaker 4: and not fully explained by access or occupation, pointing to

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Speaker 4: some deeper social, cultural, and institutional frictions.

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Speaker 3: So overall, it just seems like women.

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Speaker 4: Are not the ones when when people are saying it's inevitable,

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Speaker 4: get on board, you got to use it.

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Speaker 3: It sounds like, by and large that is not coming

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Speaker 3: from women.

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Speaker 4: I'm curious, do these findings surprise you, like as women

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Speaker 4: in media, as women in tech and podcasting, Like, is

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Speaker 4: this surprising information for me?

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Speaker 1: It's not.

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Speaker 2: I don't think i'd ever clocked it until I was

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Speaker 2: looking at your outline. But once I looked at the outline,

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Speaker 2: I was like, yeah, yeah, because most people I know

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Speaker 2: who are into AI are men, and they usually have

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Speaker 2: a very like but it's this global technology thing about it,

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Speaker 2: and I can't say I have really anyone I can

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Speaker 2: think of, but as a woman, that's like that. And

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Speaker 2: I mean, we're going to go over some of the

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Speaker 2: reasons why this is, and I thought a lot of

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Speaker 2: them were interesting, but they did resonate with me, and

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Speaker 2: I thought like, yeah, that makes sense, Like I get

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Speaker 2: I can't why there's a hesitance, but I guess I

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Speaker 2: hadn't until I read this, I hadn't picked up on it,

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Speaker 2: but I do.

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Speaker 1: It resonates with me now, and I'm like, yeah, that

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Speaker 1: seems right, that seems right.

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Speaker 5: I think being on like the ex lineal community. That's

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Speaker 5: kind of one of those things that I don't think about.

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Speaker 5: But my partner, who was heavily, like even employed into

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Speaker 5: the industry of AI, the difference between him and I

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Speaker 5: are so vast that it's not surprising the way that

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Speaker 5: he has integrated that into his systems, into his life,

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Speaker 5: but also understands how he has to be cautious and

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Speaker 5: how it is one of those things that has to

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Speaker 5: be operated with responsibility. I think that there's a level

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Speaker 5: of understanding, but he is also very very excited by

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Speaker 5: the possibilities of what AI could do. So there's this

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Speaker 5: level like in our in my life specifically now our

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Speaker 5: responses to AI as me being a little more like

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Speaker 5: dismissive and very much more like negative about the whole

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Speaker 5: thing as where he is trying to play it to

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Speaker 5: his cards. That makes sense.

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Speaker 4: That makes so much sense, And it's funny because you

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Speaker 4: are basically living a microchasm of what the data suggests.

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Speaker 3: And that's kind of what it feels like when I'm

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Speaker 3: like thinking about it.

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Speaker 5: I'm like, honestly, i feel like I'm living as the

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Speaker 5: stereotype that is just or like in this conversation.

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Speaker 4: Absolutely so in this meta analysis, they found that across

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Speaker 4: almost all the data sets they looked at, women are

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Speaker 4: about twenty to twenty five percent less likely than men

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Speaker 4: to be using generative AI. The meta analysis shows women

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Speaker 4: have twenty two percent lower odds of using AI than men.

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Speaker 4: The gap appears across regions, industries, education levels, and occupations.

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Speaker 4: So you might hear that and think, okay, but maybe

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Speaker 4: the people who did this these surveys got it wrong,

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Speaker 4: looked at the wrong information. That gap, however, cannot be

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Speaker 4: explained by things like survey bias, because they also, as

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Speaker 4: they said, looked at who is downloading tools like Claud

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Speaker 4: and chat sheebut, and that data showed that women compromise

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Speaker 4: about forty two percent of chatjept website users globally, twenty

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Speaker 4: seven percent of chat sheepet mobile app users, and even

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Speaker 4: lower shares of women are using the tool Claud for manthropic.

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Speaker 4: So this really suggests that the gender gap is at

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Speaker 4: a pretty massive global scale.

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Speaker 3: A couple of notable findings. If women are more senior

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Speaker 3: or have more experience.

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Speaker 4: It does narrow that gender gap a little bit, but

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Speaker 4: it does not completely eliminate that gap. Senior women in

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Speaker 4: technical roles sometimes match or exceed men's usage, but also

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Speaker 4: junior women and women in non technical roles show much

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Speaker 4: larger gaps. So there are a couple of interesting, notable

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Speaker 4: outliers there, But in general, just like what's happening in

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Speaker 4: Samantha's household, men are even cautiously sort of gung ho,

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Speaker 4: experimenting with it, thinking pretty optimistically about what AI might be.

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Speaker 3: Able to do. Women are a little more skeptical.

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Speaker 1: We'll say. I wonder.

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Speaker 2: Just because we've been talking a lot on the show

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Speaker 2: about like the quote male loneliness epidemic, which women are

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Speaker 2: also very lonely, but that's what we're talking about. But

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Speaker 2: I see a lot of stories of men like forming

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Speaker 2: these bonds with AI and these relationships with AI, and

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Speaker 2: I have no idea, but I wonder if that's also

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Speaker 2: part of it, because women usually have more of a

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Speaker 2: support group than this.

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Speaker 3: Yeah, yeah, I mean, I absolutely think that.

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Speaker 4: Some of the way that AI is talked about by

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Speaker 4: people who make it, some of the marketing language and

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Speaker 4: decisions around it, I think reinforce some attitudes that I

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Speaker 4: think are are kind of sexist.

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Speaker 3: Right.

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Speaker 4: I remember very clearly it was like a grand opening

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Speaker 4: when sam Altman the CEO of open Ai, was introducing

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Speaker 4: this new vocal chatbot for open AI's chatbot named Sky,

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Speaker 4: and he basically said that he wanted the experience to

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Speaker 4: be like the experience that people had in the movie Her,

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Speaker 4: which is Folks seventeen. That movie Looking Phoenix is basically

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Speaker 4: having a romantic and sexual relationship with an AI chatbot

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Speaker 4: voice by Scarlett Johanson.

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Speaker 3: And I love that movie. It's one of my favorite movies.

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Speaker 3: But obviously, within the framing of.

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Speaker 4: The movie, it is about a man you and technology

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Speaker 4: to satisfy his emotional and sexual needs on top of

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Speaker 4: his you know, admin that needs that you might be

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Speaker 4: using AI for already. And so when the head of

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Speaker 4: open Ai gets on a stage and promises people that

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Speaker 4: they will be able to use technology the same way

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Speaker 4: it is used in this movie, well, I can understand

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Speaker 4: why that might hit women in a kind of a

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Speaker 4: weird way, right, I can understand why it's like, well,

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Speaker 4: what are you really saying about this technology that that

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Speaker 4: is your frame of reference for how you envision people

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Speaker 4: using it?

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Speaker 3: Do you know what I mean?

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Speaker 2: Yes, yes, absolutely, And going back to one of the

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Speaker 2: other points he made at the beginning that comes up

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Speaker 2: in these arguments of why women aren't using AI as much.

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Speaker 2: It did kind of remind me of the argument around STEM,

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Speaker 2: like a lot of STEM things, where it's like, well

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Speaker 2: they just that's not how their brain works, or that

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Speaker 2: they don't pursue those films because of whatever X y Z.

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Speaker 2: So that feels very similar to me.

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Speaker 4: Oh yes, I'm so glad that you brought that up,

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Speaker 4: because when you look at how the other conversations around

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Speaker 4: gender gaps that we know exist in workplaces more broadly,

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Speaker 4: in leadership roles more broadly, stam rolls more broadly. Often,

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Speaker 4: just as you said, the pushback is that women maybe

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Speaker 4: are picking less lucrative.

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Speaker 3: Work or fields.

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Speaker 4: If you're Larry Summers, maybe it's the idea that women

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Speaker 4: are just innately less good at these fields or something. Now,

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Speaker 4: never mind that that really discounts the question of why

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Speaker 4: fields that are associated with women are historically devalued and underpaid.

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Speaker 4: So even if you know there was a time where

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Speaker 4: computing was seen as women's administrative or secretarial work. When

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Speaker 4: women were associated with it, it was lower paid ad

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Speaker 4: men work. When men became associated with it, very clearly,

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Speaker 4: it became a different kind of work. So that's clearly

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Speaker 4: not the women's fault for you know, for what roles

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Speaker 4: they happen to be choosing. But in any event, not

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Speaker 4: only is that not what's really going on and not

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Speaker 4: telling the full story of what's happening in these workplaces,

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Speaker 4: nor is it really what's going on or telling the

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Speaker 4: full story of AI adoption. That's not me saying this,

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Speaker 4: that's according to this meta analysis, as social scientists Katie

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Speaker 4: Jin puts it in a piece called There's a reason

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Speaker 4: why women aren't swooning over AI like men are, which

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Speaker 4: I love that title, She writes, The off proposed explanation

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Speaker 4: is that women understand this new technology less, largely because

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Speaker 4: they work in roles with lower exposure to it. Women are,

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Speaker 4: after all, still outnumbered in STEM degrees and careers, including

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Speaker 4: AI specific roles. The same is true in AI leadership,

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Speaker 4: women hold fewer than fourteen percent of senior executive positions

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Speaker 4: in the industry. But Harvard's study also found that the

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Speaker 4: usage gap remains even when women are explicitly given opportunities

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Speaker 4: to learn and use AI roles. So it's not just

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Speaker 4: that women have less training or less access to AI.

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Speaker 4: Even when the people who did this meta analysis equalized

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Speaker 4: for that, they still found that this AI gender gap persist.

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Speaker 4: They looked at a study out of Kenya where women

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Speaker 4: were exuplicitly offered training and access to AI, yet those

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Speaker 4: women still used chat GPT thirteen percent less than men.

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Speaker 3: So this seems to suggest.

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Speaker 4: These barriers aren't really about access or awareness about AI,

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Speaker 4: it's something else going on.

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Speaker 5: I feel like there's so many things as I'm trying

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Speaker 5: to work out when it comes to this conversation about

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Speaker 5: whys that seem like, well, obviously A equals B, or

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Speaker 5: A is because of B. And one of the things

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Speaker 5: that I'm thinking about is like maybe just what I'm

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Speaker 5: seeing when it comes to things like chat gpt, which

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Speaker 5: feels like just an advanced version of Google where we

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Speaker 5: made that that the thing in researching, But it gives

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Speaker 5: the mediocre man who has a lot more confidence than

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Speaker 5: women in general, even more confidence, I guess in a

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Speaker 5: way because they think they're getting that answer without having

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Speaker 5: to go to the people who actually found those answers

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Speaker 5: in some weird ways, Like there's so many thoughts like

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Speaker 5: maybe what are some of these reasons that we are

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Speaker 5: finding that men are more likely to use it and

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Speaker 5: not for me like, that just seems like a rational

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Speaker 5: I think that's true.

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Speaker 4: I think twice now, I've gotten into a back and

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Speaker 4: forth with somebody on Reddit, and I guess I don't

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Speaker 4: know their gender, but I can tell that they were

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Speaker 4: responding to me using chat sheep put and I'm like, Oh,

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Speaker 4: you didn't.

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Speaker 3: You couldn't. It wasn't worth it.

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Speaker 4: You just wanted to keep the like argument going, but

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Speaker 4: it wasn't worth it for you to come up with

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Speaker 4: your own answer. We're going to take the chat sepeute

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Speaker 4: really right. So the meta analysis gave some answers as

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Speaker 4: to what might be going on here, what might explain

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Speaker 4: some of these gaps. I'll tell y'all what they said,

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Speaker 4: and then i'll kind of give you my thoughts on it,

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Speaker 4: because I'm not some of these I'm not so sure about.

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Speaker 4: So the first, it's a lower familiarity and knowledge with AI,

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Speaker 4: So they said women are more likely to report not

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Speaker 4: knowing how generative AI works or.

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Speaker 3: How to use it.

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Speaker 4: I'm not totally sold on this. You know it is

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Speaker 4: in their meta analysis, so I wanted to include it.

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Speaker 4: But this is just my tabe. AI is not difficult

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Speaker 4: to understand. It is not difficult to use, nor is

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Speaker 4: it terribly complex. There is nobody listening to the sound

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Speaker 4: of my voice who could not figure it out who

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Speaker 4: it is outside of the realm of their ability to

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Speaker 4: comprehend it is not terribly complex. I think that what

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Speaker 4: this study is actually highlighting here is I think this

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Speaker 4: tendency for the people who make technology and talk about

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Speaker 4: technology to do so in a way where it seems

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Speaker 4: like the technology they're talking about is so complex and

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Speaker 4: your puty little lady brains could never figure out what

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Speaker 4: we're talking about here. Think about the way that people

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Speaker 4: like Elon Musk, Mark Zuckerberg, Sam Altman talk about technology.

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Speaker 4: They talk about it in this way where it gives

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Speaker 4: it this gravity toss that I think is oftentimes unearned,

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Speaker 4: because you don't need any kind of special training to

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Speaker 4: understand the basics of AI. And so I think the

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Speaker 4: fact that tech leaders, who, let's be real, often happen

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Speaker 4: to be not just me, but a specific kind of man,

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Speaker 4: not just white men, but a specific kind of white guy.

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Speaker 4: I think the fact that they are consistently talking about

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Speaker 4: this technology in a way that makes it seem like

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Speaker 4: you're ordinary person could never understand it. I think it's

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Speaker 4: sort of at play here, and I think that is

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Speaker 4: with intention. I think they do this because they want

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Speaker 4: people to feel I couldn't possibly understand what they're talking about.

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Speaker 4: How could I hold these people accountable? How could I

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Speaker 4: expect things to be better for me? How could I

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Speaker 4: even you know, know, you know, Oftentimes they are telling people, oh, no, no,

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Speaker 4: you want AI.

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Speaker 3: AI is good. People will say, no, I know what

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Speaker 3: I like, and I know.

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Speaker 4: What my experiences are, and I know that I'm not enjoying,

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Speaker 4: you know, opening my email, not even be able to

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Speaker 4: use it because AI tools are being foisted on me

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Speaker 4: non consensually, and they're like, no, no, no, it's actually

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Speaker 4: great for you. Right that if people truly innately felt

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Speaker 4: like they understood this technology, they would tech leaders wouldn't

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Speaker 4: be able to do that anymore.

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Speaker 5: Right. It really does feel like they're just inflating the

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Speaker 5: importance of it so that we'll believe it, and then

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Speaker 5: that when we are forced to use it because we

404
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Speaker 5: have no way of pushing back and there's actually no

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Speaker 5: regulation anymore on how it's being used, that will just

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Speaker 5: accept how great it is without actually knowing how great

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Speaker 5: it is. In the midst of the conversation of knowing

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Speaker 5: how inaccurate it has been in the past. Also, how

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Speaker 5: hard is it when you literally do not have a

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Speaker 5: choice if you use what used to be Google and

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Speaker 5: what you thought would be something that would give you

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Speaker 5: trusted sources, and the top of it is literally AI

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Speaker 5: results that you did not ask for. So how hard

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Speaker 5: is it to use when you're not given a choice

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Speaker 5: to whether or not you have to use it?

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Speaker 3: Right? And this is what I'm saying.

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Speaker 4: It goes back to what I was when I started

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Speaker 4: the conversation with of how we talk about it as

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Speaker 4: this inevitable technology and we all have to get on board.

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Speaker 4: Things that are good, you don't have to talk about

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Speaker 4: them that way. Things that people want you don't. That's

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Speaker 4: not how that's not how you have to frame them.

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Speaker 4: So Sam Wiles said on threads If AI is a

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Speaker 4: good thing, why are we constantly told it's inevitable. You

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Speaker 4: don't have to do that with good things. No one's

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Speaker 4: ever like iced coffee is inevitable, So you may as

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Speaker 4: well get used to it whole. This is so correct, right,

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Speaker 4: This is things that are good. You do not have

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Speaker 4: to continue over and over and over again to tell

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Speaker 4: people that they better get on board with this good thing, like, no,

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Speaker 4: that's not how we talk about things that people like

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Speaker 4: and are good.

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Speaker 1: Right.

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Speaker 5: Also, it's not forced upon us by the government, like

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Speaker 5: we are not the ice coffee being told you have

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Speaker 5: to buy ice coffee when it gets to this temperature,

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Speaker 5: and no state can regulate whether or not you can

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Speaker 5: use it, even though it can be harmful for you.

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Speaker 5: And we have seen harm done by this.

440
00:22:31,880 --> 00:22:34,600
Speaker 3: Sorry exactly that.

441
00:22:45,200 --> 00:22:49,200
Speaker 4: Another thing this meta analysis found was lower confidence with

442
00:22:49,320 --> 00:22:52,280
Speaker 4: it and sort of persistence or lower levels of persistence,

443
00:22:52,320 --> 00:22:55,040
Speaker 4: and so, according to the meta analysis, women report less

444
00:22:55,119 --> 00:22:58,560
Speaker 4: confidence in prompting and are less likely to persist after

445
00:22:58,720 --> 00:23:03,520
Speaker 4: poor AI outputs. Men, however, are more likely to experiment repeatedly.

446
00:23:04,160 --> 00:23:06,920
Speaker 4: So I think this is one of those things where

447
00:23:08,040 --> 00:23:10,640
Speaker 4: essentially what is being said there is women have tried

448
00:23:10,680 --> 00:23:13,880
Speaker 4: this technology and have identified that it can be janky

449
00:23:14,320 --> 00:23:17,000
Speaker 4: and have reasonably concluded that maybe it's not worked the

450
00:23:17,040 --> 00:23:18,960
Speaker 4: pain in the butt to keep trying with it, and

451
00:23:19,320 --> 00:23:20,960
Speaker 4: men are like, I'm going to keep on trying.

452
00:23:21,280 --> 00:23:28,159
Speaker 3: I think that it's like framing women taking like gleaning.

453
00:23:27,880 --> 00:23:31,359
Speaker 4: From their experiences of AI being full of vies hallucinations

454
00:23:31,440 --> 00:23:33,440
Speaker 4: actually being more work for them, which has been my

455
00:23:33,560 --> 00:23:35,520
Speaker 4: experience when I when I've tried to use AI a

456
00:23:35,560 --> 00:23:38,359
Speaker 4: lot of times, I'm like, Okay, well, I could have

457
00:23:38,400 --> 00:23:40,000
Speaker 4: done this in the time, in the time it took

458
00:23:40,040 --> 00:23:42,000
Speaker 4: me to get to finally get AI to do, I

459
00:23:42,040 --> 00:23:43,479
Speaker 4: could have done this myself and done a better job

460
00:23:43,480 --> 00:23:43,639
Speaker 4: at it.

461
00:23:43,640 --> 00:23:44,800
Speaker 3: So why should it is on that to begin with

462
00:23:45,080 --> 00:23:45,680
Speaker 3: wasted time?

463
00:23:46,000 --> 00:23:48,400
Speaker 4: But I don't like how this is framed as women

464
00:23:48,560 --> 00:23:52,200
Speaker 4: being risk averse, when in reality, I think it's more

465
00:23:52,280 --> 00:23:56,120
Speaker 4: like women making reasonable deductions about the time they want

466
00:23:56,200 --> 00:23:58,879
Speaker 4: to spend on something that has proved itself to be ineffective,

467
00:23:59,520 --> 00:24:01,000
Speaker 4: and like, that's that's not risk averse.

468
00:24:01,040 --> 00:24:01,760
Speaker 3: That's something else.

469
00:24:02,320 --> 00:24:05,680
Speaker 5: That's being tired of arguing with an inanimate object, which

470
00:24:05,680 --> 00:24:07,840
Speaker 5: is what I've had to do when I'm yelling back

471
00:24:08,200 --> 00:24:10,760
Speaker 5: at Google I want you Siri, but any of those

472
00:24:10,840 --> 00:24:12,720
Speaker 5: days because they did not understand what I was saying

473
00:24:12,920 --> 00:24:14,800
Speaker 5: and I just stare at it and like, why am

474
00:24:14,800 --> 00:24:17,240
Speaker 5: I doing this with you? You're and then being told

475
00:24:17,280 --> 00:24:20,440
Speaker 5: also they remember when you're reading them.

476
00:24:20,840 --> 00:24:23,480
Speaker 4: Listen, I told this story on my own podcast, there

477
00:24:23,480 --> 00:24:25,960
Speaker 4: are our goals on the Internet. After I did the

478
00:24:26,080 --> 00:24:29,399
Speaker 4: episode with you all about Larry Summers, who was formerly

479
00:24:29,520 --> 00:24:31,800
Speaker 4: on the board of Open AI, stepping down because of

480
00:24:31,880 --> 00:24:35,600
Speaker 4: those emails that were revealed between him and convicted child

481
00:24:35,680 --> 00:24:37,000
Speaker 4: sex criminal Jeffrey Epstein.

482
00:24:37,560 --> 00:24:38,840
Speaker 3: I was trying to get.

483
00:24:38,920 --> 00:24:40,760
Speaker 4: I was like, oh, maybe chatchepet can help me come

484
00:24:40,800 --> 00:24:42,720
Speaker 4: up with like metadata and all of that. So I

485
00:24:42,880 --> 00:24:45,920
Speaker 4: put in what I have to Chattebaut and Chattebet says

486
00:24:46,200 --> 00:24:50,240
Speaker 4: this is not correct. There is no link between Larry

487
00:24:50,320 --> 00:24:53,720
Speaker 4: Summers and Jeffrey Epstein. And I was like, oh, really, wow,

488
00:24:53,880 --> 00:24:56,840
Speaker 4: we don't have emails linking the two and they're like

489
00:24:56,960 --> 00:25:00,879
Speaker 4: and then Chattebet is like, Okay, well, yeah, maybe there's emails,

490
00:25:01,280 --> 00:25:05,040
Speaker 4: but you're suggesting that something illegal happened if you don't

491
00:25:05,040 --> 00:25:05,840
Speaker 4: know anything about that.

492
00:25:05,960 --> 00:25:08,439
Speaker 3: And I was like, I said the emails were creepy.

493
00:25:08,800 --> 00:25:11,360
Speaker 4: How would you define emails where a grown married man

494
00:25:11,840 --> 00:25:14,639
Speaker 4: is going to a convicted pedophile for advice on how

495
00:25:14,680 --> 00:25:16,000
Speaker 4: we can have text with his mentee.

496
00:25:16,840 --> 00:25:19,480
Speaker 3: You wouldn't define those as creepy. We got into a

497
00:25:19,600 --> 00:25:23,600
Speaker 3: huge argument to the point where I asked it. I

498
00:25:23,800 --> 00:25:28,399
Speaker 3: was like, cut the crap. Are you being so cagey

499
00:25:28,560 --> 00:25:30,520
Speaker 3: because Larry Summers used to be on the board of

500
00:25:30,600 --> 00:25:31,000
Speaker 3: open AI.

501
00:25:31,200 --> 00:25:36,399
Speaker 4: That makes you CHATCHYPT. CHATCHYPT says, let's be clear, Larry

502
00:25:36,440 --> 00:25:39,040
Speaker 4: Summers was never on the board of open AI. Larry

503
00:25:39,080 --> 00:25:40,680
Speaker 4: Summers has been on the board of open ai since

504
00:25:40,720 --> 00:25:42,240
Speaker 4: like twenty twenty three, so it's.

505
00:25:42,240 --> 00:25:43,040
Speaker 3: Been kind of a while.

506
00:25:43,200 --> 00:25:47,200
Speaker 4: So it's like, not only was that infuriating, think the

507
00:25:47,400 --> 00:25:50,320
Speaker 4: work it took for me to to like take stop

508
00:25:50,400 --> 00:25:53,440
Speaker 4: my work day and have an argument, a real no

509
00:25:54,119 --> 00:25:57,600
Speaker 4: argument with CHATCHPT. This is the technology they're saying is

510
00:25:57,640 --> 00:25:59,320
Speaker 4: the legiten of our king economy right now.

511
00:25:59,400 --> 00:25:59,879
Speaker 3: I don't think so.

512
00:26:00,040 --> 00:26:03,520
Speaker 5: Oh, you literally spent emotions with a computer who is

513
00:26:03,600 --> 00:26:06,080
Speaker 5: just trying to protect their reputation, which.

514
00:26:05,920 --> 00:26:07,600
Speaker 3: Is weird because they're not a human.

515
00:26:08,000 --> 00:26:11,280
Speaker 4: This is what I'm saying, Like, like, I'll tell you something,

516
00:26:11,400 --> 00:26:13,959
Speaker 4: I have not asked chat gpt a single thing since then.

517
00:26:14,240 --> 00:26:16,200
Speaker 3: That was a little experiment to see if it was

518
00:26:16,240 --> 00:26:16,680
Speaker 3: gonna work.

519
00:26:16,760 --> 00:26:19,480
Speaker 4: It went so astronomically left and I'm like, okay, well

520
00:26:19,520 --> 00:26:21,280
Speaker 4: that's a waste of my time and I got my

521
00:26:21,320 --> 00:26:23,520
Speaker 4: blood pressure up, so it's a double bad.

522
00:26:23,760 --> 00:26:25,320
Speaker 5: I don't I don't need this from you too.

523
00:26:26,080 --> 00:26:27,760
Speaker 4: If I'm gonna argue with someone, it's gonna be a

524
00:26:27,800 --> 00:26:30,400
Speaker 4: flesh and blood human in my life, that freaking chat GPT.

525
00:26:31,560 --> 00:26:33,280
Speaker 2: Well, I think that's also a good point of like

526
00:26:33,560 --> 00:26:36,520
Speaker 2: we've we've talked about this before several times, who makes

527
00:26:36,560 --> 00:26:38,840
Speaker 2: this technology and the fear of like the biases and

528
00:26:38,880 --> 00:26:41,479
Speaker 2: all that stuff in there. And I know, like Elon

529
00:26:41,600 --> 00:26:45,080
Speaker 2: Musk and Gronkipedia has had a lot of things come

530
00:26:45,200 --> 00:26:48,280
Speaker 2: up with what it is saying that are just flat

531
00:26:48,359 --> 00:26:51,159
Speaker 2: out ridiculous and like these flight out lies, and so

532
00:26:51,280 --> 00:26:56,680
Speaker 2: I do think that that is a concern, and I

533
00:26:57,040 --> 00:27:03,840
Speaker 2: would I would pause it that that influences like I

534
00:27:03,920 --> 00:27:06,320
Speaker 2: would guess that women are not getting the answers they

535
00:27:06,400 --> 00:27:09,320
Speaker 2: want more often than men are getting the answers that

536
00:27:09,440 --> 00:27:10,240
Speaker 2: they do want.

537
00:27:10,840 --> 00:27:13,600
Speaker 3: Yes, And something to remember about AI.

538
00:27:13,840 --> 00:27:16,359
Speaker 4: If there's one thing, even if you're thinking, I'm not

539
00:27:16,440 --> 00:27:18,080
Speaker 4: a teche, I'm not a computer person.

540
00:27:18,359 --> 00:27:20,920
Speaker 3: This is not my forte. If there's one thing to

541
00:27:21,000 --> 00:27:22,200
Speaker 3: remember is that AI.

542
00:27:22,600 --> 00:27:26,160
Speaker 4: It's easy to think of it as robot computer brains

543
00:27:26,200 --> 00:27:28,000
Speaker 4: that know everything's all knowing.

544
00:27:28,119 --> 00:27:28,399
Speaker 3: It's not.

545
00:27:28,600 --> 00:27:31,159
Speaker 4: It's trained and built by all of us humans and

546
00:27:31,240 --> 00:27:34,440
Speaker 4: so the same kind of biases that we know humans have,

547
00:27:35,040 --> 00:27:38,720
Speaker 4: AI is just trained on all of that to replicate it, right,

548
00:27:38,840 --> 00:27:41,560
Speaker 4: And so keep that in mind when you're thinking about AI.

549
00:27:41,680 --> 00:27:44,320
Speaker 4: And we have so much documentation about the ways that

550
00:27:44,400 --> 00:27:47,760
Speaker 4: AI is biased when it comes to reflecting women in workplaces.

551
00:27:47,840 --> 00:27:50,760
Speaker 4: It's more likely to reflect women as younger than they

552
00:27:50,840 --> 00:27:54,879
Speaker 4: actually are. And so if the majority of let's say,

553
00:27:54,920 --> 00:27:57,520
Speaker 4: for instance, if the majority of women who are engineers

554
00:27:57,680 --> 00:28:02,280
Speaker 4: are over forty, it's like a documented fact. AI will

555
00:28:02,440 --> 00:28:04,879
Speaker 4: will tell people, oh, they're actually younger than they are,

556
00:28:05,000 --> 00:28:08,880
Speaker 4: because it's reflecting a bias that against more mature women.

557
00:28:09,280 --> 00:28:12,400
Speaker 4: You know, there have been studies that when women ask

558
00:28:12,880 --> 00:28:16,400
Speaker 4: CHATGBT for negotiation advice, it regularly tells them to ask

559
00:28:16,480 --> 00:28:19,520
Speaker 4: for less money. Right, so all of the biases that

560
00:28:19,600 --> 00:28:22,520
Speaker 4: exist in society, AI is simply replicating those because it

561
00:28:22,600 --> 00:28:25,160
Speaker 4: was trained on all of us. And so really keep

562
00:28:25,240 --> 00:28:28,520
Speaker 4: that in mind, Like I can understand why women are

563
00:28:28,600 --> 00:28:32,320
Speaker 4: not keen about going to this technology to help them

564
00:28:32,520 --> 00:28:35,720
Speaker 4: understand the world around them and their workplace when we

565
00:28:35,960 --> 00:28:38,480
Speaker 4: know it has these biases.

566
00:28:38,680 --> 00:28:40,840
Speaker 5: Right, I mean it comes to the other point and

567
00:28:40,920 --> 00:28:44,880
Speaker 5: that we already understand and know that the data research

568
00:28:45,040 --> 00:28:48,160
Speaker 5: are based on men and there's not enough based on

569
00:28:48,280 --> 00:28:51,240
Speaker 5: women or those marginalized communities at all. Like we just

570
00:28:51,360 --> 00:28:53,400
Speaker 5: got to the point of realizing, hey, we need more,

571
00:28:53,760 --> 00:28:56,280
Speaker 5: so more people have been doing it, but all of

572
00:28:56,360 --> 00:29:00,240
Speaker 5: that information is not there to provide a background for chat,

573
00:29:00,320 --> 00:29:03,000
Speaker 5: GPT to pick up or whatever AI company. And that's

574
00:29:03,040 --> 00:29:06,320
Speaker 5: really concerning that we're acting like the information we know

575
00:29:06,400 --> 00:29:07,840
Speaker 5: now is all we need to know when we know

576
00:29:07,920 --> 00:29:09,280
Speaker 5: that's not true exactly.

577
00:29:09,720 --> 00:29:11,040
Speaker 3: That is such a good way to put it.

578
00:29:12,440 --> 00:29:16,800
Speaker 4: And another concern that women report about using AI why

579
00:29:16,840 --> 00:29:18,440
Speaker 4: they're kind of skeptical about it, is that women have

580
00:29:18,520 --> 00:29:21,920
Speaker 4: stronger what the meta analysis called kind of ethical concern.

581
00:29:22,040 --> 00:29:25,640
Speaker 4: So women are more likely to view using AI, especially

582
00:29:25,720 --> 00:29:29,479
Speaker 4: in professional settings or education settings, as unethical or cheating.

583
00:29:29,920 --> 00:29:33,480
Speaker 3: So I don't disagree with this. This totally makes sense

584
00:29:33,520 --> 00:29:34,560
Speaker 3: to me anecdotally.

585
00:29:34,960 --> 00:29:38,640
Speaker 4: However, I think it's more complex than that, because I

586
00:29:38,840 --> 00:29:41,440
Speaker 4: don't think it's just that women see AI is cheating.

587
00:29:42,240 --> 00:29:44,719
Speaker 4: It is that, But I also think that in workplaces

588
00:29:44,800 --> 00:29:48,400
Speaker 4: and in educational settings, women are more likely to be scrutinized.

589
00:29:47,840 --> 00:29:50,920
Speaker 3: That our male counterparts, right, and that's just the way.

590
00:29:51,000 --> 00:29:52,959
Speaker 4: Then it is, like women aren't wrong for being like, oh,

591
00:29:53,000 --> 00:29:55,720
Speaker 4: I feel like everybody's checking my work extra hard and

592
00:29:55,800 --> 00:29:59,160
Speaker 4: harder than they're checking Joe's work or whatever. The BBC

593
00:29:59,280 --> 00:30:02,239
Speaker 4: spoke to techlogist Lee Chambers, who said women are more

594
00:30:02,400 --> 00:30:04,800
Speaker 4: likely to be accused of not being competent, so they

595
00:30:04,880 --> 00:30:07,400
Speaker 4: have to emphasize their credentials more or demonstrate their subject

596
00:30:07,440 --> 00:30:09,800
Speaker 4: matter expertise in a particular field. There could be this

597
00:30:09,920 --> 00:30:12,080
Speaker 4: feeling that if people know that you, as a woman,

598
00:30:12,240 --> 00:30:14,560
Speaker 4: use AI, it is suggesting that you might not be

599
00:30:14,640 --> 00:30:17,680
Speaker 4: as qualified as you actually are. Women are already discredited

600
00:30:17,720 --> 00:30:19,800
Speaker 4: and have their ideas taken by men and passed off

601
00:30:19,880 --> 00:30:22,520
Speaker 4: as their own, So having people knowing that you use

602
00:30:22,560 --> 00:30:24,920
Speaker 4: AI might also play into this narrative that you're not

603
00:30:25,040 --> 00:30:28,280
Speaker 4: qualified enough. It's just another thing debasing your skills, your

604
00:30:28,320 --> 00:30:31,920
Speaker 4: competence and your value. Now that makes total sense to me.

605
00:30:32,000 --> 00:30:33,280
Speaker 3: And I am in.

606
00:30:35,040 --> 00:30:38,440
Speaker 4: Professional spaces of all kinds, and in some of these

607
00:30:38,440 --> 00:30:43,160
Speaker 4: professional spaces, I've been around very accomplished men in tech

608
00:30:43,560 --> 00:30:47,080
Speaker 4: who will not blink an eye at submitting like an

609
00:30:47,120 --> 00:30:49,160
Speaker 4: op ed or something, or a piece of writing that

610
00:30:49,280 --> 00:30:51,880
Speaker 4: was clearly written by AI. And it's just like and

611
00:30:52,040 --> 00:30:57,400
Speaker 4: I would never like the way. I would be so

612
00:30:57,640 --> 00:31:01,480
Speaker 4: embarrassed to have anybody be like, oh did you submit this?

613
00:31:01,640 --> 00:31:04,280
Speaker 4: And it was generated? I can tell by whatever, whatever,

614
00:31:04,680 --> 00:31:06,280
Speaker 4: I would crawl into a hole and die. I would

615
00:31:06,280 --> 00:31:08,640
Speaker 4: be so embarrassed. And the way that I can confirm

616
00:31:08,720 --> 00:31:11,760
Speaker 4: that there are a lot of men in high up

617
00:31:13,040 --> 00:31:16,160
Speaker 4: positions who do not feel that level of scrutiny. And

618
00:31:16,200 --> 00:31:19,160
Speaker 4: it goes back to like marginalized people in these settings.

619
00:31:19,480 --> 00:31:22,840
Speaker 4: Are we're so used to carrying that the extra eyes,

620
00:31:23,320 --> 00:31:26,480
Speaker 4: you know, extra scrutiny of why we're here at all,

621
00:31:26,760 --> 00:31:30,240
Speaker 4: our work, our value, our competence. Why would you want

622
00:31:30,280 --> 00:31:33,920
Speaker 4: to inject another reason to give somebody to continue.

623
00:31:33,560 --> 00:31:35,920
Speaker 3: To do that when you're already facing that in multiple levels.

624
00:31:37,440 --> 00:31:40,360
Speaker 2: I have to bring this back to fan fiction, you

625
00:31:40,480 --> 00:31:45,840
Speaker 2: know I do. So there's there's a big, like concern

626
00:31:46,000 --> 00:31:50,520
Speaker 2: in the fan fiction space of like using AI, and

627
00:31:50,880 --> 00:31:55,440
Speaker 2: it's become such a thing, and as discussed, a lot

628
00:31:55,600 --> 00:31:58,120
Speaker 2: of fan fiction is mostly written by women or non

629
00:31:58,200 --> 00:32:01,120
Speaker 2: binary people. People will go out of their way to say,

630
00:32:01,280 --> 00:32:03,959
Speaker 2: like in the tags I use M dashes, but it's

631
00:32:04,000 --> 00:32:06,200
Speaker 2: not AI. They go way out of their way to

632
00:32:06,240 --> 00:32:09,760
Speaker 2: be like I sawyar, it's not AI. And it's just

633
00:32:10,800 --> 00:32:13,040
Speaker 2: it's so clear to me that people don't want you

634
00:32:13,160 --> 00:32:14,960
Speaker 2: to think like, oh, this is not worth Like I

635
00:32:15,000 --> 00:32:19,040
Speaker 2: didn't even put in the time to do this, whereas yeah,

636
00:32:19,080 --> 00:32:22,920
Speaker 2: I do think I don't think men would see it

637
00:32:23,200 --> 00:32:26,200
Speaker 2: necessarily in that way. They would see it as like, oh, yeah,

638
00:32:26,200 --> 00:32:28,440
Speaker 2: it just saved me some time. But meanwhile, and now

639
00:32:28,480 --> 00:32:30,440
Speaker 2: I know that AI uses a lot of M dashes

640
00:32:30,560 --> 00:32:31,920
Speaker 2: because of fiction.

641
00:32:33,000 --> 00:32:37,040
Speaker 4: Yeah, that doesn't surprise me at all, because I think

642
00:32:37,560 --> 00:32:41,360
Speaker 4: the fan fiction community is definitely a community that values

643
00:32:41,880 --> 00:32:44,200
Speaker 4: human creativity, Like you wouldn't be in the fan fiction

644
00:32:44,280 --> 00:32:46,440
Speaker 4: community if you didn't value human creativity, and it's not.

645
00:32:46,520 --> 00:32:48,239
Speaker 4: It doesn't surprise me that that's a community that it's

646
00:32:48,280 --> 00:32:49,560
Speaker 4: also full of women.

647
00:32:51,000 --> 00:32:54,280
Speaker 2: Yes, yes, And then people always put this thing too

648
00:32:54,320 --> 00:32:56,160
Speaker 2: at the top where they're like, please don't feed my

649
00:32:56,240 --> 00:32:57,960
Speaker 2: work to AI, and I'm like, that's just not going

650
00:32:58,040 --> 00:32:59,920
Speaker 2: to help you, but I appreciate the effort.

651
00:33:01,240 --> 00:33:03,680
Speaker 5: Well, that's the other part to that is when you

652
00:33:03,800 --> 00:33:07,320
Speaker 5: start thinking about using chat GPT or any of the situations,

653
00:33:07,360 --> 00:33:09,520
Speaker 5: you don't know who you're taking from, and there is

654
00:33:09,600 --> 00:33:12,000
Speaker 5: this level of guilt for me on the end of

655
00:33:12,080 --> 00:33:14,960
Speaker 5: like not being the creative. But if I'm researching things

656
00:33:15,040 --> 00:33:17,920
Speaker 5: and I'm just basing it on one small bit of

657
00:33:17,960 --> 00:33:21,880
Speaker 5: information that's you know, from CHATJPT or Gemini or whatever whatnot,

658
00:33:22,080 --> 00:33:24,680
Speaker 5: which is constant, and I can't stand it. It feels

659
00:33:24,800 --> 00:33:28,760
Speaker 5: unethical in that manner of like we already know artists

660
00:33:29,000 --> 00:33:32,400
Speaker 5: are really really on edge because their work is feeling

661
00:33:32,480 --> 00:33:36,240
Speaker 5: constantly stolen. Creators who are writers, who are part of this,

662
00:33:36,480 --> 00:33:38,840
Speaker 5: researchers who are doing lots of work to get their

663
00:33:38,960 --> 00:33:41,480
Speaker 5: educations and to be in this field like using that

664
00:33:41,600 --> 00:33:45,480
Speaker 5: information and like oftentimes it's not cited. It just becomes

665
00:33:45,600 --> 00:33:48,600
Speaker 5: one big blob of information. It feels so gross to

666
00:33:48,800 --> 00:33:50,120
Speaker 5: see that play out.

667
00:33:50,320 --> 00:33:52,120
Speaker 3: Yeah, said such a good point.

668
00:33:52,240 --> 00:33:55,760
Speaker 4: There was this like micro controversy online a couple of

669
00:33:55,800 --> 00:33:59,440
Speaker 4: weeks ago about this literary festival, the Black Romance Literary Festival,

670
00:33:59,480 --> 00:34:02,760
Speaker 4: where the question was, do the people who run this festival,

671
00:34:02,760 --> 00:34:06,760
Speaker 4: the organizers, will they accept people who use AI in.

672
00:34:06,840 --> 00:34:09,399
Speaker 3: Their work at this organization? Will those people be allowed

673
00:34:09,440 --> 00:34:10,320
Speaker 3: to be included in panels?

674
00:34:10,360 --> 00:34:12,239
Speaker 4: And so it sounded that they were really talking about

675
00:34:12,640 --> 00:34:16,160
Speaker 4: illustrations like cover art, and so the organization gave what

676
00:34:16,360 --> 00:34:18,000
Speaker 4: to a lot of people in the community felt like

677
00:34:18,960 --> 00:34:21,759
Speaker 4: not a great answer, just sort of what they wrote.

678
00:34:21,800 --> 00:34:24,279
Speaker 4: The people the organizer said, we don't use AI in

679
00:34:24,400 --> 00:34:26,360
Speaker 4: our work, but we're not turning away people who do

680
00:34:26,600 --> 00:34:30,960
Speaker 4: use AI. We want to provide resources and education to

681
00:34:31,040 --> 00:34:33,239
Speaker 4: those folks, but we don't want to disclude them. One

682
00:34:33,280 --> 00:34:35,800
Speaker 4: of my listeners made a very good point that it

683
00:34:35,920 --> 00:34:38,919
Speaker 4: seemed like that was a kind of attempt, a harm

684
00:34:39,000 --> 00:34:41,800
Speaker 4: reduction kind of position, But maybe they didn't say it

685
00:34:41,880 --> 00:34:43,759
Speaker 4: like that, and they perhaps they shouldn't have quarified that.

686
00:34:43,920 --> 00:34:49,120
Speaker 4: But you know, I think at a time when artists,

687
00:34:49,320 --> 00:34:53,960
Speaker 4: particularly marginalized artists, are really having a hard like anybody

688
00:34:54,000 --> 00:34:56,719
Speaker 4: who tries to make make money from making a thing

689
00:34:56,880 --> 00:34:58,840
Speaker 4: is having a hard time of it right now. And

690
00:34:59,840 --> 00:35:02,680
Speaker 4: I I think when, as you said, Sam, we know

691
00:35:03,680 --> 00:35:07,919
Speaker 4: when you ask AI to generated image, it's not generating

692
00:35:07,960 --> 00:35:10,279
Speaker 4: a new piece, it's just taking.

693
00:35:10,080 --> 00:35:11,200
Speaker 3: From what's out there, right.

694
00:35:11,520 --> 00:35:14,400
Speaker 4: I remember there was a time where people were using

695
00:35:15,239 --> 00:35:18,360
Speaker 4: an AI tool to make those futuristic selfies. Half of

696
00:35:18,400 --> 00:35:22,600
Speaker 4: them had watermarks or logos in them somewhere. That's how

697
00:35:22,680 --> 00:35:25,640
Speaker 4: much they were just taking from other artists copyrighted work.

698
00:35:25,920 --> 00:35:26,759
Speaker 3: So I don't think that.

699
00:35:26,960 --> 00:35:30,560
Speaker 4: The creatives who are saying, hey, we need this to

700
00:35:30,640 --> 00:35:32,320
Speaker 4: be clarified, Hey, we need to put a line on

701
00:35:32,400 --> 00:35:34,160
Speaker 4: the stand of how what we're going to do when

702
00:35:34,200 --> 00:35:37,879
Speaker 4: it comes to taking other artists work in this way.

703
00:35:38,040 --> 00:35:40,520
Speaker 4: I don't think they're wrong for being anxious about that

704
00:35:40,640 --> 00:35:42,920
Speaker 4: and expecting and deserving some answers around it.

705
00:35:43,000 --> 00:35:56,560
Speaker 3: Frankly so, when it comes to kind of the.

706
00:35:56,600 --> 00:35:59,719
Speaker 4: Reasons why women might not be adopting AI like their

707
00:35:59,760 --> 00:36:03,600
Speaker 4: male counterparts, I did find this meta analysis pretty useful

708
00:36:03,680 --> 00:36:06,080
Speaker 4: and under like giving us the scope of the issue,

709
00:36:06,239 --> 00:36:08,640
Speaker 4: giving us the scope of the gender gap, and some

710
00:36:08,800 --> 00:36:11,400
Speaker 4: reasons as to why that might be. However, I just

711
00:36:11,480 --> 00:36:13,840
Speaker 4: don't think that some of the answers really spoke to

712
00:36:13,920 --> 00:36:16,080
Speaker 4: the nuance that I see in that experience as a

713
00:36:16,120 --> 00:36:18,200
Speaker 4: woman that's a little bit skeptical of AI. So I

714
00:36:18,280 --> 00:36:21,080
Speaker 4: found this piece in the Stanford Social Innovation Review by

715
00:36:21,160 --> 00:36:23,719
Speaker 4: Mara Bolis, who is the founder of First Prompt and

716
00:36:23,719 --> 00:36:26,520
Speaker 4: a fellow at the Harvard Kennedy School, called the AI

717
00:36:26,719 --> 00:36:30,279
Speaker 4: gender Gap Paradox, and I think that this piece does

718
00:36:30,480 --> 00:36:32,400
Speaker 4: a really good job of getting at some of the

719
00:36:32,480 --> 00:36:35,640
Speaker 4: nuances that's not just women aren't smart enough to get

720
00:36:35,719 --> 00:36:38,440
Speaker 4: AI or whatever right. And so one bit that she

721
00:36:38,520 --> 00:36:41,600
Speaker 4: pointed out is that women are already doing a lot

722
00:36:41,840 --> 00:36:44,839
Speaker 4: of work. She writes, more than half a professional say

723
00:36:44,880 --> 00:36:47,520
Speaker 4: that learning AI feels like a second job, which for

724
00:36:47,640 --> 00:36:50,720
Speaker 4: most women is actually a third job when you consider

725
00:36:50,800 --> 00:36:54,880
Speaker 4: the continued disparities in time spent on childcare and housework.

726
00:36:55,640 --> 00:36:58,800
Speaker 4: And so that I thought was such a good point,

727
00:36:58,920 --> 00:37:02,120
Speaker 4: right that doing your current job or your current educational

728
00:37:02,160 --> 00:37:04,839
Speaker 4: workload or whatever is already a lot of work. If

729
00:37:04,840 --> 00:37:08,440
Speaker 4: you're a woman, you're probably taking on more of the

730
00:37:08,520 --> 00:37:10,719
Speaker 4: labor at home, or of the emotional labor, or of

731
00:37:10,800 --> 00:37:13,520
Speaker 4: the care work, all of that. And on top of it,

732
00:37:13,719 --> 00:37:15,440
Speaker 4: now people are telling you that if you don't take

733
00:37:15,480 --> 00:37:18,120
Speaker 4: the time to learn how to integrate AI into that work,

734
00:37:18,480 --> 00:37:20,439
Speaker 4: you don't really care about your career and you're holding

735
00:37:20,480 --> 00:37:23,320
Speaker 4: yourself back. I just, yeah, it's like that, Like that

736
00:37:23,480 --> 00:37:25,239
Speaker 4: has to be part of the conversation if we're going

737
00:37:25,280 --> 00:37:30,040
Speaker 4: to actually address the gender gap in AI usage. Yes. Yeah,

738
00:37:30,360 --> 00:37:32,439
Speaker 4: And this piece I think also just does a great

739
00:37:32,520 --> 00:37:37,759
Speaker 4: job of reframing fear or knowledge gaps into what I

740
00:37:37,840 --> 00:37:41,480
Speaker 4: suspect they actually are, which is the ability to competently

741
00:37:41,719 --> 00:37:44,960
Speaker 4: analyze risk. Women aren't looking at AI and saying I'm

742
00:37:45,000 --> 00:37:46,840
Speaker 4: afraid of it or like I can never learn it.

743
00:37:47,080 --> 00:37:50,399
Speaker 4: I think what they're actually saying is I have analyzed

744
00:37:50,640 --> 00:37:53,600
Speaker 4: the risk that using it might bring into my work,

745
00:37:53,680 --> 00:37:56,919
Speaker 4: and I've made a decision based on that that I'm

746
00:37:56,960 --> 00:37:59,880
Speaker 4: not gonna be using it like that. That piece in

747
00:38:00,080 --> 00:38:03,719
Speaker 4: the Stanford Innovation Review looked at studies from Deloitte and Pew.

748
00:38:04,480 --> 00:38:07,279
Speaker 4: Both of them showed that women predict AI will bring

749
00:38:07,440 --> 00:38:11,840
Speaker 4: less benefit and do more harm across personal, professional and

750
00:38:11,920 --> 00:38:15,680
Speaker 4: public life. Men, however, tend to be more optimistic, confident,

751
00:38:15,960 --> 00:38:18,080
Speaker 4: and self assured in their competence.

752
00:38:18,840 --> 00:38:19,920
Speaker 3: Doesn't that really say at all?

753
00:38:20,960 --> 00:38:22,960
Speaker 5: Well, there's so much Like when I'm thinking about the

754
00:38:23,000 --> 00:38:26,239
Speaker 5: amount that we research and the amount that we try

755
00:38:26,360 --> 00:38:29,360
Speaker 5: to make sure that we are noting who is you know,

756
00:38:29,480 --> 00:38:32,080
Speaker 5: referencing and giving credit to whom ever, so we're not

757
00:38:32,280 --> 00:38:37,120
Speaker 5: doing any violent violations, copyright law, any of those things.

758
00:38:37,440 --> 00:38:40,480
Speaker 5: And again we know that chat, GPT or any of

759
00:38:40,560 --> 00:38:41,600
Speaker 5: the AI really care.

760
00:38:42,000 --> 00:38:44,120
Speaker 3: They don't care where the sources are coming from.

761
00:38:44,560 --> 00:38:48,360
Speaker 5: And so as people who know A that it is

762
00:38:48,480 --> 00:38:51,160
Speaker 5: not one hundred percent proof and we are trying to

763
00:38:51,239 --> 00:38:56,080
Speaker 5: be doing our due diligence and give correct information. Yeah,

764
00:38:56,239 --> 00:38:58,640
Speaker 5: that is more work to go back to whatever we

765
00:38:58,800 --> 00:39:03,160
Speaker 5: thought we could use with AI, to actually verify that

766
00:39:03,480 --> 00:39:05,880
Speaker 5: where this information is coming from. Like it's kind of

767
00:39:05,880 --> 00:39:08,600
Speaker 5: funny when we talk about having research help and having

768
00:39:08,719 --> 00:39:11,359
Speaker 5: someone else do the research, it feels like more work

769
00:39:11,360 --> 00:39:13,040
Speaker 5: because I'm like, I don't know if this is true.

770
00:39:13,040 --> 00:39:14,640
Speaker 3: I gotta go read the whole article again, you know.

771
00:39:14,640 --> 00:39:17,080
Speaker 5: What, I'm like doing more work. And it feels the

772
00:39:17,160 --> 00:39:21,080
Speaker 5: same way in this type of conversation with AI, because

773
00:39:21,120 --> 00:39:24,600
Speaker 5: I don't completely trust once again and like analyzing the

774
00:39:24,680 --> 00:39:26,200
Speaker 5: risk that this is actually true.

775
00:39:26,920 --> 00:39:29,320
Speaker 3: This might sound way off dates, but when I was

776
00:39:29,440 --> 00:39:32,680
Speaker 3: reading some of the research about why women don't use

777
00:39:32,800 --> 00:39:36,680
Speaker 3: AI as much, I couldn't help thinking about what a

778
00:39:36,760 --> 00:39:39,600
Speaker 3: lot of heterosexual women who are married to man or

779
00:39:39,640 --> 00:39:42,759
Speaker 3: in relationships with men report, where if you ask a

780
00:39:42,840 --> 00:39:45,600
Speaker 3: man in your life to do something, what you're actually

781
00:39:45,800 --> 00:39:48,440
Speaker 3: creating is like work for you later when you have

782
00:39:48,560 --> 00:39:52,479
Speaker 3: to answer the follow up or correct what he's done wrong.

783
00:39:53,040 --> 00:39:56,240
Speaker 3: I hate that for us, find women who find themselves

784
00:39:56,360 --> 00:39:59,040
Speaker 3: in close relation with men hate I hate that for us,

785
00:40:00,160 --> 00:40:04,279
Speaker 3: But like that that is what we got, that we're

786
00:40:04,360 --> 00:40:06,880
Speaker 3: served that all the time we would be lying, Like I.

787
00:40:06,880 --> 00:40:09,000
Speaker 4: Think every woman listening has had that experience, and it's

788
00:40:09,360 --> 00:40:13,279
Speaker 4: so frustrating, But I think that's it really mirrors to

789
00:40:13,400 --> 00:40:16,000
Speaker 4: me what women are talking about when they talk about

790
00:40:16,120 --> 00:40:18,120
Speaker 4: how AI is used in their work. When you have

791
00:40:18,200 --> 00:40:21,719
Speaker 4: to go and correct the mistake, ask again, give the

792
00:40:21,800 --> 00:40:24,520
Speaker 4: ball whatever it's gonna be. That's just adding more and

793
00:40:24,640 --> 00:40:27,279
Speaker 4: more to your emotional load. It's been easier for you

794
00:40:27,360 --> 00:40:29,160
Speaker 4: to do it yourself in the first place. Right.

795
00:40:29,760 --> 00:40:32,000
Speaker 5: Also, that does feel like if we get something wrong,

796
00:40:32,200 --> 00:40:34,600
Speaker 5: as women are marginalized people, we're going to be penalized

797
00:40:34,719 --> 00:40:37,680
Speaker 5: a lot more than when men do. Men are given

798
00:40:37,760 --> 00:40:40,640
Speaker 5: excuses and are like, eh, you're fine as a one

799
00:40:40,719 --> 00:40:42,759
Speaker 5: time thing, or they feel like they can brush it off.

800
00:40:42,760 --> 00:40:45,000
Speaker 5: Whether it's just maybe they feel like they can do

801
00:40:45,080 --> 00:40:47,040
Speaker 5: that way as where women have the anxiety of like, no,

802
00:40:47,120 --> 00:40:50,080
Speaker 5: we're going to get the worst punishment everlash martinalized people.

803
00:40:50,120 --> 00:40:52,840
Speaker 5: So maybe that's also that level of confidence that we

804
00:40:52,960 --> 00:40:54,760
Speaker 5: don't have, which is delusional.

805
00:40:55,960 --> 00:40:59,600
Speaker 4: Yes, And I think this speaks to something else that

806
00:40:59,640 --> 00:41:01,480
Speaker 4: I saw the Stanford piece and I just thought was

807
00:41:01,560 --> 00:41:04,560
Speaker 4: so interesting, which is that women's self report not feeling

808
00:41:04,640 --> 00:41:08,400
Speaker 4: confident with how AI works. We might be offered AI

809
00:41:08,760 --> 00:41:11,680
Speaker 4: skills training, but what we're not really offered is a

810
00:41:11,760 --> 00:41:14,920
Speaker 4: chance that like actual knowledge into this technology, all the

811
00:41:15,000 --> 00:41:18,120
Speaker 4: issues that go into it, like an actual fundamental understanding

812
00:41:18,200 --> 00:41:20,440
Speaker 4: and crash course on how it works. The Peace Sites

813
00:41:20,480 --> 00:41:22,520
Speaker 4: a Federal Reserve Bank of New York survey that shows

814
00:41:22,520 --> 00:41:25,319
Speaker 4: that when women say they want generative AI training, they

815
00:41:25,360 --> 00:41:28,480
Speaker 4: are not just looking for skills. They're signaling awareness of

816
00:41:28,520 --> 00:41:31,480
Speaker 4: this technology's opacity and their unwillingness to trust a system

817
00:41:31,480 --> 00:41:34,399
Speaker 4: that they don't fully understand. A systemic review of gender

818
00:41:34,480 --> 00:41:37,360
Speaker 4: and AI adoption found that women consistently cite things like

819
00:41:37,480 --> 00:41:40,760
Speaker 4: lack of transparency and the opaque nature of AI tools

820
00:41:40,800 --> 00:41:41,960
Speaker 4: as barriers to trust.

821
00:41:42,280 --> 00:41:45,040
Speaker 3: And so it's exactly what you dis laid out, Sam,

822
00:41:45,160 --> 00:41:46,960
Speaker 3: And again, that's.

823
00:41:46,840 --> 00:41:50,160
Speaker 4: Not the same thing as not feeling smart enough or

824
00:41:50,280 --> 00:41:53,520
Speaker 4: competent enough to understand something. That it's reasonable to say

825
00:41:53,800 --> 00:41:56,839
Speaker 4: that people that make AI tools have intentionally designed them

826
00:41:56,920 --> 00:41:59,399
Speaker 4: and they intentionally talk about them with such a lack

827
00:41:59,440 --> 00:42:02,480
Speaker 4: of transparent garancy that I don't trust it. That's that's

828
00:42:02,680 --> 00:42:06,640
Speaker 4: that is a really reasonable position. That's not unreasonable. I

829
00:42:06,640 --> 00:42:10,080
Speaker 4: don't like the framing that this like totally reasonable way

830
00:42:10,200 --> 00:42:14,200
Speaker 4: to be is you know, bad in some way, or

831
00:42:14,239 --> 00:42:16,960
Speaker 4: that women are shooting themselves in the foot economically by

832
00:42:17,320 --> 00:42:20,239
Speaker 4: showing this like reasonable lack of trust.

833
00:42:20,480 --> 00:42:23,080
Speaker 5: Right, And you know, another thing to that is again,

834
00:42:23,120 --> 00:42:25,520
Speaker 5: I keep coming back to how we've seen the mistakes

835
00:42:25,640 --> 00:42:27,960
Speaker 5: and maybe that's what it is. This has been so rushed,

836
00:42:28,280 --> 00:42:30,560
Speaker 5: but the constant times that like we see things that

837
00:42:30,640 --> 00:42:33,160
Speaker 5: are not good are usually really bad for women. Like

838
00:42:33,200 --> 00:42:36,239
Speaker 5: I keep thinking about the AI videos of the deep

839
00:42:36,320 --> 00:42:38,520
Speaker 5: fakes that happened with that twitch switch streamer that we

840
00:42:38,600 --> 00:42:40,439
Speaker 5: talked about. Was it two years ago? And I don't

841
00:42:40,440 --> 00:42:42,399
Speaker 5: even remember we had, like cause we were really talking

842
00:42:42,480 --> 00:42:46,400
Speaker 5: about the fact things like this is super concerning because

843
00:42:46,440 --> 00:42:49,919
Speaker 5: it does go after women for like just being there.

844
00:42:50,680 --> 00:42:52,959
Speaker 4: Yeah, I mean that speaks to what I was talking

845
00:42:53,000 --> 00:42:54,640
Speaker 4: about earlier when we were talking about the way that

846
00:42:54,680 --> 00:42:58,160
Speaker 4: Sam Altman, the way that Sam Altman was presenting talking

847
00:42:58,200 --> 00:43:02,000
Speaker 4: about the chatbot they were creating Sky that there are

848
00:43:02,200 --> 00:43:08,600
Speaker 4: so many examples of the vibes just being suss for

849
00:43:08,760 --> 00:43:11,920
Speaker 4: women in these spaces, right. You know, I make a

850
00:43:12,040 --> 00:43:17,319
Speaker 4: podcast about the intersection of gender and technology, and oftentimes

851
00:43:17,360 --> 00:43:20,120
Speaker 4: when we talk about AI, we're talking about things like

852
00:43:20,520 --> 00:43:23,960
Speaker 4: non consensual sexualized deep fakes. We're talking about things like

853
00:43:24,120 --> 00:43:27,400
Speaker 4: Sam Altman, you know, saying people should be able to

854
00:43:27,440 --> 00:43:30,239
Speaker 4: have the kind of relationship that Joaquin Phoenix has with

855
00:43:30,880 --> 00:43:33,880
Speaker 4: the chatbot in the movie Her. We're talking about things

856
00:43:34,000 --> 00:43:40,040
Speaker 4: like Elon Musk creating a anime teenager kind of sex

857
00:43:40,239 --> 00:43:45,799
Speaker 4: fantasy chatbot. We're talking about men who build technology, who

858
00:43:46,520 --> 00:43:50,640
Speaker 4: fill their teams with other men. They lack gender diversity

859
00:43:50,680 --> 00:43:52,719
Speaker 4: and racial diversity or any kind of real inclusion, and

860
00:43:52,880 --> 00:43:57,160
Speaker 4: then they talk about women in these like misogynistic ways.

861
00:43:57,400 --> 00:44:00,279
Speaker 4: It's not surprising to me that women are being lower

862
00:44:00,400 --> 00:44:02,719
Speaker 4: to adopt this technology that is made in such a

863
00:44:03,480 --> 00:44:07,160
Speaker 4: misogynistic soup. And as Mara Boll has put it in

864
00:44:07,239 --> 00:44:11,279
Speaker 4: that Stanford Review piece, as in financial systems, women are

865
00:44:11,280 --> 00:44:14,680
Speaker 4: attuned to the weaknesses in generative AI systems that designers

866
00:44:14,880 --> 00:44:18,640
Speaker 4: didn't notice or prioritize things like bias, privacy risks, unreliable

867
00:44:18,680 --> 00:44:22,080
Speaker 4: outputs before putting their products out into the world, which

868
00:44:22,120 --> 00:44:24,200
Speaker 4: speaks to that that rushed quality that you were just

869
00:44:24,440 --> 00:44:28,560
Speaker 4: clocking earlier. Some of the industry's more misogynistic offerings see

870
00:44:28,800 --> 00:44:33,120
Speaker 4: rocks Annie fantasy chatbot, or disturbing policies see Facebook's Leach

871
00:44:33,200 --> 00:44:35,640
Speaker 4: policies on children and a list of content are enough

872
00:44:35,680 --> 00:44:39,000
Speaker 4: to send most users into a kindatonic depressive spiral. But

873
00:44:39,160 --> 00:44:43,239
Speaker 4: for women, beyond being offensive, such outputs are evidence of

874
00:44:43,360 --> 00:44:47,040
Speaker 4: what gets built when developmental teams lack gender diversity. When

875
00:44:47,080 --> 00:44:49,680
Speaker 4: women engage with systems that they've been largely left out

876
00:44:49,680 --> 00:44:53,840
Speaker 4: of creating, the products can feel foreign, awkward, or even hostile.

877
00:44:54,200 --> 00:44:56,960
Speaker 4: And so I think that is absolutely what's going on here.

878
00:44:57,560 --> 00:45:00,560
Speaker 4: The vibes around how this technology is made, who makes it,

879
00:45:00,840 --> 00:45:02,960
Speaker 4: who is in the room, who's who has the power

880
00:45:03,040 --> 00:45:05,160
Speaker 4: over it, and how how that power is being youthed

881
00:45:05,160 --> 00:45:08,000
Speaker 4: and wielded to shape the world. All of that sucks

882
00:45:08,080 --> 00:45:11,880
Speaker 4: for marginalized people. So yeah, surprise, surprise, those same marginalized

883
00:45:11,880 --> 00:45:15,439
Speaker 4: people are saying no, thank you, y'all can keep it, yep.

884
00:45:15,960 --> 00:45:20,200
Speaker 2: And it's not like we don't have historical, societal, systemic

885
00:45:20,320 --> 00:45:22,200
Speaker 2: evidence of this going wrong for us.

886
00:45:22,280 --> 00:45:22,560
Speaker 1: I could.

887
00:45:22,840 --> 00:45:26,239
Speaker 2: I love how men are like confident, optimistic. Oh, it's

888
00:45:26,280 --> 00:45:29,040
Speaker 2: gonna be great. I'm like, because its historically as for.

889
00:45:29,120 --> 00:45:34,800
Speaker 3: You, Yeah, guess what. Not so much over here, not

890
00:45:35,080 --> 00:45:38,279
Speaker 3: so much at all. And I wanted to end on this.

891
00:45:39,080 --> 00:45:40,800
Speaker 4: When I was researching this, I found this post on

892
00:45:40,840 --> 00:45:42,800
Speaker 4: Reddit that I thought really did a great job of

893
00:45:42,840 --> 00:45:44,800
Speaker 4: summarizing my thoughts. So there was a post in the

894
00:45:44,920 --> 00:45:48,520
Speaker 4: ask Feminists subreddit, and the post asks why are women

895
00:45:48,800 --> 00:45:52,040
Speaker 4: using generative AI lesson men and the redditor trooper s

896
00:45:52,120 --> 00:45:53,000
Speaker 4: JP answers.

897
00:45:53,680 --> 00:45:54,920
Speaker 3: I'm a university professor.

898
00:45:55,320 --> 00:45:58,440
Speaker 4: If female students are using chat EPT less than male students,

899
00:45:58,640 --> 00:46:01,399
Speaker 4: it is the male students should be worried about. There

900
00:46:01,440 --> 00:46:03,680
Speaker 4: have been studies showing that there is a negative cognitive

901
00:46:03,719 --> 00:46:07,000
Speaker 4: impact of using generative AI on learners. This includes loss

902
00:46:07,040 --> 00:46:10,040
Speaker 4: and ability to retain information, loss of critical thinking skills,

903
00:46:10,320 --> 00:46:13,680
Speaker 4: loss of empathy, degradation of writing and math skills, degradation

904
00:46:13,800 --> 00:46:17,960
Speaker 4: of problem solving skills, and on and on. Furthermore, generative

905
00:46:18,000 --> 00:46:20,840
Speaker 4: AI is bad for the environment, contributes to environmental racism,

906
00:46:21,200 --> 00:46:23,840
Speaker 4: is built on stolen data by corporations who will zealously

907
00:46:23,920 --> 00:46:27,360
Speaker 4: guard their own intellectual property while willingly violating yours and

908
00:46:27,520 --> 00:46:31,520
Speaker 4: capitalist excitement in replacing entry level workers with AI is

909
00:46:31,600 --> 00:46:34,759
Speaker 4: having an immediate negative impact on young job seekers aged

910
00:46:34,760 --> 00:46:37,160
Speaker 4: twenty two to twenty seven, but will have a much

911
00:46:37,320 --> 00:46:40,120
Speaker 4: more profound impact on all of us when we lose

912
00:46:40,160 --> 00:46:42,640
Speaker 4: a generation of entry level workers who won't get the

913
00:46:42,680 --> 00:46:46,960
Speaker 4: experience needed to become experienced workers when the experienced workers retire.

914
00:46:47,480 --> 00:46:51,120
Speaker 4: Chat GPT is also inaccurate, bland, and produces terrible work

915
00:46:51,360 --> 00:46:55,440
Speaker 4: full of false information. Furthermore, in classrooms, including mine, the

916
00:46:55,560 --> 00:46:58,760
Speaker 4: use of generative AI is considered an academic integrity violation

917
00:46:59,120 --> 00:47:01,040
Speaker 4: and will result in as zero for that assignment and

918
00:47:01,200 --> 00:47:04,200
Speaker 4: forwarding the case to the student Conduct Board. My students

919
00:47:04,239 --> 00:47:06,440
Speaker 4: would not appreciate it if I use chat cheapt to

920
00:47:06,480 --> 00:47:08,919
Speaker 4: create lesson plans, to grade their work, and to write

921
00:47:08,920 --> 00:47:11,800
Speaker 4: their letters of recommendation. I do not appreciate my students

922
00:47:11,880 --> 00:47:14,120
Speaker 4: passing off work they didn't write that has a direct

923
00:47:14,200 --> 00:47:16,640
Speaker 4: negative impact on themselves and the world as their own.

924
00:47:16,960 --> 00:47:18,560
Speaker 4: So I don't think it is bad that women are

925
00:47:18,640 --> 00:47:20,960
Speaker 4: less likely to be cheating by using chat cheapt in

926
00:47:20,960 --> 00:47:22,800
Speaker 4: their work than men. I think it is bad that

927
00:47:22,920 --> 00:47:26,880
Speaker 4: so many men are wasting their educations by cheating. And

928
00:47:27,000 --> 00:47:30,239
Speaker 4: I felt like that really summed up what I was

929
00:47:30,320 --> 00:47:33,360
Speaker 4: trying to say about this reframing. I don't like that

930
00:47:33,480 --> 00:47:37,000
Speaker 4: this conversation is simply framed as women aren't using AI

931
00:47:37,719 --> 00:47:41,640
Speaker 4: and thus sort of almost blaming women for all of

932
00:47:41,719 --> 00:47:44,600
Speaker 4: these systemic reasons and reasonable reasons as to why that

933
00:47:44,760 --> 00:47:47,439
Speaker 4: might be, and also sort of saying, well, if women

934
00:47:47,520 --> 00:47:51,080
Speaker 4: weren't so afraid and also stupid about AI, maybe they'd

935
00:47:51,120 --> 00:47:51,919
Speaker 4: be making more women.

936
00:47:52,040 --> 00:47:53,640
Speaker 3: It's not society, it's a stupid women.

937
00:47:54,000 --> 00:47:56,600
Speaker 4: I really think that we need to reframe that and say, well,

938
00:47:56,640 --> 00:47:59,680
Speaker 4: what are women telling us by not using this technology? Like,

939
00:48:00,040 --> 00:48:02,399
Speaker 4: should we really just be saying these women are making

940
00:48:02,440 --> 00:48:04,600
Speaker 4: a bad choice. Shouldn't we really be zeroing in on

941
00:48:04,760 --> 00:48:07,279
Speaker 4: what women are saying as that pertains to why we

942
00:48:07,360 --> 00:48:11,000
Speaker 4: are not so gung ho to be adopting this technology

943
00:48:11,040 --> 00:48:11,799
Speaker 4: the way that men are.

944
00:48:12,120 --> 00:48:13,719
Speaker 3: That's that's sort of my that's sort of my my.

945
00:48:13,880 --> 00:48:14,840
Speaker 3: So what in all this?

946
00:48:15,840 --> 00:48:16,000
Speaker 4: You know?

947
00:48:16,040 --> 00:48:17,759
Speaker 5: And I think in the future we were going to

948
00:48:17,800 --> 00:48:21,520
Speaker 5: come back and have a conversation about the red the

949
00:48:21,640 --> 00:48:24,960
Speaker 5: lining of this type of usage in this conversation and

950
00:48:25,280 --> 00:48:29,440
Speaker 5: the growing population of the red pill community, Yes, because

951
00:48:29,440 --> 00:48:31,399
Speaker 5: we're talking more and more like it has become even

952
00:48:31,440 --> 00:48:33,200
Speaker 5: more so. Worthy have come to the point of saying

953
00:48:33,560 --> 00:48:37,000
Speaker 5: of inceels saying being straight and having a relationship with

954
00:48:37,040 --> 00:48:41,399
Speaker 5: a woman is gay, Like literally, that's their like and soul?

955
00:48:41,480 --> 00:48:43,800
Speaker 3: Have you seen this? So really a man can do

956
00:48:44,000 --> 00:48:46,680
Speaker 3: is sleep with a woman having sex with a woman.

957
00:48:46,719 --> 00:48:48,920
Speaker 5: Right, Submitting and pleasing a woman essentially was kind of

958
00:48:48,960 --> 00:48:51,640
Speaker 5: like that kind of intake. But I really do wonder

959
00:48:51,880 --> 00:48:56,000
Speaker 5: if this conversation of using chat GPT to replace that

960
00:48:56,120 --> 00:48:58,440
Speaker 5: interaction again with a mal loneliness epidemic, and how it

961
00:48:58,440 --> 00:49:02,160
Speaker 5: becomes violent. If this conversation and if this push and

962
00:49:02,239 --> 00:49:04,920
Speaker 5: things like what Sam Allman was trying to do when

963
00:49:04,960 --> 00:49:08,640
Speaker 5: he first created that chatbot, as well as what Elon

964
00:49:08,760 --> 00:49:11,800
Speaker 5: Musk is trying to do creating his chatbot is just

965
00:49:12,000 --> 00:49:16,560
Speaker 5: opening up a bigger community, a bigger doorway for more

966
00:49:16,800 --> 00:49:19,080
Speaker 5: in cell activity and red pill activity, and we're gonna

967
00:49:19,120 --> 00:49:20,800
Speaker 5: have to come back and have to deal with the

968
00:49:20,840 --> 00:49:25,000
Speaker 5: consequence of things like AI being a part of this narrative.

969
00:49:25,840 --> 00:49:30,800
Speaker 4: Absolutely, I completely agree. I think those things are totally linked.

970
00:49:31,280 --> 00:49:32,480
Speaker 4: And I'm in the middle of doing a lot of

971
00:49:32,520 --> 00:49:36,160
Speaker 4: research about the kinds of sexual and romantic attachments that

972
00:49:36,200 --> 00:49:40,800
Speaker 4: people create with AI, and I think it's complicated because

973
00:49:41,040 --> 00:49:43,600
Speaker 4: I want people to feel like they have nourishing relationships

974
00:49:43,600 --> 00:49:48,120
Speaker 4: in their lives. But healthy nourishing relationships come with friction,

975
00:49:48,360 --> 00:49:50,480
Speaker 4: like that's what you get from being in relationship with

976
00:49:50,560 --> 00:49:54,320
Speaker 4: other humans. And I think that the availability of folks

977
00:49:54,400 --> 00:49:59,880
Speaker 4: to kind of engage in frictionless relationships where they can

978
00:50:00,080 --> 00:50:02,000
Speaker 4: just take and take and take an extract and extract

979
00:50:02,040 --> 00:50:03,800
Speaker 4: and extract and always get what they want out of

980
00:50:03,840 --> 00:50:06,280
Speaker 4: them in some ways I can see by that is therapeutic,

981
00:50:06,400 --> 00:50:09,879
Speaker 4: but that is not what builds healthy people like health

982
00:50:09,960 --> 00:50:13,600
Speaker 4: Like healthy people have to experience relationships with friction. And

983
00:50:13,680 --> 00:50:14,600
Speaker 4: that's just the bottom line.

984
00:50:16,239 --> 00:50:20,160
Speaker 1: Yes, yes, well topic for a future day.

985
00:50:20,520 --> 00:50:21,800
Speaker 3: Yeah, I love it.

986
00:50:23,080 --> 00:50:25,800
Speaker 2: I also have to say a dude told me the

987
00:50:25,880 --> 00:50:29,520
Speaker 2: other day. He was like, well, I love AI because

988
00:50:29,520 --> 00:50:32,160
Speaker 2: it gives me more leisure time. And I was like, well,

989
00:50:32,160 --> 00:50:34,399
Speaker 2: wait till you don't have a job, and then you're

990
00:50:34,440 --> 00:50:35,760
Speaker 2: not gonna have a way.

991
00:50:35,920 --> 00:50:40,040
Speaker 3: That time, it'll be all all the time and we

992
00:50:40,200 --> 00:50:43,360
Speaker 3: won't be able to do all those things because you

993
00:50:43,480 --> 00:50:47,319
Speaker 3: have no monies more leisure time.

994
00:50:48,040 --> 00:50:51,680
Speaker 2: Oh all right, well, thank you so much, Bridget for

995
00:50:51,880 --> 00:50:55,239
Speaker 2: coming during the stressful time. We always love talking to you.

996
00:50:55,880 --> 00:50:57,640
Speaker 2: Can't wait to see you in the new year twenty

997
00:50:57,680 --> 00:50:58,200
Speaker 2: twenty six.

998
00:50:58,680 --> 00:50:59,879
Speaker 3: Yes, we're almost.

999
00:50:59,600 --> 00:51:03,200
Speaker 1: There, almost there. In the meantime, where can the good

1000
00:51:03,239 --> 00:51:03,920
Speaker 1: listeners find you?

1001
00:51:04,200 --> 00:51:05,880
Speaker 4: You can find me at my podcast There are no

1002
00:51:06,040 --> 00:51:07,960
Speaker 4: goals on the internet, where we explore all kinds of

1003
00:51:08,000 --> 00:51:11,640
Speaker 4: issues of the intersection of gender and identity and technology

1004
00:51:11,680 --> 00:51:13,520
Speaker 4: and social media. You can check me out on Instagram

1005
00:51:13,600 --> 00:51:16,000
Speaker 4: at Bridget Marie in DC, or on YouTube. But there

1006
00:51:16,040 --> 00:51:17,439
Speaker 4: are no girls on the internet.

1007
00:51:17,840 --> 00:51:19,279
Speaker 1: Yes, and go do that.

1008
00:51:19,440 --> 00:51:22,440
Speaker 2: If you have not already listeners, If you would like

1009
00:51:22,520 --> 00:51:24,400
Speaker 2: to contact us, you can You can email us at

1010
00:51:24,440 --> 00:51:26,080
Speaker 2: Hello at stuff onever Told You dot com. You can

1011
00:51:26,120 --> 00:51:27,880
Speaker 2: find us on Blue skyte Mom Stuff Podcasts, or on

1012
00:51:27,880 --> 00:51:29,600
Speaker 2: Instagram and TikTok at stuff one Never Told You.

1013
00:51:29,880 --> 00:51:30,760
Speaker 1: We're also on YouTube.

1014
00:51:30,880 --> 00:51:32,480
Speaker 2: We have some new merchandise at comp Bureau, and we

1015
00:51:32,520 --> 00:51:34,040
Speaker 2: have a book you can get wherever you get your books.

1016
00:51:34,239 --> 00:51:36,839
Speaker 2: Thanks as always too, our superducer PRIs Senior Executive Prius

1017
00:51:36,880 --> 00:51:39,040
Speaker 2: my under contributor Joey, Thank you and thanks to you

1018
00:51:39,160 --> 00:51:41,480
Speaker 2: for listening. Stuff Never Told You is production by Heart Radio.

1019
00:51:41,520 --> 00:51:43,080
Speaker 2: For more podcasts from my heart Radio, you can check

1020
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1021
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