Speaker 1: It's nine eighteen pm at the Malaga Inn and you're
Speaker 1: listening to Night Call. Hello, and welcome to night Call,
Speaker 1: a call in show for artist Topy in Reality. My
Speaker 1: name is Emily Oshida, and with me on the other
Speaker 1: line are Molly Lambert and Tess Lynch. Also stay tuned
Speaker 1: because later in the show we have Meredith Whittaker, the
Speaker 1: research professor and co founder of the AI Now Institute
Speaker 1: at n y u UM. She was also involved in
Speaker 1: the Google walkouts. She's awesome, so please stick around to
Speaker 1: hear her. Come by later. Hi, guys, but first I'm back.
Speaker 1: I'm back from my truck across the country. Thank you
Speaker 1: both for holding down the fort while I communed with
Speaker 1: the Highway after but here after Highway Uh yeah, I
Speaker 1: did a six day trip across the country and it
Speaker 1: wouldn't have been complete if I didn't stay at a
Speaker 1: ghost hotel my last night, uh in Mobile, Alabama. So
Speaker 1: I actually, I mean I wish that I had a
Speaker 1: more colorful story to share with you, because you guys
Speaker 1: are like call us from the hotel, like, like, you know,
Speaker 1: tell us what it's like. It was like it was
Speaker 1: actually just like nice, It was like a nice kind
Speaker 1: of very Southern style old hotel that was surprisingly affordable,
Speaker 1: which is why I went there. I was like, this,
Speaker 1: there's got to be a scam. Like, I'm that's the
Speaker 1: benefit of a ghost I know. I was like, I
Speaker 1: was like, what's the catch because it's so nice. It
Speaker 1: was like one of the cleanest places I stayed, which
Speaker 1: was a big concern over my trip. Um. But I also,
Speaker 1: you know, it's on the It's on all these lists
Speaker 1: of haunted hotels in UM in the South and this once. Yeah,
Speaker 1: it's called the Malaga in it's in Mobile, Alabama. I
Speaker 1: you know, I booked it, and then I went, I
Speaker 1: should have done this in the other order. I booked it,
Speaker 1: and then I went looking for ghost stories about it. Um.
Speaker 1: But there was one I sent you guys that I
Speaker 1: just want to bring up again because it actually did
Speaker 1: kind of freak me out. UM. But this is on
Speaker 1: I wish I remember what sight this was on. But
Speaker 1: somebody said, my sister and I stayed at the Malaga
Speaker 1: in in summer. Uh. They had a great dinner and
Speaker 1: spent their time out on the balcony and joying the evening.
Speaker 1: But in the middle of the night, I was awakened
Speaker 1: by my sister screaming bloody murder because someone had crawled
Speaker 1: into bed with her, and even worse what she realized
Speaker 1: there was no one there. And the next day they
Speaker 1: asked the friend dusk about it, and apparently the room
Speaker 1: they were in was the one with the activity, which
Speaker 1: is apparently room one oh seven at this hotel. So
Speaker 1: I stayed in room one oh three. I was two
Speaker 1: doors down from there, but I was in a historic room.
Speaker 1: There were parts of the hotel that we're part of
Speaker 1: the original structure, and then ones that came in later.
Speaker 1: So I made sure to get one that was like
Speaker 1: in in the good and haunted zone. Uh. There's other
Speaker 1: stuff that I read about it that said like it
Speaker 1: was a there's creaking and activity and apparently because it
Speaker 1: was used as a hideout for Confederate soldiers, um, which
Speaker 1: I was like, it was Mobile, Alabama. Where why are
Speaker 1: they hiding out? But I I don't I guess I
Speaker 1: don't know what how that worked. As with all things
Speaker 1: in the South, is incredibly haunted, as with all things
Speaker 1: in America, it was incredibly hunted. But um, but nothing
Speaker 1: really weird happened while I was there. As I said,
Speaker 1: it was pretty chill and just sort of nice to
Speaker 1: be somewhere that was like not a chain hotel. It
Speaker 1: was just like pleasant. But a weird thing did happen
Speaker 1: when I there was like a portrait of like a
Speaker 1: Southern bell on the wall next to my bed, and
Speaker 1: I was just like lying in bed looking at my
Speaker 1: phone and then I just like aimlessly sort of put
Speaker 1: my phone up and tried to take a picture of it,
Speaker 1: and my phone would not take a picture of it.
Speaker 1: I tried and tried, and it did this weird glitchy
Speaker 1: thing that my phone has never done before, where like
Speaker 1: the image on it seemed to slow down. It wasn't like, um,
Speaker 1: it wasn't in sync with like how I was moving
Speaker 1: my phone around. It was sort of like there was
Speaker 1: a big delay and it was like in slow mo.
Speaker 1: And then I just turned it and I pointed it
Speaker 1: at another part of the room and it took a
Speaker 1: picture just fine. Then I went back to take a
Speaker 1: picture of the portrait and it couldn't take a picture
Speaker 1: of the portrait, so, um, I have like a blurry,
Speaker 1: dark photo that it tried to take but it would
Speaker 1: not get a proper photo. So that's as weird as
Speaker 1: it got. That's pretty weird. That's pretty weird. Yeah, but
Speaker 1: I wanted to I wanted a real encounter. I want,
Speaker 1: I want to believe. But I guess next time, whenever
Speaker 1: I make my trip back, I'll try to hit all
Speaker 1: the haunts haunted portrait good enough, Yeah, I'll take it. Um.
Speaker 1: Speaking of haunted things and and the cousin of haunted
Speaker 1: things witchy things, it's been a while since we've had
Speaker 1: a Moon minute. Minute. I think it's time for a
Speaker 1: Moon minute because baby witches were trying to hex them moon. Yeah, baby,
Speaker 1: which is from Witchtok, which is the witch part of TikTok. Yeah,
Speaker 1: everything comes from TikTok. Now, apparently this is just gonna
Speaker 1: pivot to be a hundred percent TikTok podcast because they're
Speaker 1: giving us lots of material recently. Yeah, I just thought.
Speaker 1: I also, like, I was not online a lot, but
Speaker 1: I just I did see a lot of stuff. Every
Speaker 1: time I would go on Twitter, people just being like
Speaker 1: what's up with the moon? And I didn't know what
Speaker 1: people were talking about most of the time. But then
Speaker 1: and then I caught onto this thing that was going
Speaker 1: on with with the baby witches, where apparently it became
Speaker 1: a thing on TikTok to hex the moon. Um. They
Speaker 1: first tried to hex the fae, which are like, uh
Speaker 1: like kind of fairies, like the fair folk Celtics specific
Speaker 1: fairy folks. Yes, yeah, and then and then they decided
Speaker 1: to try the moon. Uh. And it's not clear to
Speaker 1: me why other than for lulls um, which just feels
Speaker 1: like for the lulls feels like a very chaotic spirit
Speaker 1: to go into being a baby, which but I guess
Speaker 1: that's the nature of being a baby, which um. But
Speaker 1: people were getting really mad and there was a rumor
Speaker 1: floating around Twitter, which I have no idea if it's
Speaker 1: true or not, that a twelve year old girl was
Speaker 1: was killed after trying to hex the moon. I hope
Speaker 1: this feels very creepy pasta ish, but I don't so
Speaker 1: I have no I mean, if that's true, it's horrible.
Speaker 1: I also don't know if the moon is responsible, but
Speaker 1: either way, bad bad news. But um, but still like this,
Speaker 1: this kind of urban legends sort of grew up around
Speaker 1: these moon hexes that are going on. Well, the reasons
Speaker 1: why you shouldn't hex the moon are pretty straightforward. I guess,
Speaker 1: um it's because any well, anytime you do evil magic,
Speaker 1: it's going to come back to you and uh come
Speaker 1: back to you times three, they say. But hacking the
Speaker 1: moon also angers the gods that rule the moon, who
Speaker 1: are Apollo and Artemis. I think, um, Artemis rules the moon,
Speaker 1: Apollo rules the Sun, and Apollo is twins with Artemis.
Speaker 1: That's right, so he gets mad if you do anything
Speaker 1: to insult his sister. Yeah. But also so now the baby,
Speaker 1: which is we're saying they were going to curse the
Speaker 1: sun and if you mess with Apollo, apparently Apollo rules
Speaker 1: you know, the arts and creativity, but also healing and medicine.
Speaker 1: Uh so not a super smart thing to do if
Speaker 1: that time. Yeah, Um, personally I would not funk with
Speaker 1: the moon. Well the moon is I just don't. What
Speaker 1: did the moon do to you? The whon is a
Speaker 1: friend to all witches and other people. I'm just picturing
Speaker 1: that part in the craft where Nancy's like just overcome
Speaker 1: with how much power she has, you know, and you're like,
Speaker 1: why I can do anything? And that's maybe how you
Speaker 1: think you should hack the moon. I just I also
Speaker 1: don't if they're baby, which is can they even this is?
Speaker 1: This is the sort of cycle I get into it.
Speaker 1: Think eat about that this, like, how are they even
Speaker 1: being successful at hexing them? That they had come into
Speaker 1: their power yet? But yeah, like I don't I don't
Speaker 1: know how they would even be capable of doing such
Speaker 1: a thing. I just think if there are enough of
Speaker 1: them that the collective energy. But also Mary Anne Williamson
Speaker 1: weighed in on this, Oh yeah, yeah, which is funny. Yeah,
Speaker 1: she said, uh, that's got to be some really drunk
Speaker 1: or stoned hashtag baby, which is if they think that
Speaker 1: in the midst of a hashtag secret police invasion of Portland's,
Speaker 1: the best they can do is hex the moon all
Speaker 1: caps face palma, om gi uh, which is true. I
Speaker 1: don't know why these again, I don't get it. Like
Speaker 1: I remember when that like all the Bushwick, which is,
Speaker 1: we're trying to hex Donald Trump at one point, like
Speaker 1: you know, maybe in year one or two of the
Speaker 1: of the Trump presidency, and that I understood. I actually
Speaker 1: I feel that somehow if everybody, like enough people at
Speaker 1: the same time or concentrating on somebody's head exploding allegedly, uh,
Speaker 1: that that could happen. Maybe, Like I I do think
Speaker 1: that maybe there's energy that are you talking about scanners magic. Yes, yes, exactly,
Speaker 1: that's the only that's my Scannermancy, that's my that's my trade. Um.
Speaker 1: But uh yeah. But and so that felt, you know,
Speaker 1: like there was a clear political motive there. But the
Speaker 1: moon thing is beyond me. It's the chaos of the youth. Um.
Speaker 1: There's something else going on in the sky, which is well,
Speaker 1: they will no longer be going on in the sky
Speaker 1: when you hear this podcast. But there has been a
Speaker 1: comment that Emily has been trying to see I've been guys,
Speaker 1: haven't you guys haven't I haven't seen it. I haven't
Speaker 1: been able to see it. I haven't been anywhere dark enough.
Speaker 1: But the planets are really bright right now. Yeah. I
Speaker 1: saw Jupiter when I was driving U. I listened to
Speaker 1: you know that that syndicated MPR show where they just
Speaker 1: say what planets are out, and they said that Jupiter
Speaker 1: was out, So I like, I made a point to
Speaker 1: check out Jupiter because you could. It's it's like a
Speaker 1: very bright star. It's like a very bright, warm looking
Speaker 1: star when it's out. But it's it's always cool when
Speaker 1: you can see it. When I see the planets in
Speaker 1: the moon. The last thing I want to do is
Speaker 1: hex them. Yeah right, I wanna, I wanna get them whatever.
Speaker 1: We are pro planet on this podcast. Um, speaking of TikTok,
Speaker 1: Let's take a quick break and then we will be
Speaker 1: back with more TikTok updates. Welcome back to Night Call.
Speaker 1: We got a night email updating us on cottage Core,
Speaker 1: which we spoke about. It turns out without really knowing
Speaker 1: anything about it, which is something we're trying to be
Speaker 1: better about doing. But we love when you write in
Speaker 1: and tell us things that we didn't know. So here's
Speaker 1: the email we got. Hi, everyone, I was a little
Speaker 1: surprised by how you talked about cottage core, though I
Speaker 1: had heard rumbles it was starting to be co opted
Speaker 1: by fascists. Cottage core as I've encountered it on TikTok. Yes,
Speaker 1: I'm another gen Z listener is primarily a queer, specifically
Speaker 1: sapphic aesthetic. I think cottage cores appeal is a something
Speaker 1: of an escapist fan of See you and your partner
Speaker 1: can escape a homophobic, transphobic, misogynistic, capitalistic society by isolating
Speaker 1: yourselves in a cottage where you raise chickens and embroidered together. UM.
Speaker 1: Cottage core, though, can glorify history based on exploitation of
Speaker 1: black and indigenous folks, which is something you discussed on
Speaker 1: the episode, and I think is important to discuss in
Speaker 1: the context of its use as a queer aesthetic, especially
Speaker 1: since the prominent cottage core influencers are predominantly white. Thanks
Speaker 1: for all the work you do. Thanks so much, listener, Katie. Yeah,
Speaker 1: I don't. I still don't really have a grasp on
Speaker 1: on cottage core that this really helped out a lot.
Speaker 1: Uh And there was she also linked to uh article
Speaker 1: in paper about about just explaining cottage core. I don't
Speaker 1: remember if I said it on the pot or not,
Speaker 1: but like I kind of hypothesize that that a lot
Speaker 1: of Miyazaki movies are a cottage core just because of
Speaker 1: this sort of almost did how from history fetishization of
Speaker 1: like a kind of Bavarian slash Italian aesthetic, like a
Speaker 1: very Euro old world euro aesthetic, um like reappropriating it
Speaker 1: for his own kind of mythology and everything. I think
Speaker 1: there's always tons of that going on in all contexts.
Speaker 1: I think the people were kind of commenting on the
Speaker 1: fact that it does tend to be a pretty white aesthetic,
Speaker 1: which is what from from the little that I had
Speaker 1: seen I had also witnessed as well. But it is Walden,
Speaker 1: the original cottage Core. It's Henry David Thorreau, the first
Speaker 1: contin Yeah, because it's like that's the thing, Like, it's
Speaker 1: like the same thing as country music. It's like, you know,
Speaker 1: the myth of Walden and the then the myth of
Speaker 1: of actually going out into the wilderness is is kind
Speaker 1: of like inescapable from these things. Like country music was
Speaker 1: never really about anybody living in the country. It was
Speaker 1: all about like an imagined as a nostalgia for a
Speaker 1: country life, for like a rural lifestyle or something. Um So,
Speaker 1: I think that that's just like it's almost inescapable. Anytime
Speaker 1: you try to like codify a vibe that there's going
Speaker 1: to be some mythologizing there. Um Yeah. One thing I
Speaker 1: didn't mention last time, but there was like a like
Speaker 1: a Japanese subculture called Mori girls that I was like,
Speaker 1: is that what this is? That? Are that are forest girls? Totally? Yeah.
Speaker 1: That was like sort of the anti gothic Lolita, like
Speaker 1: very minimal like earth tones and likes clothing. Yeah, it's
Speaker 1: a very familiar Internet esthetic. It's a you know, in
Speaker 1: the in the family of the Autumn Lovers UM, and
Speaker 1: it came up a lot last night because of the
Speaker 1: new Taylor Swift album coming out. I was gonna say,
Speaker 1: is this not cottage core? Is she not going right
Speaker 1: into the cottage core. I'm just gonna say, tread carefully, Molly.
Speaker 1: Every time you mentioned Taylor Swift, you enrage her fans,
Speaker 1: and I don't want them coming for us. I I
Speaker 1: love Taylor, don't. It's not me. I welcome to this
Speaker 1: course with the Swift Eats. People are specifically like, UM,
Speaker 1: the band Muna, who I really love, UM did a
Speaker 1: treet where they were like, the Taylor Swift album is
Speaker 1: for like sapphic cottage core influencers. UM, because a lot
Speaker 1: of people are reading some kind of sapphic vibes into it,
Speaker 1: and UM, people are like because she wrote she wrote
Speaker 1: one song from the perspective of a of a male
Speaker 1: voice where it's um, you know, talking about girls. But
Speaker 1: people were sort of like, what's this all about? But
Speaker 1: I think also it's like, whether that is Taylor's intent
Speaker 1: or not. UM, it's definitely like resonating with a lot
Speaker 1: of people that are into the queer cottage chor aesthetic. Well,
Speaker 1: cottage corep also just is very timely obviously. Yeah, it
Speaker 1: makes I mean, it's a it's almost practical at this
Speaker 1: point that being trapped in your home but also so
Speaker 1: tethered to technology makes you consider that the only other
Speaker 1: option of being trapped at home that is more appealing
Speaker 1: than being tethered to technology is being trapped at home
Speaker 1: in kind of homesteading or like becoming involved in like
Speaker 1: plant matter and like digging in the mulch, but also
Speaker 1: looking kind of cute while you do it. Why not? Now,
Speaker 1: That's what I think is so funny when people like,
Speaker 1: you know, I've seen friends who have like gone and split,
Speaker 1: you know, a cabin somewhere for a weekend. Uh, And
Speaker 1: then there's always like a kind of sub tweetie energy
Speaker 1: of like, oh you went to a cabin for the weekend.
Speaker 1: It's like that's literally the only place you can go
Speaker 1: right now. Like, if you want to go anywhere that's
Speaker 1: not your home, you kind of have to go cottage
Speaker 1: or a little bit. I also just think it's funny
Speaker 1: because it is such a sort of like it's a
Speaker 1: fantasy of no technology in a lot of ways. But
Speaker 1: then it's on TikTok. Yeah, well, the thing's dark academia.
Speaker 1: That's there. That's I also was thinking, like the tailor
Speaker 1: swift aesthetic. I was like, is it light academia? Is
Speaker 1: that her aesthetic is non dark academia. She had a
Speaker 1: tweed coat in one of those photos, so that that
Speaker 1: kind of edges into dark academia. Terry people were people
Speaker 1: were saying, they were like, oh, this album is so autumnal,
Speaker 1: but everybody's trapped at home, so it doesn't really matter
Speaker 1: what season it is. You can be cozy, cozy cottage
Speaker 1: core in the summer, because what are you going to do?
Speaker 1: I think of cottage core is a much more a
Speaker 1: summary vibe. Yeah, because cottage core is the like linen
Speaker 1: dresses with the bit it's breathable and and dark academia
Speaker 1: is much more layer friendly cottage cores like Paul and
Speaker 1: in the air and you're wearing a dress and you're
Speaker 1: like digging up root or whatever. Yeah. Um, one of
Speaker 1: us is going to get on TikTok one of these
Speaker 1: days and actually figure out this ship for ourselves. I
Speaker 1: would be really curious as to what subculture can we
Speaker 1: just create a nightcall TikTok and share it. Somehow, I'm
Speaker 1: afraid to commit. We also got another email about our
Speaker 1: our COVID reality right now from a listener who writes, Hello, Nightcall.
Speaker 1: This email is two episodes too late. I'm just catching
Speaker 1: up on the episodes, but I thought I would send
Speaker 1: it anyway. I really appreciate when Emily talked about international
Speaker 1: students in relation to the on campus experience. As someone
Speaker 1: who works at a university and as an international student,
Speaker 1: I've seen the effect COVID Nightina has had on international students.
Speaker 1: When universities kicked out students in dorms, international students and
Speaker 1: other students without a network were placed in precarious situations
Speaker 1: because they have no family to turn to for support. Moreover,
Speaker 1: many international students where I live are essential workers, and
Speaker 1: new visa laws and immigration changes made their situation even worse.
Speaker 1: For me personally, I was accepted into my dream graduate
Speaker 1: program at n y U and was super excited to
Speaker 1: move to New York City and to have new exciting experiences. However,
Speaker 1: due to my family's experiences with US immigration and the
Speaker 1: harassment we face, I ultimately had to reject the offer.
Speaker 1: My experience is nothing compared to thousands of students who
Speaker 1: might have to leave the country due to ices online
Speaker 1: learning stipulation. Thank you so much. That letter was from Hussein. Uh,
Speaker 1: it's great to hear from somebody who had actual experience
Speaker 1: with this. Yeah. I think it's I mean, what is
Speaker 1: I vaguely keep catching up on what's been going on
Speaker 1: with um, with school, the entire notion of school. UM.
Speaker 1: I know that Uh, several universities sued the Trump administration
Speaker 1: for this policy. Uh where ice was you know, going
Speaker 1: to potentially deport students who were on a visa. So
Speaker 1: I mean, but I don't know if that goes for
Speaker 1: everybody or just for the people who are at those
Speaker 1: schools that are that are filing lawsuits. UM. It just
Speaker 1: seems so again, this like piecemeal approach to everything right
Speaker 1: now is just like so disorienting and so confusing and stressful,
Speaker 1: especially if you are somebody in this situation where you
Speaker 1: don't know if you are able to stay in the
Speaker 1: country but you still have to like finish school. You know.
Speaker 1: There are also so many, so many reversals every time
Speaker 1: a decision like this is made, and I think particularly
Speaker 1: when it comes to foreign students who are going to
Speaker 1: who are making these huge moves. It's not not a
Speaker 1: great position to be in if you know that any
Speaker 1: decision that is made could be easily reversed at any point.
Speaker 1: I mean, you making the commitment to make that kind
Speaker 1: of a move, um, under such precarious circumstances as at
Speaker 1: the impossible task. Yeah, it seems it seems like a
Speaker 1: lot of the stuff is the Trump administration will like
Speaker 1: declare something and then people have to spend a lot
Speaker 1: of time being like, wait, is that even legal? Um? Yea,
Speaker 1: and by the time you figure it out, it's just
Speaker 1: it's um. It's a terrible chaotic approach to everything, and
Speaker 1: the confusion I think is intentionally uh, definitely so uh man,
Speaker 1: thank you so much for writing in Hussein. That's a
Speaker 1: great perspective for us to have. We also have another
Speaker 1: call and by the way, if you would like to
Speaker 1: talk about your experiences as an essential worker or otherwise
Speaker 1: working during the pandemic, please give us a night call
Speaker 1: at one two four oh four six night. You can
Speaker 1: also email us at Night Call Podcast at gmail dot com.
Speaker 1: We are more than happy to keep you anonymous. UM.
Speaker 1: Just let us know at the top of your letter
Speaker 1: or or your experiences as a student, or just any
Speaker 1: stories about COVID times and trying to get stuff done
Speaker 1: under impossible circumstances. We would love to hear from you,
Speaker 1: and we're happy to keep you anonymous. So this comes
Speaker 1: from one of those anonymous writers they write. I work
Speaker 1: in a clinic for a surgeon in Orange County. He
Speaker 1: never stopped seeing patients. I got COVID the first week
Speaker 1: of March from a patient. I was out of work
Speaker 1: for six weeks and I'm still having breathing problems four
Speaker 1: months later. I had a second exposure three weeks ago
Speaker 1: with another patient who was positive. I ran a fever
Speaker 1: for three days and had to have two tests to
Speaker 1: make sure I was negative before I could return to work.
Speaker 1: We have screening questions that were supposed to ask patients,
Speaker 1: including if patients have traveled and if they have a fever.
Speaker 1: But even if they have traveled or if they have
Speaker 1: a fever, we haven't turned anyone away. I'm afraid I'm
Speaker 1: going to get sick again, and I'm concerned that I
Speaker 1: will bring it home to my family. Thank you. I
Speaker 1: just wanted to share this is so psycho as somebody
Speaker 1: who has been in doctor's offices more times than I
Speaker 1: would have liked to be in the last of five
Speaker 1: Oh my god, almost like five months now. Uh I.
Speaker 1: The first thing I had to do was get a
Speaker 1: tooth pulled, and they're why is that? You know? Over
Speaker 1: the phone when I made the appointment, there was that
Speaker 1: checklist like have you been out of the country? Do
Speaker 1: you have any of these symptoms? Um? And I, you know,
Speaker 1: being this is very early in lockdown. I the day
Speaker 1: of I kind of had a sore throat, but it
Speaker 1: was because of my tooth that was that was giving
Speaker 1: me problems, Like that's a that's a symptom of a
Speaker 1: of a kind of irritated wisdom tooth, as you can
Speaker 1: have a sore throat sometimes and so that I just
Speaker 1: started up that morning. So I went there to the
Speaker 1: office and I like was honest about that. I was like,
Speaker 1: I think it's just the tooth though, and they almost
Speaker 1: didn't let me in, like they were taking it super
Speaker 1: super seriously there at this um at this oral surgeon,
Speaker 1: which I appreciated a lot, they still saw me and
Speaker 1: like the sore throat went away as soon as the
Speaker 1: tooth was out, So that was totally what it was.
Speaker 1: But um, the fact that they're asking questions and then
Speaker 1: letting people in any way. It's like, what is what
Speaker 1: is the point. It's like people are not thinking about
Speaker 1: what the actual point of any of these safety majors
Speaker 1: are now. Um, and that's so upsetting, especially if you
Speaker 1: don't really have an option not to distance from people
Speaker 1: when you're a job like this, right, And it's also
Speaker 1: like you're taking people at their word, you know. I mean, yeah,
Speaker 1: this is is the lack of testing as well, UM,
Speaker 1: because like Emily said, so many people are having symptoms
Speaker 1: of anxiety or neglected problem, you know, health problems that
Speaker 1: you have to deal with later. Like I had a migraine, um,
Speaker 1: a couple of weeks ago, and my because I was
Speaker 1: having a migraine, my heart started racing and I have
Speaker 1: like the blood oximeter and then of course you look up,
Speaker 1: you know, you're just feeling like you have a migraine,
Speaker 1: You're feeling under the weather. And then I was like, oh, okay,
Speaker 1: well this all of this could also be related to COVID.
Speaker 1: But then it was really hard to get tested and
Speaker 1: I was very lucky to be able to. But I mean,
Speaker 1: when you interview people about problems that they're facing and
Speaker 1: for which they are seeing a doctor. There are, of
Speaker 1: course things like a fever that you can measure, but
Speaker 1: then there are also just general how are you feeling questions,
Speaker 1: And if you're seeing a doctor, chances are you're not
Speaker 1: feeling great. So you have to be able to know
Speaker 1: what's going on on an individual level before you see
Speaker 1: a patient. Otherwise you're putting everyone at risk, and that
Speaker 1: can really only be accomplished with a test. Yeah, I mean,
Speaker 1: I really feel for people that are working in doctor's
Speaker 1: offices and hospitals right now, because you need a hospital
Speaker 1: to be that safe zone. But yeah, there's so many
Speaker 1: factors that we don't have under control because we don't
Speaker 1: have testing, and there's so much protocol that we don't
Speaker 1: have yet either. Like I you know, I am way
Speaker 1: overdue for just a dentist checkup at this point, and
Speaker 1: I've been putting it off because I was like, it's
Speaker 1: not essential, like I had to get my tooth pull
Speaker 1: that I haven't had a dentist checkup in a while,
Speaker 1: and now it's just like, well, when when can I
Speaker 1: do that? It's like this, it's the same thing as
Speaker 1: like a regular checkup or getting a physical or something
Speaker 1: like that. It's like we I think when we everybody
Speaker 1: was under this assumption that it was only going to
Speaker 1: be for three months. It's like, okay, well I can
Speaker 1: put off a physical for three months, who cares. But
Speaker 1: now it's like, okay, if this is going to be
Speaker 1: going on for a year, a year and a half,
Speaker 1: like what what is the best practice in that case
Speaker 1: just to not take care of yourself? Like and there's no,
Speaker 1: there's no there's been no guidance around that from any
Speaker 1: of that at all, especially here where there are things like,
Speaker 1: for instance, I was talking with someone um and they
Speaker 1: weren't able to get their contact lens prescription because it
Speaker 1: was like a month, you know, past its expiration date.
Speaker 1: In Canada, my friend said, you can if you're slightly
Speaker 1: past your prescription date, and there are circumstances like this,
Speaker 1: they will just send you your contact lenses. But because
Speaker 1: they're classified as something that almost like is ingestible, they
Speaker 1: go like inside your body, then they are regulated, so
Speaker 1: it's she has to go out now to the eye doctor.
Speaker 1: And there's things like that where it's like, you know,
Speaker 1: for certain types of medications that require a doctor's visit,
Speaker 1: the patient might want to not take the chance on
Speaker 1: exposing their doctor, but then they're not going to get
Speaker 1: them at cation that they need. So there are all
Speaker 1: of these complicating factors that might make patients go see
Speaker 1: the doctor even if they weren't able to get a test,
Speaker 1: even if they didn't feel great. And that's such a
Speaker 1: terrifying predicament because there's really no right way to handle it.
Speaker 1: And I think we're also seeing now like the three
Speaker 1: months thing. I feel like we always kind of thought
Speaker 1: was bullshit. It was just like to buy people enough
Speaker 1: time because they can't think beyond like three months to
Speaker 1: something everyone can handle. If they had said it was
Speaker 1: going to be a year or two, people would have
Speaker 1: flipped out more. I definitely felt strongly that it was
Speaker 1: going to be a year, uh, just from the get
Speaker 1: really yeah, I did not, because I was like, they're
Speaker 1: they're trying to be comforting, but like nothing they're saying
Speaker 1: has any concrete plan, you know. But I didn't think
Speaker 1: that even even as somebody who thought it was going
Speaker 1: to be a year, I was still like, five months
Speaker 1: from now, will at least have testing or something, you know,
Speaker 1: we'll have like regulated testing. Um. And the fact that
Speaker 1: we are five months into this and it's still so
Speaker 1: ticky tack and doesn't seem like it's going to get better.
Speaker 1: And if you get a test, there's still enough of
Speaker 1: a delay in getting your results that it's it's so
Speaker 1: much less useful than the rapid results tests, which I
Speaker 1: think are still like a hundred and twenty five dollars
Speaker 1: here um that you can get an urgent care and
Speaker 1: are those accurate ones? Like, I think that those are
Speaker 1: more accurate. I've heard if people who were Yeah, the
Speaker 1: nose is more is more accurate, The nose swab is
Speaker 1: more accurate. The cheek swabs, though there have been instances
Speaker 1: of people um getting positive cheek swab test results and
Speaker 1: they never actually swabbed their cheeks. They like got in line,
Speaker 1: had an appointment turned around because of the weight was
Speaker 1: too long or whatever, and then they get a caller
Speaker 1: an email that they tested positive and they were like, no,
Speaker 1: I didn't. Yeah. The l A swab testing is being
Speaker 1: run through a nonprofit uh, created by Sean Penn. Is
Speaker 1: that Curative Care or whatever? Yeah, So again, it just
Speaker 1: seems like the privatized nature of all these things as
Speaker 1: part of the problem, um that we don't just have
Speaker 1: nationalized healthcare where everybody could get tested. That would be great,
Speaker 1: That would be nice. Well, we're going to take a
Speaker 1: break and when we come back, we're going to be
Speaker 1: joined by our guest this week, Meredith Whittaker, to talk
Speaker 1: about UH, surveillance, UH, labor rights, artificial intelligence so called UH,
Speaker 1: and so much more. So stay tuned for that welcome
Speaker 1: back tonight call. We are joined today by our very
Speaker 1: special guest, Meredith Whittaker. Meredith is a research professor and
Speaker 1: the co founder of the AI Now Institute at ny U.
Speaker 1: Her work focuses on the social implications of artificial intelligence
Speaker 1: and the tech end street responsible for it. Previously, she
Speaker 1: worked at Google for thirteen years, where she founded the
Speaker 1: company's Open Research group and helped lead labor organizing efforts,
Speaker 1: including the Google walk out and efforts to end AI
Speaker 1: contracts with the U S Department of Defense. Her work
Speaker 1: is driven by the belief that worker power, collective action,
Speaker 1: and strong social movements are necessary to ensure meaningful tech accountability.
Speaker 1: Welcome Meredith, Thanks so much for being with us this week. Yeah,
Speaker 1: I'm happy to be here. Um. Uh Yeah, So I
Speaker 1: feel like we we constantly are saying among ourselves that
Speaker 1: we wish that like we had a kind of a
Speaker 1: an info SEC expert on call at all times. So
Speaker 1: you are going to be our info SEC expert for
Speaker 1: the next thirty minutes. UM, So just strap in. UM.
Speaker 1: I think that like maybe a good way to kick
Speaker 1: it off is just talking about kind of the specific
Speaker 1: concern recently about UM about surveillance specifically at protests and
Speaker 1: among activists. UM, particularly in Portland, there's been concerns that
Speaker 1: I know you've been talking about on your Twitter about
Speaker 1: just sort of the kind of murky waters of of
Speaker 1: of capturing face data and other kinds of data from
Speaker 1: these events. UM. It's it's like a weird new world
Speaker 1: that we're all navigating, and unfortunately there's like uh proto
Speaker 1: fascist slash fascist forces that are taking advantage of that
Speaker 1: uh not quite figured out world. So UM, yeah, is
Speaker 1: there anything particularly from that that that has been surprising
Speaker 1: to you recently? Yeah, I mean let's yeah, let's let's
Speaker 1: open up the whole cannon. Or yeah, I don't. I mean,
Speaker 1: I think surprise isn't the word, right. I think we
Speaker 1: know that the tools of centralized power social control surveillance exists,
Speaker 1: and frankly have existed for some time. I think what
Speaker 1: we're beginning to recognize is what they look like in
Speaker 1: the hands of you know, I think proto fascist is
Speaker 1: a bit of a diplomatic term for what we're seeing
Speaker 1: with the you know, the the federal troops that you know,
Speaker 1: hs as involvement, um. You know. But but you know,
Speaker 1: I think I don't think I'm surprised. I think this
Speaker 1: is it's it's extremely troubling, right and a lot of
Speaker 1: what you know, my senses with a lot of people
Speaker 1: who are sort of historicizing this moment looking back at like, well,
Speaker 1: this is you know, this is how fascism looks when
Speaker 1: you know, suddenly there is a you know, there are
Speaker 1: kind of a domestic militia that is serving you know,
Speaker 1: the the goals of the you know, authoritarian party on
Speaker 1: the streets. Um. You know, what we haven't seen before
Speaker 1: is fascism with the types of surveillance affordance. Is that
Speaker 1: these folks have, right with the ability to you know,
Speaker 1: buy location data and other intimate data from you know,
Speaker 1: data brokers, with the ties that companies like Talents here
Speaker 1: and others have directly to the Trump administration, with the
Speaker 1: sort of you know, the way in which we have
Speaker 1: now sort of structured are our social and political lives
Speaker 1: around the necessity of having these networked infrastructures that are
Speaker 1: necessarily collecting you know, data about us, data, you know,
Speaker 1: making inferences about us. Um you know, whether or not
Speaker 1: we call that surveillance, whether or not we call having
Speaker 1: you know, a private company own all of our Gmail
Speaker 1: going back for years and years surveillance. You know, that's
Speaker 1: the bedrock on which all of this network technology and
Speaker 1: the industry that has been built around it is is resting.
Speaker 1: And we're now saying, you know what happens when the
Speaker 1: you know, happy primary colored promises of tech you know,
Speaker 1: actually aren't kept right. UM. So, so here we are. UM.
Speaker 1: But you know, again this isn't that we saw this
Speaker 1: in Ferguson, you know, standing Rock. A number of these
Speaker 1: you know dynamics were at play. We felt with the
Speaker 1: pandemic powers that were given to Ice by Trump that
Speaker 1: they now sort of you know, this is permitted under
Speaker 1: those powers for these um you know, for for federal
Speaker 1: agents to be deployed in this way, for them to
Speaker 1: use you know, drones and other surveillance capabilities to you know, track,
Speaker 1: and what we're seeing is sort of you know, harass harm. Yeah,
Speaker 1: I mean it's like ironically, as in all the ways
Speaker 1: that the the the uprisings right now are taking place
Speaker 1: in the same way at the same time as the pandemic.
Speaker 1: It's like at the same time that we're the other
Speaker 1: countries have enacted contact tracing and that sort of thing,
Speaker 1: and that's something that we're talking about here. It's like
Speaker 1: we also see we're we're immediately living through the downsides
Speaker 1: of something like contact tracing um currently, as opposed to
Speaker 1: like a hypothetical that could happen sometimes. Yeah, I mean,
Speaker 1: contact tracing is a great example of just like how
Speaker 1: wild tech hubris is, right because we are, you know,
Speaker 1: the foundation we are in a failed, failing, present progressive
Speaker 1: state right now, right Like, we don't have testing that
Speaker 1: is clearly deployed. We certainly don't have a social stafety
Speaker 1: net that would enable people to make a sort of
Speaker 1: choice about their protection, right Like, oh, maybe you know
Speaker 1: I have received a ping that I was in contact
Speaker 1: with someone who tested positive, which presumes the presence of tests, right, Um,
Speaker 1: and now I need to stay home for two weeks, right,
Speaker 1: you know, most people don't even have two weeks vacation
Speaker 1: time to take let alone you know the you know,
Speaker 1: you know the resources to you know, make that choice
Speaker 1: for you know, community and self care. So I think
Speaker 1: what we're looking at, you know, the idea that you
Speaker 1: could just slap contact tracing on that when you don't
Speaker 1: have ground truth data around testing, when people aren't in
Speaker 1: a position to you know, make a choice for public health,
Speaker 1: when we don't have those politics of care right you
Speaker 1: know anywhere in our infrastructures. Is you know, that's the
Speaker 1: kind of like we'll solve it with tech while ignoring
Speaker 1: those sort of bedrock conditions that would be you know
Speaker 1: necessitate you know that that are necessary for tech too,
Speaker 1: you know, for us even have a conversation of whether
Speaker 1: sort of tech based contract trasing contact tracing could work,
Speaker 1: and there's a bunch of problems with tech based contract tracing,
Speaker 1: especially Bluetooth BASTE. It like doesn't know when it's going
Speaker 1: through walls so you can get a contact trace that's
Speaker 1: like if you're next to somebody in a car, it
Speaker 1: will consider you next to them, right. Like, there's all
Speaker 1: sorts of like you know, real world lizations to the
Speaker 1: hype that is being sort of you know floated around
Speaker 1: these these approaches, and you know, and again you need
Speaker 1: you need test data to be using test data to
Speaker 1: do contact tracing, and you need you know, frankly, contact
Speaker 1: tracing is best done by like an army of human
Speaker 1: beings who are doing the hard work of of you
Speaker 1: know what you know, public you know, doing the work
Speaker 1: of public health experts. Yeah, yeah, Meredith. One thing I
Speaker 1: really love about your work you use the word tech
Speaker 1: hue breast Um. I think you're really good at calling
Speaker 1: out tech hubris, and especially at sort of talking about
Speaker 1: promises that tech companies make that they can't actually deliver
Speaker 1: on and tech that doesn't exist that's being sold. Um.
Speaker 1: You had a good tweet the other day about uh
Speaker 1: software that was being sold I think to the Pentagon
Speaker 1: that was supposed to recognize human emotions. And you said,
Speaker 1: let me save you a billion dollars and tell you
Speaker 1: that this doesn't work. Surprised. Oh, it's so, y'all, it
Speaker 1: is so it. I mean, there's that we've we've had
Speaker 1: twenty years, I think, and and more. I'm not you know,
Speaker 1: I'm not a trained historian in this, but I've like
Speaker 1: came up in fact, right like we have just sort
Speaker 1: of trusted this narrative that you know, tech company revenue
Speaker 1: gains are you know, are synonymous with progress, right, with
Speaker 1: scientific progress, with social progress, with sort of you know,
Speaker 1: the betterment of the world. And I think we all
Speaker 1: know that's more or less bullshit, but I think it
Speaker 1: has combined with this kind of you know, the way
Speaker 1: in which tech supremacy has become part of our are
Speaker 1: just general narrative, right, and people are afraid to question
Speaker 1: these technologies, right you see, you know, there's there's this
Speaker 1: constant kind of derisive commentary on Twitter, like if only
Speaker 1: senators were smart enough to question Zuckerberg, right, But like,
Speaker 1: we are in a context where, for some reason, we
Speaker 1: made it okay for people with a sort of elite
Speaker 1: technical education that had access and expertise a certain information
Speaker 1: most people don't to sort of, you know, assume that
Speaker 1: the right to make significant policy decisions, significant social decisions,
Speaker 1: significant decisions on behalf of you know, millions and billions
Speaker 1: of others, you know, rested with the you know, with
Speaker 1: only um that that only people with this training sort
Speaker 1: of had that right and that no one who you know,
Speaker 1: if you didn't have a CS degree from Stanford, you
Speaker 1: can't actually be talking about these things. So I think,
Speaker 1: you know, under that sort of smoke screen. You know,
Speaker 1: so much of this stuff is a scam, right, that
Speaker 1: emotion recognition stuff like that is being piped into every
Speaker 1: market you could imagine, Right, there's job interview software that
Speaker 1: based on your tone of voice, based on your micro expressions,
Speaker 1: based on you know, how you you sound, what your
Speaker 1: mannerisms are, will determine if you're going to be a
Speaker 1: good worker or not. Right, Like, we're looking at something
Speaker 1: there's no scientific consensus around this that you can automate
Speaker 1: that type of detection, and you're looking at sort of
Speaker 1: the resurgence of principles of physiognomy and race science and
Speaker 1: this idea that someone's worth can be determined by their
Speaker 1: sort of you know, external physical characteristics, which you know
Speaker 1: hasn't there been mentioned of that, Like then using that
Speaker 1: sort of thing for policing too, or am I misremembering
Speaker 1: because I feel like it's it's you know, one of
Speaker 1: the issues where you know, people are like give us
Speaker 1: specific evidence of this. And one of the issues is
Speaker 1: that a lot of the technologies that are being used
Speaker 1: are produced by private firms and then they're procured by
Speaker 1: governments or private businesses, and that whole process is either
Speaker 1: you know, if it's if we're talking about like policing agencies,
Speaker 1: often times it's classified or hidden based on national security.
Speaker 1: If we're talking about private firms, it's hidden behind trade secrecy.
Speaker 1: So we know some examples, but I think it's it's
Speaker 1: also important to recognize, like it is that the obscurity
Speaker 1: is structural. Right, it's intentional, and there's a lot we
Speaker 1: don't know, but we do know that. You know, police
Speaker 1: departments are being sold facial recognition systems that claim to
Speaker 1: sort of detect aggression in people's faces, and obviously when
Speaker 1: researchers have looked at these, they are like extraordinarily racist, right,
Speaker 1: or looked at similar systems, right, because getting access to
Speaker 1: some of these is also hard because of what I
Speaker 1: just mentioned. But yeah, policing is using it. The military
Speaker 1: is like the military just a big old magic that
Speaker 1: the military is certainly using it. Uh, schools are using it. Uh,
Speaker 1: how it's wait, in what contexts are schools using it?
Speaker 1: Are our students attempted? Of right? Are they paying attention?
Speaker 1: It is a bed racket that is funneling um money
Speaker 1: for help for a sort of security that was kind
Speaker 1: of pumped into school districts following, you know, because of
Speaker 1: the school shooting problem, and that money is now being
Speaker 1: used to buy sort of security cameras to you know
Speaker 1: that that are yeah, and and at police to schools, um,
Speaker 1: but a lot of those, you know, like detect suspicious activity. Again,
Speaker 1: this is sort of the inference that what your body
Speaker 1: looks like and what it does can somehow translate into
Speaker 1: a magical assessment of uh, you know, of criminality. That
Speaker 1: is so terrifying. I wanted to ask you also, just
Speaker 1: like what especially when you think about how that could
Speaker 1: also be applied now that students are going to be
Speaker 1: doing remote learning and they're all going to be on
Speaker 1: zoom and I mean, it's the idea that that could
Speaker 1: kind of evolve in an even more sinister ways it is.
Speaker 1: There's online proctoring software that now sort of you know,
Speaker 1: claims to detective insiveness, but we're talking about yeah, yeah.
Speaker 1: I also wanted to ask if there was a point,
Speaker 1: if you, so you've been in the tech world for
Speaker 1: a long time, what was there a kind of evolution
Speaker 1: for you At a point did you become kind of
Speaker 1: like more concerned and more aware of these issues and
Speaker 1: when you entered the tech world, like how aware were
Speaker 1: you of the kind of like ways that this could
Speaker 1: be manipulated in racist or really you know, to like
Speaker 1: eliminate you know, the personal privacy of users? Like when
Speaker 1: did did you kind of become aware of that in
Speaker 1: this sense? Mm hmm, I mean I don't have like
Speaker 1: a there wasn't one crisp revelation. But I you know,
Speaker 1: I started at Google in two thousand six, right out
Speaker 1: of undergrad and I took the job at Google because
Speaker 1: they offered it to me and I was broke, right,
Speaker 1: so like for me, it was very it was very simple, right,
Speaker 1: like rich kids went to grad school and that I
Speaker 1: was not one of them, and I needed a job
Speaker 1: because there was a certain amount of money at my
Speaker 1: bank account and it needed to be more money if
Speaker 1: I so like, um, you know, I I also, I mean,
Speaker 1: I don't know, I come from a class of where
Speaker 1: like the point of a job was to do the
Speaker 1: least amount of work and get the most amount of money.
Speaker 1: And there wasn't this sort of identity like your job
Speaker 1: was not who you were or your passion necessarily, so
Speaker 1: I kind of entered it like that, right and then
Speaker 1: and then I was just you know, and I did
Speaker 1: come from I kind of it's been involved in sort
Speaker 1: of radical politics, radical spaces as a teen, and that
Speaker 1: was sort of where I was at home. And so
Speaker 1: there was a whole wild time of being kind of
Speaker 1: entering Google and being like what is this? Right? Like,
Speaker 1: I don't have technical training. I had to figure out
Speaker 1: a lot of these things by like asking people, um,
Speaker 1: and I think part of that process was like a
Speaker 1: demystification journey for me. So what that you just call
Speaker 1: it that, but that's actually not like that's just a
Speaker 1: confusing word. Okay, I'm like and recognizing that it wasn't magic,
Speaker 1: that a lot of the ground truth was really shaky,
Speaker 1: that a lot of this sort of you know, a
Speaker 1: lot of this was sort of heights. But that took
Speaker 1: me a while, I think, you know, just being like
Speaker 1: asking these questions and sort of being in the like
Speaker 1: arriving at this time where it was just there was
Speaker 1: little pockets of kind of academic tech critique, but there
Speaker 1: was not the sort of wholesale kind of skepticism that
Speaker 1: I think is you know now part of a you know,
Speaker 1: a healthy conversation about tech, right, And so just saying
Speaker 1: like why do these guys you know, think they're ethical billionaires?
Speaker 1: Like what is ad tech? Right? Like what does it
Speaker 1: mean to sort of have this data? What are the
Speaker 1: excuses they use? Um? So that was I mean that
Speaker 1: was thirteen years. So like over that time, I was
Speaker 1: like interested in social issues, right, Like that was where
Speaker 1: my work gravitated. I worked on privacy, I was on
Speaker 1: that neutrality, I worked on a lot of these other things,
Speaker 1: and I got interested in AI because AI is sort
Speaker 1: of like, I don't know, I have a whole feel
Speaker 1: all save for some other time around it kind of
Speaker 1: it's kind of a scam. It's like this big term
Speaker 1: under which like data based technologies, right, technology is that
Speaker 1: make inferences about data? Are you know, kind of marketed
Speaker 1: right now? And there's a number of reasons that sort
Speaker 1: of emerged in the last ten years around sort of
Speaker 1: you know se tech company sort of consolidation of power,
Speaker 1: the consolidation of data, the consolidation of powerful infrastructures, whatever.
Speaker 1: But you know, the mythology around artificial intelligence that it
Speaker 1: was sort of infallible, that was actually you know, more
Speaker 1: capable than humans, that it was you know, it was
Speaker 1: you know, going to solve any number of you know,
Speaker 1: so dual purpose. It could solve any number of problems
Speaker 1: from healthcare to schools to you know, policing. Um I
Speaker 1: think was you know, I was really concerned about that
Speaker 1: because I was like dealing with issues of like how
Speaker 1: clunky and fallible data was, right, Like how you know,
Speaker 1: even when there was a lot of intention behind trying
Speaker 1: to like get data right, it was always like clergy.
Speaker 1: It was always just an artifact of like institutional processes
Speaker 1: and compromises and perspectives. Right. It wasn't there was no
Speaker 1: you know and run to get sort of that cart
Speaker 1: hesion window of objectivity, right. But people were building these
Speaker 1: systems on you know, crappy data with crappy assumptions and
Speaker 1: selling them a sort of magic into some of the
Speaker 1: most sensitive social domains you could imagine. And it was
Speaker 1: you know, it was consolidating power. It was threading these
Speaker 1: you know, it's like a hand five companies have the
Speaker 1: resources to make this kind of technology at scale right now,
Speaker 1: So it's sort of threading all of our social and
Speaker 1: economic infrastructures through these five companies whose core function as
Speaker 1: always forever and nothing else but to grow revenues increasingly
Speaker 1: infinitely over time, right, Like I think I think that
Speaker 1: when people think about artificial intelligence and machine learning and
Speaker 1: and you know, the kind of using machines, are using
Speaker 1: uh any kind of tech to replace a workforce. You know,
Speaker 1: we think about like a labor workforce or industrial jobs
Speaker 1: and that kind of thing. But I think that kind
Speaker 1: of we're seeing more and more the core philosophy of
Speaker 1: that and the actual failings of it, of the failings
Speaker 1: of the um of taking for granted that it's better
Speaker 1: to not have a human do a job, or that
Speaker 1: it's somehow more advantageous, because when you're talking about stuff
Speaker 1: like teaching, policing, all these things that we've have been
Speaker 1: talking about, like those are those are cases where they
Speaker 1: are like, it's good to have a human there, it's
Speaker 1: good to build human skills like those sort of intangible
Speaker 1: skills are are priceless. And that's just not a part
Speaker 1: of the tech philosophy, like the big tech philosophy right now.
Speaker 1: Um so that I've seen. Yeah, it also feels to
Speaker 1: me like the people who are in charge of tech
Speaker 1: are part of the reason that this is all being
Speaker 1: handled so badly, because you know, in my experience, all
Speaker 1: the people who have been like the Canary and the
Speaker 1: coal mine, have been people like you and other like
Speaker 1: women and especially women of color working at tech companies
Speaker 1: who noticed that, like the algorithm is racist and that
Speaker 1: we shouldn't be selling it as like the subjective thing um.
Speaker 1: But there's a lot of money obviously behind people sort
Speaker 1: of being like, nope, it's perfect algorithms. You know, the
Speaker 1: math is infallible and this is math. Yeah, they love it, right,
Speaker 1: And I think, I mean, I think one thing around
Speaker 1: that as well, as you're having this sort of confluence
Speaker 1: of like that type of tech huberis and these sort
Speaker 1: of you know, these kind of hollow marketing claims on
Speaker 1: the tech company side, and then you're having you know,
Speaker 1: decades of neoliberal austerity on the other side, where the
Speaker 1: offer of replacing a hundred humans with a machine that
Speaker 1: you only have the license for so much is really tempting, right, Like,
Speaker 1: and I think there's a sort of you know, there's
Speaker 1: a feedback loop there that's extremely dangerous. Like you had
Speaker 1: in Michigan, there was a they installed an algorithmic system
Speaker 1: that was replacing unemployment adjudication, so it would they used
Speaker 1: to have a staff of people saying like, oh, this
Speaker 1: you know, unemployment benefits claim. Uh, looks like frauds, right,
Speaker 1: and we're gonna chase that down. They instead passed the
Speaker 1: machine to do that fired all those people. Forty thousand
Speaker 1: people were wrongly accused, right, and then that creates a
Speaker 1: record that means you can't get benefits, you can't get
Speaker 1: another job, right, like they were bankruptcies. There were suicides
Speaker 1: like people, And that's like one example of this type
Speaker 1: you know, the way this type of logic is operationalized,
Speaker 1: like in Michigan, we need to also name like Rick
Speaker 1: Snyder and you know, the person who presided up a
Speaker 1: flint like that was the logics that are sort of
Speaker 1: driving this. And then of course there's a company on
Speaker 1: the other end, like welling To willing to sell some
Speaker 1: like you know, Jankie bullshit that hasn't been audited because
Speaker 1: no one involved in that sale and that propurement and
Speaker 1: those marketings are going to be the people who are
Speaker 1: like harm by it, right, and the people are subject
Speaker 1: to it, you know, have no freaking idea that there's
Speaker 1: an algorithm on the other end because they were never
Speaker 1: part of that process. There was no democratic decision making.
Speaker 1: So well, that's also you had testified before Congress Um
Speaker 1: at some point to talk about the need for over
Speaker 1: site and transparency and AI I think that that ties
Speaker 1: into this because there's we we just are never like
Speaker 1: a victim of that kind of thing, is never necessarily
Speaker 1: aware of the fact that this decision is being made
Speaker 1: by a boat basically um. And so it becomes I
Speaker 1: think harder to figure out how to address those things
Speaker 1: if they're if they happen wrongfully, because there's no kind
Speaker 1: of chain to follow up. But I wanted to talk about,
Speaker 1: you know, when when you kind of bring attention to
Speaker 1: these things and in your activism, you know, what kind
Speaker 1: of challenges are faced by people who come forward about
Speaker 1: these Like I know that you know, after the Google
Speaker 1: walkouts and everything that that was pretty contentious, and you know,
Speaker 1: I feel like generally it seems as though people who
Speaker 1: bring attention to these issues in big tech are really
Speaker 1: kind of facing an uphill battle. So, I mean, how
Speaker 1: do we kind of advocate for people who are whistleblowing
Speaker 1: or who are advocating for changes to be made? Yeah,
Speaker 1: I mean I think like it's no surprise, right of
Speaker 1: you know, they were fine with me when it was
Speaker 1: just me as a critic saying things right, and even
Speaker 1: if I called them out, even if I drag them like,
Speaker 1: you know, I might get some of me like come
Speaker 1: on there, but like they actually like that served a purpose, right.
Speaker 1: It made it look like Google was a place that
Speaker 1: accepted all voices and you know, a robust debate even
Speaker 1: if I had no power to actually make a decision
Speaker 1: based on my research and my insights. Um. It was
Speaker 1: when I started organizing, so you know with my colleagues,
Speaker 1: when you actually start building power, which requires solidarity in
Speaker 1: power asymmetric situations. Um. That they were upset, right, and
Speaker 1: it was classic. It was there was no you know,
Speaker 1: there was no tech wizard reinvolved, right, like they got
Speaker 1: some old quirky union bus name firm and to be
Speaker 1: like you hurt the people leading the people might organize, right,
Speaker 1: Like it's you know, it's pretty simple. And I think,
Speaker 1: you know, recognizing that fundamentally this is about power. Fundamentally,
Speaker 1: there's nothing particularly smart or different about tech, although the
Speaker 1: way it's you know, it masks and centralizes power some
Speaker 1: and we need to recognize when we're sort of in
Speaker 1: these struggles and doing this research, um, but that you know,
Speaker 1: it's going to require solidarity. It's going to require sort
Speaker 1: of new forms of solidarity in my view, that are
Speaker 1: kind of across you know, what people have called supply
Speaker 1: chains or you know, seemingly disparate sites. So if we're
Speaker 1: just looking at workers that receive a full time employment
Speaker 1: check from Google, we are not looking at a coalition
Speaker 1: that has the power across all of the domains where
Speaker 1: Google is threaded to actually check them. Right, So we
Speaker 1: need to be thinking about, you know, what does it
Speaker 1: mean to be a worker, What does it mean to
Speaker 1: be in solidarity as a worker, and what does it
Speaker 1: mean to be in solidarity with the communities, like you said,
Speaker 1: who are subject to these technologies that we as workers
Speaker 1: may not be subject too because of certain privileges. Right, So, like,
Speaker 1: how do you you know, how do you allow those
Speaker 1: movements to actually lead? If you're talking about sort of
Speaker 1: worker solidarity actions and I think you know, there are
Speaker 1: a million ways to support it's just you know, support
Speaker 1: like big where you stand. Yeah, I'm so curious because
Speaker 1: you you said you you started a Google in two
Speaker 1: thousand six, right, and you've been working in tech since then,
Speaker 1: And I feel like that kind of that time period,
Speaker 1: especially with you leading the walkouts. It's like the the
Speaker 1: the attitude and the and the trust that the average
Speaker 1: person has in tech companies and their understanding of what
Speaker 1: they are and and the fact that they are about
Speaker 1: power and in many cases of fruscating it by like
Speaker 1: giving you free email or whatever the case may be.
Speaker 1: It's like now you're in control when really, like you,
Speaker 1: like at Google, by having the slogan be don't be evil. Sure, yeah, yeah,
Speaker 1: like all of that, like kind of having the scales
Speaker 1: fall from our eyes over the past decade or something.
Speaker 1: I mean, what's been your experience of that from the
Speaker 1: inside and especially among the people who work there, because like,
Speaker 1: that's that's been the crux of it, right, is that
Speaker 1: people are like, oh, wait, this is not like I
Speaker 1: don't know if I signed up for this, Like I
Speaker 1: don't know, I don't. I mean, I think I had
Speaker 1: a little bit of a head start just because I
Speaker 1: was sort of I was, you know, I don't think
Speaker 1: it's an accident that the people who have sort of
Speaker 1: lad the research and the critique and sort of the
Speaker 1: you know, the insights were drawing on as sort of
Speaker 1: tech critics or whatever you call us, um are women
Speaker 1: of color generally, right, Um, you know there is something
Speaker 1: about you know, coming into those spaces and you know,
Speaker 1: always already not belonging because of how white they are,
Speaker 1: because of how you know, these are deeply racist, deeply
Speaker 1: misogynist spaces, like you know, some of like wildly so
Speaker 1: in some cases. Um, I think you know, there's sort
Speaker 1: of a sedimentary a cruel of experiences that has happened.
Speaker 1: Like people talk, there was always a whisper network. There
Speaker 1: always is a whisper network, right, Um, you know I'm
Speaker 1: not you know, a lot of things sort of happened
Speaker 1: and changed. Like when I started in two thousand and six,
Speaker 1: there were you know, somewhere between three and five thousand
Speaker 1: workers at Google. Right there are over two or thousand now,
Speaker 1: and you know that is made up of half, you know,
Speaker 1: more than half of those are contractors and temps, which
Speaker 1: is also the trend of sort of you know, Actually,
Speaker 1: work isn't safe for anyone. There is no such thing
Speaker 1: as non precarious work. And you know, if it hasn't
Speaker 1: happened to you yet, it's happening to you. Right, there's
Speaker 1: a whole you know, historical changes Trump happened, right, you know,
Speaker 1: Facebook cameradge Analytica happened. There's been you know, both a
Speaker 1: swell in the power of these companies and at the
Speaker 1: same time, so I'm just like kind of egregious and
Speaker 1: embarrassing disasters. Um. So I you know, I don't think
Speaker 1: it's any one thing. I think all of those things
Speaker 1: that you know, I can speak at Google. The thing
Speaker 1: that I think more than more than any other one
Speaker 1: event sort of shifted the tide was the Naven contract.
Speaker 1: And this was a secretive contract that Google had with
Speaker 1: the Department of Defense to build you know, drone targeting
Speaker 1: and surveillance AI for you know, for the drone program, right.
Speaker 1: But it was facial and object recognition. Is that right?
Speaker 1: It was object recognition. Um. It had a sort of
Speaker 1: person category when we look through the code, right, like
Speaker 1: it's you know it is and it's trained on images
Speaker 1: from you know, from the Middle East. Like again, we
Speaker 1: don't know. But I think this also gets to like
Speaker 1: there's an idea that you know a lot of the
Speaker 1: people who are sort of pushing back against our descent
Speaker 1: there against our call to cancel this Maven contracts with
Speaker 1: maybe with the name for it. Um. You know, we're
Speaker 1: saying like, you know, well, the best technology will actually
Speaker 1: you know, save lives, right, Like, how are you withholding
Speaker 1: this from the people who need it? What you're doing
Speaker 1: is actually sort of you know, misguided, and you're going
Speaker 1: to cost lives because they don't have the intelligence they need.
Speaker 1: And somehow automation will help that, but you know, it
Speaker 1: doesn't answer the fundamental question like your your data is
Speaker 1: only good as your impel Right. There isn't a magic
Speaker 1: you know, amount of data that's going to tell you
Speaker 1: more than what you know. And this data is always
Speaker 1: always irreducibly labeled by human beings, right, it only reflects
Speaker 1: those insights and so when you know again, I think
Speaker 1: this gets back to an earlier point around the sort
Speaker 1: of like eliminating or displacing jobs. It just I think
Speaker 1: it it degrades and hides those jobs. Right, But there's
Speaker 1: always an army of precarious workers who are labeling that data, right,
Speaker 1: saying like this is a you know, this is a house,
Speaker 1: this is the sheep, this is a person that trains
Speaker 1: the machine learning algorithm um, and it never gets better
Speaker 1: than that. Right, Those are the the core subjectivities that
Speaker 1: are embedded in that data. There is no sort of
Speaker 1: magic wand right, it's all people all the way down.
Speaker 1: But it obscures you know, where was there a human choice?
Speaker 1: Where is there accountability? You know, is it Google or
Speaker 1: the d O D who is accountable for another civilian
Speaker 1: bound strike because it was informed by this software, etcetera.
Speaker 1: Oh my god. So you're saying there's a person inside RoboCop. Yeah, yeah,
Speaker 1: an army of people and they're not paid. Well, that's
Speaker 1: the thing. I mean, that's like I worked in at
Speaker 1: a tech publication for a couple of years, so sort
Speaker 1: of like I mean, I was covering entertainment and didn't
Speaker 1: no ship about tech, but I was sort of in
Speaker 1: the world. It was around me in a way, and
Speaker 1: like that was the thing that always sort of baffled
Speaker 1: me on a very very abstract philosophical level. Again, this
Speaker 1: thing of just like why do you deny that there
Speaker 1: are humans? Like why deny the presence of humans? And
Speaker 1: why does that equal the future? You know, it's such
Speaker 1: a it's such a strange ground groundwork to start from.
Speaker 1: Like if you're a tech if you're one of these
Speaker 1: dudes who runs one of these companies, if you're if
Speaker 1: you're a Peter Teal or something like, why I don't know, like,
Speaker 1: why is that what your assumption of what progress looks like?
Speaker 1: It's very strange. I don't I don't ask you to
Speaker 1: have any psychological inside on any of these people, but
Speaker 1: if you have any, that would be great. Uh, I mean, yeah,
Speaker 1: they're not. There's kind of a cult of the socially awkward,
Speaker 1: which has been used to minimize and justify bad behavior
Speaker 1: for a long time. But I also think, you know,
Speaker 1: as a materialist, I'm always like, well, it's a lot easier,
Speaker 1: you know, if you can sell something as replacing kinds
Speaker 1: of humans, you know, do it right. Like you don't
Speaker 1: have a Genius of AI story or a the Amazing
Speaker 1: Founders story. If you counted all that labor right, you're
Speaker 1: not the heroes. If you recognize that this actually takes
Speaker 1: a massive infrastructure that is built on irreducible human labor,
Speaker 1: that is built on you know, this sort of you know,
Speaker 1: extractive economy of you know, you know, taking or making
Speaker 1: or faking a bunch of data to do all of
Speaker 1: this right, it's not actually genius that's happening. It's sort
Speaker 1: of the you know, uh, you know, regulatory arbitrage and
Speaker 1: resource extraction. Um, it's not as it's not a story
Speaker 1: that's as marketable, right, And um, I think for too
Speaker 1: long we've just kind of guilelessly believed this idea that
Speaker 1: you know, if you had an algorithm and an idea
Speaker 1: in a garage, you could you know, you could create
Speaker 1: something new and and and us. When we see these ideas,
Speaker 1: they are progress and they are do represent a sort
Speaker 1: of you know, positivist arc towards something better. Right. It
Speaker 1: seems like there's a lot of like, if you can
Speaker 1: do something, you have to um, which reminds me a
Speaker 1: lot of like the D d T thing in the
Speaker 1: twentieth century of just sort of hey, look what we
Speaker 1: can make, uh, And people happen to be like, oh, actually,
Speaker 1: just because you can make a poisonous gas, it doesn't
Speaker 1: actually mean it's like an advance of technology or just
Speaker 1: you know, but there are reasons not to do things.
Speaker 1: There are moral and ethical reasons not to do things
Speaker 1: that can can be done. But what I love about
Speaker 1: your work so much is also you're saying, like these
Speaker 1: things don't even work most of the time. No, I
Speaker 1: mean they worked to you know, extract money and because
Speaker 1: all their power, right, So like in a sense they
Speaker 1: are working because like, ultimately, who's measuring what? You know,
Speaker 1: they're kind of measuring that. But yeah, these don't know,
Speaker 1: they're they're jankie, they're fallible, they're incontestable, they're frustrating. So
Speaker 1: you would you you would you include the thing that
Speaker 1: everybody's talking about right now is like facial recognition SoftWhere
Speaker 1: UM at protests and stuff like that. Do you think
Speaker 1: that that's ultimately not um a huge concern or the
Speaker 1: technology just isn't just isn't in there yet, Like yeah,
Speaker 1: I mean, I gotta be careful because yeah, it does,
Speaker 1: it can recognize faces, right, but of course it um
Speaker 1: it reflects the historical patterns of racism and misologyny that
Speaker 1: any technology created in you know, in the context of
Speaker 1: you know, it's time will reflect um and so it is.
Speaker 1: You know, facial recognition software has been shown repeatedly to
Speaker 1: produce far more errors for black women, for Native people,
Speaker 1: for gender nonconforming people, So it's you know, yeah, it
Speaker 1: is dangerous because it can, you know, live facial recognition
Speaker 1: can track who's there, match them with a database of identities,
Speaker 1: which because of driver's license databases and sort of government
Speaker 1: I D programs and other programs where you know, like
Speaker 1: Flicker where we've or Facebook where we've uploaded our face
Speaker 1: with our name a number of times, and that can
Speaker 1: be you know that is that is possible and it's
Speaker 1: very dangerous. And I have you know, I have advocated
Speaker 1: sort of banning facial recognition outright, Um, give in those dynamics. Oh,
Speaker 1: I just wanted to see if you had kind of
Speaker 1: any guidelines for people. I mean, we're right now conducting
Speaker 1: this podcast. We're zooming so we can all see each other.
Speaker 1: And I know that the way that we use technology
Speaker 1: has changed so much, um, even over the course of quarantine.
Speaker 1: It wasn't like we kind of started off quarantine doing
Speaker 1: all the things that we now have to do to
Speaker 1: keep up with work. But for you, who knows about
Speaker 1: these things, how would you advise other people to take
Speaker 1: precautions to protect themselves against being you know, having facial
Speaker 1: recognition software used against them and things like that, Like,
Speaker 1: how do you personally kind of operate to protect yourself
Speaker 1: And how do you recommend that people who are either
Speaker 1: going to protests or just doing a lot of like
Speaker 1: zooming and zen castering and stuff like that, how do
Speaker 1: you recommend that they take precautions. Yeah, I mean there
Speaker 1: are a bunch of good security guides for protests, but
Speaker 1: like wear a mask, right, mass laws are are off
Speaker 1: right now we can wear masks. That's really big. Um,
Speaker 1: we're big sunglasses, right those those do you know perturb
Speaker 1: facial recognition. It's good to do that. Um. But I
Speaker 1: think you know, the main message here for me is
Speaker 1: this is not you know, a kind of this is
Speaker 1: not a problem of kind of neoliberal individual responsibility, right,
Speaker 1: Like we are looking at yeah, and we're looking at
Speaker 1: you know, we have we live in we have you know,
Speaker 1: we are in a world where participation in you know,
Speaker 1: social and political and economic life requires are using these technologies, right,
Speaker 1: Like going into a cave and only using tour isn't
Speaker 1: going to you know, there are a number of reasons
Speaker 1: that won't work, but it's not you know, it's not
Speaker 1: a choice for most people, and I think we have
Speaker 1: to recognize that. You know, the the things that I
Speaker 1: am most excited about are the advocacy and the organizing.
Speaker 1: Right the the we we saw last year in and
Speaker 1: twenty nineteen sort of a wave of facial recognition moratoria
Speaker 1: and bands that were organized locally. Right, people said no,
Speaker 1: you can't, you know, use these technologies. They showed up
Speaker 1: at their civil City Council meetings, you know, they showed
Speaker 1: up at their schools. There's been organizing around of the
Speaker 1: use of these tools in schools by parents. But I
Speaker 1: think it's you know, I really do think that's you know,
Speaker 1: where it starts. I think there are people looking at
Speaker 1: sort of all axes. You know, there's a no Tech
Speaker 1: for ICE campaign, which has been really good at applying
Speaker 1: pressure on the tech companies that are providing disurveillance and
Speaker 1: tracking infrastructures that have fueled the family seperation policy and
Speaker 1: are now you know, we now have those same companies
Speaker 1: working with the Department of Health and Human Services and
Speaker 1: the CDC to collect COVID data. It's unclear with the
Speaker 1: data sharing agreements between you know, CDC and ICE might
Speaker 1: be or HHS and ICE might be. So like, there
Speaker 1: are all of these things that are going to impact
Speaker 1: you know, people, and that people are already organizing around.
Speaker 1: So I think, you know, sort of echoing the credo
Speaker 1: like look at you know, who's doing what around you
Speaker 1: and see where you can be helpful. But I don't,
Speaker 1: you know, I'm really I reject sort of shaming people
Speaker 1: for using technology. I reject that mode. Um, but I
Speaker 1: do think you know, connect it to a material issue
Speaker 1: that matters to you, and you're of people in your community,
Speaker 1: and you'll probably find a place where this stuff is
Speaker 1: um doing some harm and where you could do some organizing. Yeah.
Speaker 1: I mean, I feel like pointing out that they're humans
Speaker 1: behind all of this stuff, um, and that they need
Speaker 1: those humans for these companies to function. Um. Just with
Speaker 1: the Amazon stuff that was happening with warehouse workers uh
Speaker 1: uh those people. Yeah, I think just to to let
Speaker 1: to empower the workers to know that they the companies
Speaker 1: need them. The companies can't function without them, Yeah, and
Speaker 1: that gives them power. Like great organizing going on with
Speaker 1: gig workers, your Taxi Workers Alliance, UM, there's the Drivers
Speaker 1: United on the West Coast. There's organizing with instacrat workers
Speaker 1: and gig Workers Rising. There's um the Gig Workers Collective.
Speaker 1: There's there's a lot of really interesting organizing. It sort
Speaker 1: of at the forefront that is beginning to sort of
Speaker 1: flex and withhold labor um and has seen changes based
Speaker 1: on that, and so many of those tech companies seem
Speaker 1: like they were just made to get around labor laws.
Speaker 1: It's like the joke. It's like the move fast and
Speaker 1: break things, and what you're breaking is labor laws and
Speaker 1: regressing to like, you know, old piece work laws that
Speaker 1: or old piece work norms that. Like I would I
Speaker 1: would point to the work of a legal scholar being
Speaker 1: a new ball who has looked a lot at this,
Speaker 1: and it's like, actually, this is just a return to
Speaker 1: the past with some like entrepreneurial language left on on top.
Speaker 1: One thing I've also noticed is that there's like a
Speaker 1: reluctance in tech companies to admit that they're doing anything wrong. Ever,
Speaker 1: I had a friend who's working for a company that
Speaker 1: took a contract with Ice, and when she called them
Speaker 1: out about it, they were like, well, we don't know
Speaker 1: what they're using that facial recognition technology for. They could
Speaker 1: be using it to reunite families. She quit. She quit,
Speaker 1: but more power to her. Yeah. I mean they'll say ship,
Speaker 1: they'll say some wild ship, right. Like I remember being
Speaker 1: up on stage debating Mayven with some executives at Google
Speaker 1: and they were like, well, tech is a hammer. A
Speaker 1: hammer can be used to harm or to build, And
Speaker 1: I was like, that is like from people who sort
Speaker 1: of claim the expert knowledge on these systems. It was
Speaker 1: just a wildly lazy statement, and I think it's clear
Speaker 1: that like these people actually haven't thought through what they're
Speaker 1: doing right. They have allowed these sort of like extreme
Speaker 1: like whippers in mythologies to assure them that they're anything
Speaker 1: they do is doing good. And now they're confronted with
Speaker 1: some extraordinarily complex questions that they don't have the training,
Speaker 1: they don't have the background, and they don't have the
Speaker 1: stomach to answer honestly. And so you get these like
Speaker 1: strange ass platitudes for people who are working for these
Speaker 1: companies in any capacity. And I'm sure we have people
Speaker 1: and among our listeners who do whether you're a driver
Speaker 1: for Uber or a programmer or whatever, what what's your
Speaker 1: kind of stands generally on Like this thing that Molly
Speaker 1: was talking about where you know, her friend decided not
Speaker 1: to work for this company. Is it the I feel
Speaker 1: like this is a debate all the time where it's like, well,
Speaker 1: should I me a good conscientious person, uh enter this
Speaker 1: community and hopefully be some sort of agent of change
Speaker 1: or good with it it, or should I just stay
Speaker 1: out of it altogether? Like what do you what's your
Speaker 1: feeling on that. I mean, I you know, this is complicated,
Speaker 1: like people need to eat, people need jobs, like we
Speaker 1: don't you know that is a problem, you know, like why,
Speaker 1: you know, why why do we have to spend so
Speaker 1: much of our finite magical lifetime, uh in the service
Speaker 1: of wage work. I think that's a question sort of
Speaker 1: you know that frames this a bit. But I don't
Speaker 1: you know what, Like I don't know. I was poor
Speaker 1: growing up, and one of the things who gave me
Speaker 1: was sort of access to sort of a class and
Speaker 1: we'll learn those mannerisms that like I wouldn't have had
Speaker 1: other ways, you know, in college did that to an
Speaker 1: extent um. You know, I started organizing when I realized
Speaker 1: what I was doing as a sort of critic and
Speaker 1: like internal like a researcher isn't actually helping things. But again,
Speaker 1: I think that's you know, if you're going to go
Speaker 1: up against power, do it with other people. If you're
Speaker 1: in a position to do that, do that. Recognize that
Speaker 1: there are a lot of people who aren't always in
Speaker 1: that position. You know, there are people on visas, there
Speaker 1: are people who are you know, the soul breadwinner, but
Speaker 1: that there are ways to sort of at least begin
Speaker 1: to build like the relational bonds of solidarity with the
Speaker 1: people around you, which in itself I think is is
Speaker 1: extremely radical. Right when you begin to replace your identity
Speaker 1: in a workplace, you know, when you stop identifying with
Speaker 1: your sort of position within a hierarchy as who you
Speaker 1: are and how you fit in this space, and start
Speaker 1: identifying with your relationships with the people around you and
Speaker 1: what you owe them as sort of a practice of solidarity.
Speaker 1: It's like a transformative thing to do. And it is
Speaker 1: like there's a reason that, um, you know, like capital
Speaker 1: has fought back against those forms and that those forms
Speaker 1: haven't changed much over you know, years and years, right,
Speaker 1: that like that is of how you build power. So
Speaker 1: I think at least engaging in that and then you know,
Speaker 1: knowing that to be safe, you're gonna need you know,
Speaker 1: you're gonna need critical maths, so figure out where you
Speaker 1: start from there. Well, that also, I think by talking
Speaker 1: about how you know, we need to engage with technology
Speaker 1: as both users and in a many of us in
Speaker 1: like a professional capacity. The fact that it's it is
Speaker 1: such a privilege to be able to opt out of
Speaker 1: those things that almost none of us are afforded right now.
Speaker 1: So it seems like the best path forward is to,
Speaker 1: like you said, to to organize, to recognize the power
Speaker 1: of kind of speaking up and like you know, uniting
Speaker 1: and like being critical of these institutions is really the
Speaker 1: only option. Like I I can't imagine right now many
Speaker 1: people can afford to quit a job. It's probably the
Speaker 1: worst time in the world to consider that. Uh. And
Speaker 1: also just you know, the connection offered by social media
Speaker 1: when everyone is isolated, it's you know, it's a difficult
Speaker 1: time to consider just like making the choice to opt
Speaker 1: out of those things. So that makes sense. Yeah, And frankly,
Speaker 1: you can't opt out, right. You are the surveilled, right,
Speaker 1: collecting it from street polls, They're collecting it from sensers,
Speaker 1: They're collecting it from your credit card, They're collecting it
Speaker 1: from the geo data on your phone. You know, you
Speaker 1: cannot carry a phone, but then you can't get work email. Right,
Speaker 1: They're collecting it, you know, from data brokers that you know,
Speaker 1: pull from you your family's social media profile. So there
Speaker 1: isn't again, it's not an individual even if you were
Speaker 1: to be sort of incredibly squeaky clean, even an absence
Speaker 1: of data can be tagged a sort of data, right,
Speaker 1: so UM, it's not I think users is actually almost
Speaker 1: a data term in this case because we're UM, well,
Speaker 1: thank you so much for joining us this week, Meredith.
Speaker 1: The suspense that should uh enlightening conversation. UH, and we
Speaker 1: really appreciate all of your insight and experience and and
Speaker 1: everything you have to say. It's great. UM. Where can
Speaker 1: people find you online? You can find me on Twitter. UM.
Speaker 1: You can also visit the AI Now Institute website for
Speaker 1: some of the work that UM, you know that brilliant
Speaker 1: team is doing that I'm doing with them. UM. Yeah,
Speaker 1: I think that's that's about it. UM. I would also
Speaker 1: encourage people in tech to check out Tech Workers Coalition UM,
Speaker 1: which is a you know, a loose organization that is
Speaker 1: helping workers in tech organized UM and understand their power
Speaker 1: and its contact. Awesome, awesome, Well, thank you so much, Meredith,
Speaker 1: and we hope to have you back because this was
Speaker 1: a really really great conversation. So thank you so much.
Speaker 1: It was a delight. Have a great boy. Thank you
Speaker 1: bye bye. Well, thank you so much for listening to
Speaker 1: another episode of Nightcall. If you enjoyed the show, please
Speaker 1: give us a rating and review, and don't forget to
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Speaker 1: you can email us at Nightcall Podcast at gmail dot com.
Speaker 1: Thanks again for listening. We'll be back next week. M