1 00:00:04,200 --> 00:00:05,920 Speaker 1: There Are No Girls on the Internet. As a production 2 00:00:05,960 --> 00:00:13,640 Speaker 1: of iHeartRadio and Unbossed Creative, I'm Bridget and this is 3 00:00:13,640 --> 00:00:18,599 Speaker 1: there are No Girls on the Internet. Hey Mike, thanks 4 00:00:18,640 --> 00:00:22,200 Speaker 1: for being here. It is finally February. Happy February. 5 00:00:22,560 --> 00:00:25,360 Speaker 2: Thanks for having me, Bridget, and Happy Black History Months. 6 00:00:25,600 --> 00:00:27,920 Speaker 1: Thank you. Happy Black History Months to you too. You 7 00:00:27,960 --> 00:00:30,840 Speaker 1: know that I've been on my crusade of wishing random 8 00:00:30,840 --> 00:00:33,400 Speaker 1: strangers Happy Black History Month to say maybe that you 9 00:00:33,479 --> 00:00:37,240 Speaker 1: might be like happy Holidays, Merry Christmas. It's been funny 10 00:00:37,280 --> 00:00:40,520 Speaker 1: because universally Black people are like, hey, I be Black 11 00:00:40,560 --> 00:00:44,960 Speaker 1: History Months. Non black people either they will like look 12 00:00:44,960 --> 00:00:46,879 Speaker 1: at me weird and not be sure how to respond, 13 00:00:46,920 --> 00:00:50,720 Speaker 1: which I think is really funny, or they'll instinctively be like, oh, 14 00:00:50,760 --> 00:00:53,120 Speaker 1: happy Black History Month to you too, Like when you say, 15 00:00:53,159 --> 00:00:56,360 Speaker 1: like when you get your ticket taken at the movies 16 00:00:56,480 --> 00:00:59,120 Speaker 1: and they're like, enjoy your movie, and then you're like, 17 00:00:59,200 --> 00:01:02,800 Speaker 1: oh you too. Wait with that the right response. That's 18 00:01:02,800 --> 00:01:05,399 Speaker 1: been my favorite, the instinctual like you too, and then 19 00:01:05,480 --> 00:01:07,039 Speaker 1: like wait a minute, was that appropriate? 20 00:01:07,720 --> 00:01:09,839 Speaker 2: Yeah? You know, we're all doing our best out here. 21 00:01:11,520 --> 00:01:15,240 Speaker 2: Is there any content out there, like like Black History 22 00:01:15,280 --> 00:01:17,840 Speaker 2: Month content that you particularly like. 23 00:01:18,080 --> 00:01:20,880 Speaker 1: I'm really glad that you asked, because this time of year, 24 00:01:20,920 --> 00:01:25,800 Speaker 1: I always share my favorite TikTok about commemorating Black History Month, 25 00:01:26,120 --> 00:01:28,920 Speaker 1: and it's this I think she might be Australian, like 26 00:01:28,959 --> 00:01:32,479 Speaker 1: an influencer who is standing in the middle of the sidewalk. 27 00:01:32,760 --> 00:01:37,000 Speaker 1: She's got sneakers and running shorts and one of those 28 00:01:37,080 --> 00:01:39,560 Speaker 1: fanny packs for runners, and she's showing doing kind of 29 00:01:39,600 --> 00:01:42,160 Speaker 1: an outfit check, like outfit of the day, going running 30 00:01:42,560 --> 00:01:46,000 Speaker 1: and then this black man behind her is like, move 31 00:01:46,040 --> 00:01:49,440 Speaker 1: out of the way, it's Black History Month and just 32 00:01:49,520 --> 00:01:53,160 Speaker 1: keeps going and interrupts her like outfit of the day 33 00:01:53,280 --> 00:01:57,760 Speaker 1: influencer TikTok. Something about that video really commemorates my feelings 34 00:01:57,800 --> 00:01:58,800 Speaker 1: on Black History Month. 35 00:01:58,960 --> 00:02:01,280 Speaker 2: All right forward to checking it out. 36 00:02:01,360 --> 00:02:03,560 Speaker 1: And speaking of TikTok, I have to say, I mean, 37 00:02:03,560 --> 00:02:05,880 Speaker 1: I know that you're not a big user of the platform, 38 00:02:05,920 --> 00:02:09,280 Speaker 1: but have you heard that big changes might be coming 39 00:02:09,320 --> 00:02:09,840 Speaker 1: to TikTok. 40 00:02:10,600 --> 00:02:15,119 Speaker 2: I have, uh, and it seems interesting. I haven't really 41 00:02:15,200 --> 00:02:17,480 Speaker 2: looked too far into it, so I'm hoping that you 42 00:02:17,600 --> 00:02:19,880 Speaker 2: can hit me with some facts here. 43 00:02:20,200 --> 00:02:21,960 Speaker 1: Well, So if you use TikTok, you know that one 44 00:02:22,000 --> 00:02:24,280 Speaker 1: of the big things about it is music, Like people 45 00:02:24,480 --> 00:02:28,800 Speaker 1: upload from very big databases of songs across different record 46 00:02:28,880 --> 00:02:31,160 Speaker 1: labels to sync with their tiktoks, and it's kind of 47 00:02:31,200 --> 00:02:33,800 Speaker 1: a big part of the music ecosystem on TikTok, Like 48 00:02:34,200 --> 00:02:36,760 Speaker 1: songs from artists can really blow up if they're being 49 00:02:36,840 --> 00:02:40,239 Speaker 1: used in tiktoks that go viral. TikTok is clearly part 50 00:02:40,280 --> 00:02:44,440 Speaker 1: of like marketing strategies for new songs. It is a 51 00:02:44,440 --> 00:02:47,560 Speaker 1: big thing, and particularly that TikTok started, Like in the 52 00:02:47,600 --> 00:02:50,120 Speaker 1: earliest early days of TikTok, it was called byte dance 53 00:02:50,160 --> 00:02:53,400 Speaker 1: and it was really much more associated with music and dancing. 54 00:02:53,600 --> 00:02:56,320 Speaker 1: When people say, oh, TikTok is just an app with 55 00:02:56,440 --> 00:02:59,480 Speaker 1: kids dancing, it's never really been that, but that's what 56 00:02:59,520 --> 00:03:02,360 Speaker 1: they're for. And so music has always been a big 57 00:03:02,400 --> 00:03:04,839 Speaker 1: part of what makes TikTok work. But all of that 58 00:03:05,000 --> 00:03:08,480 Speaker 1: might be changing because the music label UMG is threatening 59 00:03:08,480 --> 00:03:11,919 Speaker 1: to pull all of their artists from TikTok permanently because 60 00:03:11,919 --> 00:03:14,320 Speaker 1: they say TikTok has not been paying artists to use 61 00:03:14,360 --> 00:03:17,680 Speaker 1: their music on the platform. They say, TikTok propose paying 62 00:03:17,680 --> 00:03:20,080 Speaker 1: our artists and songwriters at a rate that is a 63 00:03:20,120 --> 00:03:23,240 Speaker 1: fraction of the rate that similarly situated music social platforms 64 00:03:23,280 --> 00:03:25,880 Speaker 1: pay U and G represents some of the biggest and 65 00:03:25,960 --> 00:03:28,520 Speaker 1: most popular artists in the world, both on TikTok and 66 00:03:28,520 --> 00:03:32,200 Speaker 1: across the music scene more generally, people like Taylor Swift, Drake, 67 00:03:32,280 --> 00:03:35,960 Speaker 1: Olivia Rodrigo, The Beatles, U Two, Ariana Grande, Sizza, Billie 68 00:03:36,000 --> 00:03:39,120 Speaker 1: Eilish Adele, Coldplay, and a whole lot more. And it 69 00:03:39,160 --> 00:03:42,160 Speaker 1: really doesn't sound like either party is planning on backing down. 70 00:03:42,560 --> 00:03:46,520 Speaker 1: TikTok is accusing UMG of quote putting their greed above 71 00:03:46,520 --> 00:03:50,000 Speaker 1: the interests of artists and songwriters, So I will say 72 00:03:50,120 --> 00:03:52,720 Speaker 1: it does kind of sound like UMG has a point here. 73 00:03:53,160 --> 00:03:56,240 Speaker 1: NBC reported that UMG says TikTok only accounts for one 74 00:03:56,280 --> 00:03:59,440 Speaker 1: percent of its revenue, despite its artists representing eight out 75 00:03:59,440 --> 00:04:01,560 Speaker 1: of ten of the most popular bands and singers on 76 00:04:01,600 --> 00:04:04,280 Speaker 1: the platform last year, and around sixty percent of TikTok 77 00:04:04,320 --> 00:04:07,880 Speaker 1: videos do include music. UNG published an open letter to 78 00:04:07,920 --> 00:04:11,480 Speaker 1: their artists about why it's time to call out TikTok, saying, ultimately, 79 00:04:11,520 --> 00:04:14,520 Speaker 1: TikTok is trying to build a music based business without 80 00:04:14,560 --> 00:04:18,280 Speaker 1: paying fair value for the music. I absolutely agree with that, 81 00:04:18,400 --> 00:04:23,200 Speaker 1: like music is an integral part of TikTok. There is 82 00:04:23,320 --> 00:04:27,000 Speaker 1: no TikTok as we know it without music, and it 83 00:04:27,040 --> 00:04:29,320 Speaker 1: does seem like they're trying to have their cake and 84 00:04:29,360 --> 00:04:31,920 Speaker 1: eat it too. They're trying to build a music based 85 00:04:31,960 --> 00:04:35,400 Speaker 1: platform full of a library of music and maybe like 86 00:04:35,600 --> 00:04:37,839 Speaker 1: not pay for that music. So some of the things 87 00:04:37,839 --> 00:04:40,479 Speaker 1: that UMG is also concerned about are the growth of 88 00:04:40,480 --> 00:04:43,800 Speaker 1: AI tools, youth and TikTok videos and that effect on 89 00:04:43,839 --> 00:04:47,040 Speaker 1: intellectual property, while also complaining about the amount of content 90 00:04:47,080 --> 00:04:50,080 Speaker 1: that commits copyright infringement, as well as quote a title 91 00:04:50,120 --> 00:04:53,599 Speaker 1: wave of hate speech, bigotry, bullying, and harassment. These are 92 00:04:53,640 --> 00:04:57,840 Speaker 1: all valid concerns that UMG is bringing up, but none 93 00:04:57,880 --> 00:05:00,120 Speaker 1: of these things is wrong. All of these things on 94 00:05:00,120 --> 00:05:01,920 Speaker 1: a rampant on the platform. I don't think TikTok would 95 00:05:01,960 --> 00:05:07,400 Speaker 1: even disbute that. UMG continues saying, as our negotiations continued, 96 00:05:07,520 --> 00:05:10,520 Speaker 1: TikTok attempted to bully us into accepting a deal worth 97 00:05:10,600 --> 00:05:13,400 Speaker 1: less than the previous deal, far less than market value 98 00:05:13,640 --> 00:05:17,440 Speaker 1: and not reflective of their exponential growth. UMG also says 99 00:05:17,480 --> 00:05:20,040 Speaker 1: that TikTok is trying to bully them and threaten them 100 00:05:20,279 --> 00:05:23,680 Speaker 1: by taking music from developing artists off of the platform 101 00:05:23,800 --> 00:05:27,760 Speaker 1: while keeping audience driving global stars. So, as far as 102 00:05:27,800 --> 00:05:30,919 Speaker 1: I know, TikTok is not really given like a good response. 103 00:05:31,200 --> 00:05:33,800 Speaker 1: They've said. The fact is they've chosen to walk away 104 00:05:33,800 --> 00:05:36,440 Speaker 1: from the powerful support of a platform with well over 105 00:05:36,480 --> 00:05:39,360 Speaker 1: a billion users that serves as free promotional and discovery 106 00:05:39,440 --> 00:05:42,760 Speaker 1: vehicle for their talent. That response kind of gives me 107 00:05:42,880 --> 00:05:45,880 Speaker 1: pause because it feels like, oh, well, whenever somebody tells 108 00:05:45,920 --> 00:05:48,599 Speaker 1: me that I'm the reason why I'm not getting pay 109 00:05:48,720 --> 00:05:51,680 Speaker 1: or money is because I'm getting promotion, it just gets 110 00:05:51,720 --> 00:05:54,960 Speaker 1: my It just makes my asstch. I'm like, wait, you're 111 00:05:54,960 --> 00:05:58,560 Speaker 1: telling me that promotion is the same thing as money here, 112 00:05:58,839 --> 00:06:00,479 Speaker 1: and I sort of see what they're getting. But the 113 00:06:00,480 --> 00:06:02,960 Speaker 1: fact that they're trying to use that language of like, 114 00:06:03,000 --> 00:06:05,799 Speaker 1: oh no, no, promotion is actually better kind of gives 115 00:06:05,800 --> 00:06:09,440 Speaker 1: me some some warnings, some warning flags about this whole situation. 116 00:06:10,000 --> 00:06:13,400 Speaker 2: Yeah, definitely, I mean that's it's like a cliche, right 117 00:06:13,440 --> 00:06:15,800 Speaker 2: that people are going to get paid in exposure, like 118 00:06:15,839 --> 00:06:17,560 Speaker 2: you can't you can't eat promotion. 119 00:06:18,000 --> 00:06:21,920 Speaker 1: Yeah. I have tried giving my landlord promotion to pay 120 00:06:21,920 --> 00:06:23,840 Speaker 1: my rent and he doesn't accept it, So I don't 121 00:06:23,839 --> 00:06:26,680 Speaker 1: know I will say something that I think is really 122 00:06:26,720 --> 00:06:30,839 Speaker 1: important here is that you know, UMG in their messaging, 123 00:06:30,920 --> 00:06:33,880 Speaker 1: they sound really like, we are doing this for the artists. 124 00:06:33,880 --> 00:06:35,640 Speaker 1: We want the artists to get paid, we want the 125 00:06:35,720 --> 00:06:38,279 Speaker 1: artists to get with their words. Stop it splitting the artists. 126 00:06:38,680 --> 00:06:41,160 Speaker 1: I don't believe that either. I think that what UMG 127 00:06:41,360 --> 00:06:44,320 Speaker 1: is actually saying is that TikTok needs to be paying 128 00:06:44,640 --> 00:06:48,039 Speaker 1: our label, like our suits, our executives, We need to 129 00:06:48,040 --> 00:06:50,320 Speaker 1: be making more of that money. I don't think it's 130 00:06:50,320 --> 00:06:53,120 Speaker 1: a situation where they're planning on like sharing all of 131 00:06:53,120 --> 00:06:57,000 Speaker 1: that money with like independent artists who need it. I 132 00:06:57,080 --> 00:06:59,840 Speaker 1: don't see a record label behaving that way. I think 133 00:06:59,839 --> 00:07:02,240 Speaker 1: the they're trying to get a bigger payout for themselves 134 00:07:02,560 --> 00:07:05,640 Speaker 1: and just sort of using this like support the artists, 135 00:07:05,760 --> 00:07:08,479 Speaker 1: pay the artists language because they know it will be 136 00:07:08,640 --> 00:07:13,360 Speaker 1: sort of a more sympathetic argument. However, I do think 137 00:07:13,400 --> 00:07:15,680 Speaker 1: that TikTok is trying to get around paying artists, and 138 00:07:15,720 --> 00:07:18,040 Speaker 1: I think that it mirrors a lot of the conversations 139 00:07:18,040 --> 00:07:20,400 Speaker 1: that we're seeing in other platforms too, like you know, 140 00:07:20,560 --> 00:07:24,880 Speaker 1: Facebook at a time making money from news content and 141 00:07:24,960 --> 00:07:28,080 Speaker 1: the content that news outlets put out but intending to 142 00:07:28,080 --> 00:07:30,560 Speaker 1: not pay them for that content, right, Like you could 143 00:07:30,600 --> 00:07:32,680 Speaker 1: say like, oh, we're promoting you and people are reading 144 00:07:32,720 --> 00:07:35,600 Speaker 1: your content, but it's like, well, is that really translating 145 00:07:35,640 --> 00:07:39,280 Speaker 1: to off platform downloads or metrics or sales or whatever. 146 00:07:39,520 --> 00:07:41,440 Speaker 1: Probably not right, And so I think this is sort 147 00:07:41,480 --> 00:07:43,760 Speaker 1: of a cut from the same cloth kind of situation. 148 00:07:44,240 --> 00:07:47,680 Speaker 1: I don't know. I'm almost sort of not excited about this, 149 00:07:48,080 --> 00:07:53,320 Speaker 1: but I'm interested to see how this changes the platform. Already, 150 00:07:53,400 --> 00:07:57,360 Speaker 1: since this was announced, if people have uploaded tiktoks that 151 00:07:57,480 --> 00:08:01,200 Speaker 1: have music of these artists, those tiktoks are completely muted. 152 00:08:01,240 --> 00:08:04,000 Speaker 1: So like if I was doing a video TikTok where 153 00:08:04,000 --> 00:08:05,520 Speaker 1: I was talking at the camera and there was a 154 00:08:05,560 --> 00:08:08,400 Speaker 1: Taylor Swift song playing, the whole thing would be muted, 155 00:08:08,480 --> 00:08:10,160 Speaker 1: not just the music, also what I was saying. And 156 00:08:10,200 --> 00:08:13,120 Speaker 1: so I've already seen that happening on the platform. But 157 00:08:13,440 --> 00:08:18,080 Speaker 1: I mean, I don't know. I am curious if this 158 00:08:18,120 --> 00:08:23,680 Speaker 1: will actually lead to smaller or less well known artists 159 00:08:24,680 --> 00:08:26,920 Speaker 1: their having their heyday on the platform, which could be 160 00:08:27,040 --> 00:08:29,560 Speaker 1: kind of cool. Like one of my favorite music TikTokers 161 00:08:29,560 --> 00:08:33,600 Speaker 1: that I follow posted a TikTok with the song one 162 00:08:33,600 --> 00:08:36,400 Speaker 1: of my favorite songs actually Cruel Summer by Banana Rama, 163 00:08:36,920 --> 00:08:40,280 Speaker 1: and the commentary was like, oh, now that Taylor Swift's 164 00:08:41,000 --> 00:08:44,120 Speaker 1: for Taylor Swift's song Cruel Summer is no longer allowed 165 00:08:44,120 --> 00:08:47,680 Speaker 1: on the platform, the superior Cruel Summer, the one by 166 00:08:47,720 --> 00:08:49,760 Speaker 1: Banana Rama, can finally rise. 167 00:08:51,520 --> 00:08:55,360 Speaker 2: That's pretty funny. Boy. It's really interesting the comparison you 168 00:08:55,400 --> 00:09:00,440 Speaker 2: made to Facebook not wanting to pay news outlets, because 169 00:09:01,120 --> 00:09:03,400 Speaker 2: it is very similar. I hadn't thought about that, but 170 00:09:05,000 --> 00:09:09,240 Speaker 2: it kind of It does raise questions about these platforms 171 00:09:09,360 --> 00:09:15,000 Speaker 2: business models that depend on serving up content to their 172 00:09:15,080 --> 00:09:18,080 Speaker 2: users to keep their users engaged, but not wanting to 173 00:09:18,080 --> 00:09:22,600 Speaker 2: pay the creators of that content. Yeah, it seems incorrect. 174 00:09:23,080 --> 00:09:27,199 Speaker 1: That seems to be a square in a circle of 175 00:09:27,240 --> 00:09:29,160 Speaker 1: what it means to be on the Internet these days 176 00:09:29,200 --> 00:09:34,200 Speaker 1: of people like platforms that are big business want to 177 00:09:34,200 --> 00:09:37,920 Speaker 1: be like, oh, well, we're just like it's users generated content, 178 00:09:38,000 --> 00:09:40,000 Speaker 1: or we're just taking content from the Internet, or like 179 00:09:40,400 --> 00:09:44,319 Speaker 1: we're just you know, we're promoting. Essentially, they want to 180 00:09:44,480 --> 00:09:49,800 Speaker 1: build huge lucrative platforms using content and then also somehow 181 00:09:49,880 --> 00:09:52,240 Speaker 1: get around paying for that content. And it's like we 182 00:09:52,320 --> 00:09:55,320 Speaker 1: think of music like that's somebody's labor, that is somebody's 183 00:09:55,320 --> 00:09:58,080 Speaker 1: intellectual property, That is somebody's somebody put a lot of 184 00:09:58,080 --> 00:10:00,400 Speaker 1: time and intention and thought and work into that. You 185 00:10:00,440 --> 00:10:02,440 Speaker 1: don't think you have to pay to use it, of 186 00:10:02,440 --> 00:10:03,040 Speaker 1: course you do. 187 00:10:03,480 --> 00:10:06,000 Speaker 2: Yeah, And it would be one thing if like an 188 00:10:06,120 --> 00:10:09,600 Speaker 2: artist records a video of them performing their song and 189 00:10:09,720 --> 00:10:12,480 Speaker 2: uploads that, But that's not what we're talking about here. 190 00:10:12,520 --> 00:10:16,520 Speaker 2: We're talking about some you know, just some random user 191 00:10:16,960 --> 00:10:20,920 Speaker 2: choosing a song that a professional artist has recorded and 192 00:10:21,040 --> 00:10:23,200 Speaker 2: using that, Like, obviously they should get paid for that. 193 00:10:23,600 --> 00:10:26,840 Speaker 1: Yeah, And I think when it comes to music, I 194 00:10:26,960 --> 00:10:32,679 Speaker 1: do think we are overdue for some conversations about how 195 00:10:32,800 --> 00:10:38,760 Speaker 1: technology has shaped music in twenty twenty four, Like with Spotify, 196 00:10:38,160 --> 00:10:42,960 Speaker 1: maybe the model where virtually any song ever that you 197 00:10:42,960 --> 00:10:48,040 Speaker 1: could ever think of is available on Spotify to be 198 00:10:48,440 --> 00:10:53,800 Speaker 1: listened to unlimited amounts, And maybe the model that we 199 00:10:53,920 --> 00:10:57,559 Speaker 1: have is like, isn't working without somebody getting screwed. And 200 00:10:57,720 --> 00:10:59,520 Speaker 1: I think that somebody is the artists. 201 00:11:00,080 --> 00:11:03,920 Speaker 2: Yeah, I think you're absolutely right, you know, especially like 202 00:11:03,960 --> 00:11:07,800 Speaker 2: you talked about emerging artists who are not a big 203 00:11:07,880 --> 00:11:11,280 Speaker 2: name trying to make it like your Taylor Swift's, your Youtubes, 204 00:11:11,400 --> 00:11:14,080 Speaker 2: your Drakes, They're going to be fine, but somebody who's 205 00:11:14,120 --> 00:11:17,920 Speaker 2: trying to make it, they're really caught in a bind 206 00:11:17,960 --> 00:11:21,880 Speaker 2: because they really do need that promotion, but they also 207 00:11:22,520 --> 00:11:25,319 Speaker 2: need to make a living. Yeah, I think you're absolutely 208 00:11:25,400 --> 00:11:29,000 Speaker 2: right that it's tough out there for musicians. You know, 209 00:11:29,080 --> 00:11:35,200 Speaker 2: like streaming platforms like Spotify already exponentially reduced the you know, 210 00:11:35,240 --> 00:11:37,640 Speaker 2: the amount they get paid when people listen to their music, 211 00:11:37,679 --> 00:11:41,240 Speaker 2: and now TikTok is trying to cut that you know, 212 00:11:41,320 --> 00:11:42,440 Speaker 2: a fraction of that for action. 213 00:11:43,120 --> 00:11:45,079 Speaker 1: Yeah, but again, I don't want to make it seem 214 00:11:45,120 --> 00:11:48,520 Speaker 1: like the music labels are being so altruistic, like they're 215 00:11:48,520 --> 00:11:50,839 Speaker 1: just like writing for their artists to get a pay out, 216 00:11:50,840 --> 00:11:53,640 Speaker 1: because I don't think that's what's happening either. This musician, 217 00:11:53,720 --> 00:11:57,800 Speaker 1: Chelsea Collins, her brother said, my sister, Chelsea Collins, it's 218 00:11:57,800 --> 00:12:00,520 Speaker 1: only signed to the publishing side of UMG, but they 219 00:12:00,520 --> 00:12:03,280 Speaker 1: took down both her universal own songs from years ago 220 00:12:03,480 --> 00:12:07,439 Speaker 1: and her songs that she released independently on the master side. 221 00:12:07,760 --> 00:12:11,080 Speaker 1: And so I just feel like smaller artists are gonna 222 00:12:11,080 --> 00:12:14,040 Speaker 1: get screwed, whether by this TikTok decision and TikTok trying 223 00:12:14,040 --> 00:12:18,080 Speaker 1: to like, you know, play hard about this or from 224 00:12:18,120 --> 00:12:21,480 Speaker 1: the label. And I already think that TikTok has shaped 225 00:12:21,520 --> 00:12:23,319 Speaker 1: what it means to be a musician in this day 226 00:12:23,320 --> 00:12:25,480 Speaker 1: and age in some ways that I don't think are 227 00:12:25,520 --> 00:12:30,040 Speaker 1: great musicians, especially independent and up and coming musicians, have 228 00:12:30,280 --> 00:12:34,320 Speaker 1: really been candid about the ways that TikTok has forced 229 00:12:34,360 --> 00:12:37,200 Speaker 1: them to sort of be their own marketing arm. And so, 230 00:12:37,559 --> 00:12:40,520 Speaker 1: you know, before social media, before TikTok, if you were 231 00:12:40,520 --> 00:12:43,079 Speaker 1: a musician, you were making music. You were in the studio, 232 00:12:43,120 --> 00:12:45,200 Speaker 1: you were songwriting, you were being creative. You were like 233 00:12:45,480 --> 00:12:48,560 Speaker 1: doing the labor that it takes to make a creative project. Now, 234 00:12:48,600 --> 00:12:51,680 Speaker 1: on top of that, you need to be like essentially 235 00:12:51,720 --> 00:12:57,040 Speaker 1: your own one person documentary filmmaker making content in quotes 236 00:12:57,200 --> 00:13:00,000 Speaker 1: about the music. And it's like, well, how are you 237 00:13:00,360 --> 00:13:02,120 Speaker 1: meant to make the music if you also have to 238 00:13:02,160 --> 00:13:05,000 Speaker 1: be like documenting you making the music to get any traction. 239 00:13:05,360 --> 00:13:10,880 Speaker 1: There's that thing that happens where it'll be like it's 240 00:13:10,920 --> 00:13:13,120 Speaker 1: the cringiest thing, but I understand why they do it. 241 00:13:13,120 --> 00:13:15,840 Speaker 1: It's like somebody will make a musician makes a TikTok 242 00:13:15,960 --> 00:13:18,360 Speaker 1: and they say, did I just make the song of 243 00:13:18,400 --> 00:13:20,840 Speaker 1: the summer? And it has their song playing in the background. 244 00:13:21,160 --> 00:13:24,120 Speaker 1: And there was another one where a woman made a 245 00:13:24,120 --> 00:13:26,560 Speaker 1: songwriter had made us a TikTok where she was like 246 00:13:27,200 --> 00:13:30,800 Speaker 1: kind of complaining about how boring her life is, and 247 00:13:31,640 --> 00:13:35,199 Speaker 1: it's just like, you have to have this very curated, 248 00:13:35,280 --> 00:13:38,600 Speaker 1: specific kind of shtick on TikTok to get any traction. 249 00:13:39,120 --> 00:13:42,640 Speaker 1: And in an earlier era your record label that would 250 00:13:42,640 --> 00:13:45,520 Speaker 1: have been them paying money to do the labor of 251 00:13:45,559 --> 00:13:48,400 Speaker 1: marketing your album. And because of TikTok, they've just like 252 00:13:48,679 --> 00:13:51,240 Speaker 1: put that onto the plate of the musician, squeezing into 253 00:13:51,280 --> 00:13:53,600 Speaker 1: the time that the musician can be actually making the 254 00:13:53,720 --> 00:13:55,560 Speaker 1: music the thing they want to be doing, and like 255 00:13:56,040 --> 00:13:58,960 Speaker 1: it just I don't know that it's been a great 256 00:13:59,120 --> 00:14:03,800 Speaker 1: thing for musicians. One of my favorite musicians Fka Twigs. 257 00:14:04,280 --> 00:14:07,920 Speaker 1: She talked about how she has had to put her 258 00:14:08,040 --> 00:14:11,199 Speaker 1: music on TikTok in ways that make her incredibly uncomfortable, 259 00:14:11,440 --> 00:14:15,640 Speaker 1: that don't feel true or aligned with the vision for 260 00:14:15,760 --> 00:14:18,160 Speaker 1: the song or the vision that she had as an artist. 261 00:14:18,360 --> 00:14:20,720 Speaker 1: Like I'm thinking of her song Cellophane, which is this 262 00:14:20,840 --> 00:14:25,120 Speaker 1: like incredibly beautiful song that it's kind of been turned 263 00:14:25,120 --> 00:14:27,320 Speaker 1: into a little bit of a joke on TikTok, but 264 00:14:27,440 --> 00:14:30,040 Speaker 1: they've sped it up and they've made the audio wonky. 265 00:14:30,760 --> 00:14:32,720 Speaker 1: If you know the song, you know exactly what I'm 266 00:14:32,760 --> 00:14:35,080 Speaker 1: talking about. If you've ever heard that song on TikTok 267 00:14:35,120 --> 00:14:37,600 Speaker 1: that has used a lot that's like, let me do 268 00:14:37,800 --> 00:14:40,440 Speaker 1: it for you, but it's all like wonky. It has 269 00:14:40,480 --> 00:14:44,000 Speaker 1: like weird inflections and stuff that's fk. Twigs is a 270 00:14:44,040 --> 00:14:49,080 Speaker 1: song Cellophane, and it's an incredibly beautiful, intimate, vulnerable song. 271 00:14:49,520 --> 00:14:52,000 Speaker 1: So the song's Telophane, from what I understand, is about 272 00:14:52,000 --> 00:14:55,680 Speaker 1: the fracture of Twiggs's relationship with that actor Robert Pattinson. 273 00:14:55,960 --> 00:14:58,840 Speaker 1: So Twigs is black, Patentson is white, and Twigs was 274 00:14:58,840 --> 00:15:04,200 Speaker 1: targeted for like deeply messed up racist online harassment because 275 00:15:04,440 --> 00:15:07,480 Speaker 1: she and Pattinson were together, and Pattinson had been in 276 00:15:07,480 --> 00:15:10,880 Speaker 1: that movie Twilight and had been in a relationship previously 277 00:15:10,920 --> 00:15:13,840 Speaker 1: with his Twilight co star Kristin Stewart. I guess the 278 00:15:13,920 --> 00:15:17,680 Speaker 1: fans like really wanted this like perfect Twilight couple to 279 00:15:17,720 --> 00:15:22,000 Speaker 1: get back together, so they flooded Twiggs's social media with 280 00:15:22,280 --> 00:15:26,359 Speaker 1: horrible racist harassment and online abuse. So the song Cellophane 281 00:15:26,880 --> 00:15:30,720 Speaker 1: is about the role that like this racist hate campaign 282 00:15:31,200 --> 00:15:35,320 Speaker 1: played in this tough breakup of their relationship. But on TikTok, 283 00:15:35,640 --> 00:15:39,080 Speaker 1: somebody did an impression of I guess their version of 284 00:15:39,120 --> 00:15:42,480 Speaker 1: the song as sang by Miss Piggy. It's like them 285 00:15:42,520 --> 00:15:45,360 Speaker 1: doing an impression of Miss Piggy singing that song. And 286 00:15:45,440 --> 00:15:49,080 Speaker 1: so whenever you hear that sound on TikTok, what you're 287 00:15:49,080 --> 00:15:52,120 Speaker 1: actually hearing is somebody doing a Miss Piggy impression of 288 00:15:52,160 --> 00:15:55,600 Speaker 1: an FKA Twig song about the role that racist online 289 00:15:55,640 --> 00:15:59,160 Speaker 1: harassment played in the breakup of her relationship. And it's 290 00:15:59,160 --> 00:16:04,920 Speaker 1: a sound that people use on TikTok to like illustrate jokey, 291 00:16:05,080 --> 00:16:08,240 Speaker 1: silly situations and the way that that song has been 292 00:16:08,320 --> 00:16:11,640 Speaker 1: kind of turned into a joke on TikTok, but also 293 00:16:11,840 --> 00:16:14,280 Speaker 1: it's kind of good for her as an artist because 294 00:16:14,280 --> 00:16:17,000 Speaker 1: the song gets more attraction and more play. It's like 295 00:16:17,040 --> 00:16:19,720 Speaker 1: we're asking artists to do things that are incredibly unusual, 296 00:16:19,760 --> 00:16:21,640 Speaker 1: and like, I don't know, I just think I don't 297 00:16:21,640 --> 00:16:23,680 Speaker 1: know that TikTok has been a net good for artists. 298 00:16:24,040 --> 00:16:26,200 Speaker 2: Yeah, I could imagine that probably doesn't feel great for 299 00:16:26,280 --> 00:16:30,880 Speaker 2: FK Twigs to have this like vulnerable song turned into 300 00:16:30,920 --> 00:16:33,760 Speaker 2: a joke like that, And she sounds like she's like 301 00:16:34,120 --> 00:16:35,720 Speaker 2: not really even getting paid for it either. 302 00:16:36,040 --> 00:16:39,120 Speaker 1: Yeah, And I think last year another one of my 303 00:16:39,120 --> 00:16:42,800 Speaker 1: favorite artists, Fiona Apple, completely took her music off of 304 00:16:42,840 --> 00:16:46,560 Speaker 1: TikTok and I totally get it. Like, you know, Fiona 305 00:16:46,600 --> 00:16:53,600 Speaker 1: Apple has again incredibly poynant songs about her sexual assault 306 00:16:53,640 --> 00:16:56,600 Speaker 1: as a child. To see those songs take off on 307 00:16:56,640 --> 00:17:00,440 Speaker 1: a platform and become jokes or I'm not even saying 308 00:17:00,480 --> 00:17:02,360 Speaker 1: that that her song would have become a joke, but 309 00:17:02,400 --> 00:17:04,040 Speaker 1: like to be to be used in a way that 310 00:17:04,080 --> 00:17:06,320 Speaker 1: you had not intended and does not feel aligned with 311 00:17:06,359 --> 00:17:08,320 Speaker 1: what you've put out into the world, and then be 312 00:17:08,400 --> 00:17:10,360 Speaker 1: told like, oh no, actually it's good because that means 313 00:17:10,359 --> 00:17:12,600 Speaker 1: people are listening to it and it's being marketed. I 314 00:17:12,640 --> 00:17:14,960 Speaker 1: do think that it's it's art that we should be 315 00:17:15,000 --> 00:17:17,840 Speaker 1: having a serious conversation about what TikTok and social media 316 00:17:17,840 --> 00:17:21,720 Speaker 1: in general has done to the process of making art 317 00:17:21,760 --> 00:17:24,680 Speaker 1: and music in twenty twenty four. I don't necessarily think 318 00:17:24,680 --> 00:17:28,480 Speaker 1: that UMG as a label is actually altruistically trying to 319 00:17:28,520 --> 00:17:30,720 Speaker 1: have that conversation to make sure that artists are getting 320 00:17:31,040 --> 00:17:33,520 Speaker 1: the pay they deserve. That I'm a little skeptical of, 321 00:17:33,720 --> 00:17:35,800 Speaker 1: but I do think it's a conversation we should be having. 322 00:17:36,160 --> 00:17:40,400 Speaker 2: I'm just trying to imagine so many artists are famous 323 00:17:40,400 --> 00:17:44,920 Speaker 2: for being very private. I'm trying to imagine Prince like 324 00:17:45,040 --> 00:17:47,879 Speaker 2: making tiktoks or something, and it's like the image just 325 00:17:47,920 --> 00:17:49,280 Speaker 2: doesn't work right. 326 00:17:49,560 --> 00:17:52,440 Speaker 1: First of all, Prince would never even though Prince was 327 00:17:52,480 --> 00:17:55,239 Speaker 1: a huge techie, Prince would never be on TikTok. But 328 00:17:55,240 --> 00:17:58,399 Speaker 1: that's what I've always said this. If Kurt Cobain was 329 00:17:58,440 --> 00:18:01,320 Speaker 1: still alive, can you imagine how annoying he would be 330 00:18:01,400 --> 00:18:04,040 Speaker 1: on Twitter? Like like some of these musicians like you 331 00:18:04,400 --> 00:18:09,399 Speaker 1: like not every musician is made better by getting a 332 00:18:09,480 --> 00:18:12,760 Speaker 1: lens into their life, into their creative process. And I 333 00:18:12,800 --> 00:18:14,800 Speaker 1: think that we've created this dynamic where if you want 334 00:18:14,840 --> 00:18:17,399 Speaker 1: to succeed and you either a you better be like 335 00:18:17,440 --> 00:18:20,800 Speaker 1: an industry plant or have a huge apparatus behind you 336 00:18:20,920 --> 00:18:23,600 Speaker 1: or something, or you know, have some sort of connection 337 00:18:24,000 --> 00:18:26,800 Speaker 1: or be you better be ready to like be a 338 00:18:26,960 --> 00:18:30,760 Speaker 1: one person content machine shooting them behind the scenes footage 339 00:18:30,760 --> 00:18:33,280 Speaker 1: of how this thing came together. And as somebody who 340 00:18:33,320 --> 00:18:36,480 Speaker 1: makes a creative thing, like the creative process, that's that. 341 00:18:36,560 --> 00:18:39,760 Speaker 1: I'm a musician, but like you know, I make things. 342 00:18:40,480 --> 00:18:43,760 Speaker 1: It's really boring a lot of the times, and it 343 00:18:43,800 --> 00:18:46,400 Speaker 1: has to be it's like trying things, it's like writing, 344 00:18:46,480 --> 00:18:51,679 Speaker 1: it's like journaling. It's like, you know, it's not terribly exciting, 345 00:18:51,840 --> 00:18:55,000 Speaker 1: and so having to like make it seem really cool 346 00:18:55,040 --> 00:18:58,040 Speaker 1: and engaging and like tell a cool story about why 347 00:18:58,080 --> 00:19:00,159 Speaker 1: people should want to check it out on top of it, Well, 348 00:19:00,200 --> 00:19:02,160 Speaker 1: you're already trying to make the thing. It would get 349 00:19:02,200 --> 00:19:03,080 Speaker 1: really hard. 350 00:19:08,200 --> 00:19:09,120 Speaker 3: Let's take a quick break. 351 00:19:20,960 --> 00:19:26,160 Speaker 1: Ed are back, So let's talk about another big story 352 00:19:26,200 --> 00:19:28,600 Speaker 1: this week, which is that CEOs of basically all the 353 00:19:28,640 --> 00:19:33,440 Speaker 1: major social media platforms TikTok, Facebook, Twitter, Discord snap all 354 00:19:33,400 --> 00:19:36,280 Speaker 1: way before said it to testify about how their platforms 355 00:19:36,280 --> 00:19:40,000 Speaker 1: have harmed children's safety. Some of them came voluntarily, but others, 356 00:19:40,040 --> 00:19:44,200 Speaker 1: like Twitter CEO Linda Yakarino, were subpoenaed. The hearing lasted 357 00:19:44,320 --> 00:19:48,400 Speaker 1: four hours but really ended with not many clear resolutions. 358 00:19:48,520 --> 00:19:51,199 Speaker 1: But I will say that the tenseness and the vibe 359 00:19:51,280 --> 00:19:54,200 Speaker 1: of that hearing made it clear to me that tech 360 00:19:54,320 --> 00:19:56,840 Speaker 1: CEOs are realizing that this is a problem that is 361 00:19:56,880 --> 00:19:58,520 Speaker 1: not going away, that they're not going to be able 362 00:19:58,600 --> 00:20:01,240 Speaker 1: to just like vibe this one that people are really 363 00:20:01,240 --> 00:20:03,600 Speaker 1: gonna want answers and I also think that that era 364 00:20:03,680 --> 00:20:05,520 Speaker 1: that we've talked about on the show before, in our 365 00:20:05,560 --> 00:20:07,879 Speaker 1: episode that we did with Paris Marx from the podcast 366 00:20:08,280 --> 00:20:11,520 Speaker 1: Tech won't save us. That era of the early aughts, 367 00:20:11,600 --> 00:20:15,600 Speaker 1: the early two thousands of tech CEOs are all geniuses 368 00:20:15,640 --> 00:20:17,480 Speaker 1: and they're all doing really cool things. They're going to 369 00:20:17,520 --> 00:20:19,800 Speaker 1: save the world. We should have endless optimism on what 370 00:20:19,800 --> 00:20:24,040 Speaker 1: they're doing. That era is like firmly coming to an end. 371 00:20:24,119 --> 00:20:27,840 Speaker 1: I think that tech CEOs have enjoyed this benefit of 372 00:20:27,840 --> 00:20:30,760 Speaker 1: the doubt culturally for a really long time. I think 373 00:20:30,800 --> 00:20:34,399 Speaker 1: that this hearing makes it clear that that era is 374 00:20:34,480 --> 00:20:37,359 Speaker 1: over and that people want answers on how they got 375 00:20:37,440 --> 00:20:40,160 Speaker 1: rich from hurting kids. So if you didn't watch the hearing, 376 00:20:40,240 --> 00:20:42,200 Speaker 1: here are the highlights that you should know. So, first 377 00:20:42,200 --> 00:20:44,000 Speaker 1: of all, as I said, it was just like the 378 00:20:44,440 --> 00:20:48,879 Speaker 1: tone was very aggressive. Lawmakers were demanding that like CEOs 379 00:20:48,920 --> 00:20:52,640 Speaker 1: apologized for hurting kids. Lindsay Graham of South Carolina said 380 00:20:52,640 --> 00:20:54,919 Speaker 1: that companies had blood on their hands. 381 00:20:55,160 --> 00:20:58,240 Speaker 4: You have blood on your hands, you have a product, 382 00:21:00,119 --> 00:21:03,479 Speaker 4: you have a product that's killing people. 383 00:21:03,760 --> 00:21:05,840 Speaker 1: And we got to talk about Mark Zuckerberg here because 384 00:21:05,880 --> 00:21:07,960 Speaker 1: he actually, as far as I know, for the first 385 00:21:07,960 --> 00:21:11,880 Speaker 1: time ever directly apologized to the families of kids who 386 00:21:11,880 --> 00:21:15,200 Speaker 1: were hurt by Instagram and Facebook in the hearing room, 387 00:21:15,960 --> 00:21:20,479 Speaker 1: the families of young people who died by suicide and 388 00:21:20,560 --> 00:21:23,960 Speaker 1: those deaths were linked to Instagram or Facebook. They were 389 00:21:24,000 --> 00:21:26,560 Speaker 1: there in the hearing holding up framed pictures of their 390 00:21:26,640 --> 00:21:30,439 Speaker 1: lost children. And at one point Josh Holly was like, 391 00:21:30,880 --> 00:21:34,760 Speaker 1: turn around and apologize to those families, and Zuckerberg actually 392 00:21:34,800 --> 00:21:35,800 Speaker 1: did it. Here's what he said. 393 00:21:36,560 --> 00:21:39,240 Speaker 5: There's families of victims here today. Have you apologized to 394 00:21:39,240 --> 00:21:43,640 Speaker 5: the victims? Would you like to do so? Now? Well 395 00:21:43,800 --> 00:21:46,879 Speaker 5: they're here, you're on national television. Would you like now 396 00:21:46,920 --> 00:21:49,359 Speaker 5: to apologize to the victims who have been harmed by 397 00:21:49,359 --> 00:21:49,760 Speaker 5: your product? 398 00:21:49,760 --> 00:21:50,640 Speaker 6: Show them the pictures? 399 00:21:51,640 --> 00:21:53,920 Speaker 5: Would you like to apologize for what you've done to 400 00:21:53,960 --> 00:21:54,639 Speaker 5: these good people? 401 00:21:58,200 --> 00:22:06,480 Speaker 6: Because things that your families have suffered and this is 402 00:22:06,520 --> 00:22:06,840 Speaker 6: why we. 403 00:22:06,920 --> 00:22:20,159 Speaker 1: Invested so much better. So, even though Zuckerberg gave this 404 00:22:20,240 --> 00:22:22,960 Speaker 1: apology to the families in the room, he would not 405 00:22:23,080 --> 00:22:25,840 Speaker 1: agree to set up any kind of compensation fund to 406 00:22:25,880 --> 00:22:30,120 Speaker 1: compensate the families of young people hurt by his products. 407 00:22:30,119 --> 00:22:33,600 Speaker 1: And so, yeah, I'll give you an apology that's kind 408 00:22:33,600 --> 00:22:35,720 Speaker 1: of like harangued out of him by Josh Holly. But 409 00:22:36,960 --> 00:22:41,800 Speaker 1: financial compensation, a compensation fund cannot do but take take 410 00:22:41,840 --> 00:22:43,880 Speaker 1: these apologies, though these words might help. 411 00:22:44,560 --> 00:22:48,639 Speaker 2: It's like one of my favorite Futurama episodes when Hermes's 412 00:22:48,760 --> 00:22:52,720 Speaker 2: son has like broken a window until her Hermes is 413 00:22:52,760 --> 00:22:56,479 Speaker 2: taking him to apologize and his son says like, oh am, 414 00:22:56,520 --> 00:22:57,679 Speaker 2: I gonna have to pay for it, and He's like, 415 00:22:57,680 --> 00:22:59,719 Speaker 2: oh no, no, just cheap cheap words. 416 00:23:02,160 --> 00:23:05,000 Speaker 1: Yeah, cheap cheap words is all Mark Zuckerberg's got for us. 417 00:23:05,480 --> 00:23:07,800 Speaker 1: So I'll throw a link to it in the show notes. 418 00:23:07,840 --> 00:23:10,480 Speaker 1: But there was this great op ed in the Hill 419 00:23:10,520 --> 00:23:12,960 Speaker 1: from Julie Scalfo, who is a journalist and the executive 420 00:23:12,960 --> 00:23:16,240 Speaker 1: director of Get Media Savvy, a nonprofit working to establish 421 00:23:16,280 --> 00:23:18,879 Speaker 1: a healthier media vibe for kids and families, and it 422 00:23:18,960 --> 00:23:21,919 Speaker 1: really gets to the heart of the ghoulishness that was 423 00:23:21,920 --> 00:23:25,840 Speaker 1: on display when tech leaders like Mark Zuckerberg treat our 424 00:23:25,920 --> 00:23:29,160 Speaker 1: children like commodities. So the whole piece is worth a read, 425 00:23:29,200 --> 00:23:33,040 Speaker 1: but listen to this quote. As a CEO, Zuckerberg isn't 426 00:23:33,040 --> 00:23:35,560 Speaker 1: willing to spend a cent over two hundred and seventy 427 00:23:35,640 --> 00:23:38,879 Speaker 1: dollars each to keep our kids safe, not even if 428 00:23:38,920 --> 00:23:41,640 Speaker 1: their lives depend on it, and they might just ask 429 00:23:41,680 --> 00:23:44,160 Speaker 1: the parents of Angly Roberts, a fourteen year old from 430 00:23:44,160 --> 00:23:47,560 Speaker 1: Louisiana who took her own life after Instagram's algorithm sent 431 00:23:47,600 --> 00:23:51,160 Speaker 1: her numerous mentally harmful posts, they say, including one modeling 432 00:23:51,160 --> 00:23:54,159 Speaker 1: self strangulation. Or ask the parents of Gavin Guffie, a 433 00:23:54,200 --> 00:23:57,080 Speaker 1: seventeen year old from South Carolina who died by suicide 434 00:23:57,080 --> 00:24:00,119 Speaker 1: after being sex storted. Although Meta has denied put in a 435 00:24:00,160 --> 00:24:04,199 Speaker 1: monetary value on kids, a recently released unredacted version of 436 00:24:04,200 --> 00:24:06,919 Speaker 1: a lawsuit brought by attorneys general from thirty three states 437 00:24:07,200 --> 00:24:10,880 Speaker 1: includes an internal email showing how Meta characterized its youngest 438 00:24:10,960 --> 00:24:14,919 Speaker 1: users in twenty eighteen quote the lifetime value of a 439 00:24:15,000 --> 00:24:18,160 Speaker 1: thirteen year old is roughly two hundred and seventy dollars 440 00:24:18,320 --> 00:24:21,680 Speaker 1: per teen. The email went on to caution Meta employees 441 00:24:21,720 --> 00:24:24,879 Speaker 1: that this number is core to making decisions about your business, 442 00:24:24,960 --> 00:24:27,199 Speaker 1: and accordingly, you do not want to spend more than 443 00:24:27,200 --> 00:24:30,760 Speaker 1: the lifetime value of the user. In other words, our 444 00:24:30,840 --> 00:24:33,360 Speaker 1: kids are profit centers, and it doesn't make business sense 445 00:24:33,400 --> 00:24:35,480 Speaker 1: for Meta to spend more than two hundred and seventy 446 00:24:35,520 --> 00:24:38,400 Speaker 1: dollars to make the platform safe for them. As a corporation. 447 00:24:38,880 --> 00:24:42,679 Speaker 1: Meta's goal is to maximize profits, even apparently if that 448 00:24:42,840 --> 00:24:48,000 Speaker 1: means more kids die. And I feel like that really 449 00:24:48,119 --> 00:24:51,640 Speaker 1: gets at what we're talking about here, that they are 450 00:24:51,680 --> 00:24:55,680 Speaker 1: putting a monetary value on the lives of our children 451 00:24:56,320 --> 00:24:59,959 Speaker 1: and that's core to their business model. And you know, 452 00:25:00,080 --> 00:25:03,120 Speaker 1: I think it's gonna take a little more than Zuckerbird's cheap, 453 00:25:03,200 --> 00:25:07,280 Speaker 1: cheap words here to make that right and any meaningful capacity. 454 00:25:07,800 --> 00:25:12,560 Speaker 2: Yeah, well, there's the there's so much more harm too 455 00:25:12,600 --> 00:25:16,439 Speaker 2: than you know, kids who don't die, right, There are 456 00:25:16,520 --> 00:25:20,840 Speaker 2: kids whose self esteem is really hurt. They can end 457 00:25:20,920 --> 00:25:22,800 Speaker 2: up with a lot of trauma that they're they have 458 00:25:22,840 --> 00:25:25,119 Speaker 2: to carry around. And Yeah, and a friend of mine 459 00:25:25,160 --> 00:25:28,280 Speaker 2: is a defense attorney. He's a public defender, and so 460 00:25:28,320 --> 00:25:33,080 Speaker 2: he's often dealing, you know, defending young people who've been 461 00:25:33,240 --> 00:25:37,160 Speaker 2: involved in some kind of dumb crime. And he says that, 462 00:25:37,400 --> 00:25:40,200 Speaker 2: like all of them have some sort of social media component. 463 00:25:40,520 --> 00:25:44,760 Speaker 2: So that's like even another way that social media is 464 00:25:45,000 --> 00:25:46,720 Speaker 2: like can destroy lives. 465 00:25:47,000 --> 00:25:50,480 Speaker 1: Yeah, the harm it makes. It's upsetting that we're talking 466 00:25:50,520 --> 00:25:55,000 Speaker 1: about children who are no longer with us, our babies, 467 00:25:55,480 --> 00:26:00,320 Speaker 1: but the harm is so vast and diverse. It's just 468 00:26:00,800 --> 00:26:04,640 Speaker 1: it's just sickening and the scope is sickening and shocking. 469 00:26:04,960 --> 00:26:09,879 Speaker 2: And in addition to compensating the families who've been affected, 470 00:26:10,960 --> 00:26:14,240 Speaker 2: it's also important to put something in place to stop 471 00:26:14,320 --> 00:26:17,480 Speaker 2: new kids from being exposed to these same harms. 472 00:26:17,600 --> 00:26:20,000 Speaker 1: Well, that's what I'm saying, is that it feels like 473 00:26:20,040 --> 00:26:22,480 Speaker 1: the dynamic that we have when it comes to big 474 00:26:22,560 --> 00:26:25,800 Speaker 1: tech and just harms in general, is that at the 475 00:26:25,960 --> 00:26:30,399 Speaker 1: very the very best they can offer is compensation after 476 00:26:30,520 --> 00:26:33,439 Speaker 1: harm has occurred. And when we're talking about dead kids, 477 00:26:33,840 --> 00:26:35,560 Speaker 1: I don't give a fuck how much money you give 478 00:26:35,560 --> 00:26:37,880 Speaker 1: me if my kid is hurt, if my kid dies 479 00:26:37,920 --> 00:26:40,879 Speaker 1: by suicide, Like, there's not a dollar amount on this 480 00:26:41,040 --> 00:26:45,040 Speaker 1: earth that would satisfy me. I Yeah, So I would 481 00:26:45,119 --> 00:26:47,560 Speaker 1: hope for a dynamic that can be better, that can say, 482 00:26:47,560 --> 00:26:50,440 Speaker 1: like we can prevent harm. We can do more than 483 00:26:50,560 --> 00:26:56,000 Speaker 1: just do something reactive after something bad happens. We know 484 00:26:56,119 --> 00:26:58,560 Speaker 1: it's happening, we know it's going to continue to happen. 485 00:26:58,880 --> 00:27:00,919 Speaker 1: What if we put things in place to prevent it 486 00:27:00,960 --> 00:27:04,560 Speaker 1: from continuing to happen? Like, I just think that it 487 00:27:04,720 --> 00:27:07,080 Speaker 1: sickens me that this is this is apparently like the 488 00:27:07,119 --> 00:27:09,640 Speaker 1: best they can do is like after a harm has occurred, 489 00:27:09,920 --> 00:27:12,159 Speaker 1: maybe we can step in and talk what if we 490 00:27:12,200 --> 00:27:14,440 Speaker 1: had a better system that did not need for people 491 00:27:14,440 --> 00:27:16,159 Speaker 1: to be hurt before somebody did something. 492 00:27:16,440 --> 00:27:19,399 Speaker 2: Yeah, it would that would be nice. You know. I 493 00:27:19,440 --> 00:27:22,879 Speaker 2: was talking with a friend yesterday about that hearing, and 494 00:27:23,800 --> 00:27:28,359 Speaker 2: this friend was like, it seems really optimistic about something 495 00:27:28,440 --> 00:27:31,000 Speaker 2: that some sort of meaningful change would come out of this. 496 00:27:31,080 --> 00:27:32,600 Speaker 2: He was talking about all. You know, it seemed like 497 00:27:32,640 --> 00:27:36,360 Speaker 2: there was bipartisan support and both Republicans and Democrats were 498 00:27:36,359 --> 00:27:41,400 Speaker 2: really like giving it to the tech executives. But it's, 499 00:27:41,520 --> 00:27:43,040 Speaker 2: as far as I can tell, it's a lot of 500 00:27:43,760 --> 00:27:49,280 Speaker 2: bipartisan support for grand standing and stunts and you know, 501 00:27:49,400 --> 00:27:53,840 Speaker 2: yelling at CEOs and forcing them to apologize. I would 502 00:27:53,880 --> 00:27:58,119 Speaker 2: really love to see some like actual action, some legislation 503 00:27:58,640 --> 00:28:01,520 Speaker 2: that would do something meaningful here. 504 00:28:02,000 --> 00:28:05,639 Speaker 1: Absolutely same, so of course it would not be a 505 00:28:05,720 --> 00:28:10,040 Speaker 1: Senate hearing where lawmakers talks to TikTok CEO, show z 506 00:28:10,040 --> 00:28:14,240 Speaker 1: echo without getting real racist with it. Here's Tom Cotton. 507 00:28:14,800 --> 00:28:17,560 Speaker 4: So you've said today, as you often say, that you 508 00:28:17,760 --> 00:28:20,000 Speaker 4: live in Singapore of what nation? 509 00:28:20,200 --> 00:28:22,200 Speaker 6: Are you a citizen Singapore? 510 00:28:22,320 --> 00:28:22,359 Speaker 2: Is? 511 00:28:22,840 --> 00:28:24,360 Speaker 6: Are you a citizen of any other nation? 512 00:28:24,840 --> 00:28:24,919 Speaker 2: No? 513 00:28:25,040 --> 00:28:28,399 Speaker 6: Senator have you ever applied for Chinese citizenship? Senator, I 514 00:28:28,520 --> 00:28:31,679 Speaker 6: serve my nation in Singapore. No, I did not. Do 515 00:28:31,720 --> 00:28:34,480 Speaker 6: you have a Singaporean passport? Yes, and I served my 516 00:28:34,520 --> 00:28:35,880 Speaker 6: military for two and a half years. 517 00:28:35,880 --> 00:28:37,840 Speaker 4: Since you have any other Do you have any other 518 00:28:37,840 --> 00:28:39,040 Speaker 4: passports from any other names? 519 00:28:39,080 --> 00:28:39,600 Speaker 6: No, Senator. 520 00:28:39,880 --> 00:28:42,760 Speaker 4: Your wife is an American citizen. Your children are American citizens, 521 00:28:42,800 --> 00:28:45,200 Speaker 4: that's correct. Have you ever applied for American citizenship? 522 00:28:45,720 --> 00:28:45,760 Speaker 2: No? 523 00:28:46,400 --> 00:28:51,400 Speaker 6: Not yet. Okay. Have you ever been a member of 524 00:28:51,400 --> 00:28:54,000 Speaker 6: the Chinese Communist Party, Senata, I'm Singaporean. 525 00:28:54,120 --> 00:28:54,280 Speaker 3: No. 526 00:28:55,080 --> 00:28:57,280 Speaker 4: Have you ever been associated or affiliated with the Chinese 527 00:28:57,320 --> 00:28:58,120 Speaker 4: Communist Party? 528 00:28:58,440 --> 00:29:03,120 Speaker 1: No, Senator again, I'm singapore So yeah, Tom Cotton, what 529 00:29:03,280 --> 00:29:04,360 Speaker 1: is there even really to say? 530 00:29:05,000 --> 00:29:08,240 Speaker 2: Yeah, it's bad. I can't believe that wasn't a bigger thing. 531 00:29:09,320 --> 00:29:11,520 Speaker 2: Like that's what I'm saying, And that's all they've got. 532 00:29:11,720 --> 00:29:15,400 Speaker 2: It's like, not only is it grand standing, but it's 533 00:29:15,440 --> 00:29:21,640 Speaker 2: like racist grand standing. Where are the solutions? All they've 534 00:29:21,640 --> 00:29:26,360 Speaker 2: got is the Kids Online Safety Act, which, as we've 535 00:29:26,360 --> 00:29:31,120 Speaker 2: talked about before, is not gonna do It's it's just 536 00:29:31,240 --> 00:29:36,280 Speaker 2: not meaningful legislation that's gonna be workable to protect kids. 537 00:29:36,560 --> 00:29:38,960 Speaker 2: But that's all they've got So. 538 00:29:39,040 --> 00:29:43,240 Speaker 1: That is actually the only tangible thing that came out 539 00:29:43,240 --> 00:29:46,400 Speaker 1: of this this hearing. Like for all of the grand 540 00:29:46,400 --> 00:29:49,880 Speaker 1: standing and all of that, it ended with some leaders 541 00:29:49,920 --> 00:29:52,440 Speaker 1: of platforms agreeing to support the Kids Online Safety Act, 542 00:29:52,440 --> 00:29:54,440 Speaker 1: which you'll probably know if I'm listening to this podcast, 543 00:29:54,440 --> 00:29:57,680 Speaker 1: we are not too thrilled about over here. Separately, Microsoft 544 00:29:57,880 --> 00:30:00,360 Speaker 1: also came out in supported the Kids Online Safety This 545 00:30:00,400 --> 00:30:03,120 Speaker 1: week too. Our producer and guest co host Joey has 546 00:30:03,160 --> 00:30:05,920 Speaker 1: a great episode of the podcast Stuff Mom Ever Told 547 00:30:05,960 --> 00:30:08,840 Speaker 1: You breaking down how the Kids Online Safety Act is 548 00:30:08,840 --> 00:30:13,040 Speaker 1: poised to hurt LGBTQ youth, So legislation that is all 549 00:30:13,080 --> 00:30:16,560 Speaker 1: about protecting youth is actually poised to hurt those same youth. 550 00:30:16,760 --> 00:30:19,080 Speaker 1: We will put the episode in the show notes. Definitely 551 00:30:19,120 --> 00:30:22,040 Speaker 1: worth a listen. Yeah, out of these hearings, Evan Spiegel, 552 00:30:22,200 --> 00:30:25,560 Speaker 1: the CEO of Snap, and Linda Yakarino, who leads Twitter 553 00:30:25,920 --> 00:30:30,240 Speaker 1: both agreed to support the Kids Online Safety Act. And yeah, 554 00:30:30,240 --> 00:30:33,680 Speaker 1: it's just not That's what makes me think that all 555 00:30:33,720 --> 00:30:36,920 Speaker 1: of this is just bloviating and grandstanding, Right, for all 556 00:30:36,920 --> 00:30:39,600 Speaker 1: of this grandstanding, for all of this, like Josh Holly 557 00:30:39,680 --> 00:30:43,200 Speaker 1: making Zuckerberg turn around and apologize to those families. None 558 00:30:43,200 --> 00:30:46,240 Speaker 1: of it really means anything, and it just feels like theater. 559 00:30:46,400 --> 00:30:50,120 Speaker 1: It feels like subitting your wheels, Like despite years of 560 00:30:50,640 --> 00:30:54,920 Speaker 1: railing against big tech, no meaningful legislation has moved through 561 00:30:54,920 --> 00:30:58,240 Speaker 1: Congress to be signed into law. So I again I've 562 00:30:58,280 --> 00:31:03,080 Speaker 1: said this. I think this is just grandstanding to look 563 00:31:03,280 --> 00:31:06,800 Speaker 1: like we're being tough on big tech without actually doing 564 00:31:06,840 --> 00:31:08,680 Speaker 1: anything to be tough on big tech. They want to 565 00:31:08,720 --> 00:31:11,280 Speaker 1: signal to people that they're doing something big and taking 566 00:31:11,280 --> 00:31:14,320 Speaker 1: it seriously. They want to throw a little like anti 567 00:31:14,440 --> 00:31:17,480 Speaker 1: Asian xenophobia in there for good measure and call it 568 00:31:17,560 --> 00:31:18,600 Speaker 1: a day and call it good. 569 00:31:19,200 --> 00:31:21,000 Speaker 2: Yeah, they've got to do better. 570 00:31:21,480 --> 00:31:22,920 Speaker 1: So I have a little update on a story that 571 00:31:22,920 --> 00:31:25,600 Speaker 1: we talked about last week, and that is that AI 572 00:31:25,920 --> 00:31:29,680 Speaker 1: generated Joe Biden deep fake robocall that voters in New 573 00:31:29,680 --> 00:31:34,280 Speaker 1: Hampshire got right before the primary asking them not to vote. Well, 574 00:31:34,320 --> 00:31:36,760 Speaker 1: I guess that was a literal wake up call to 575 00:31:36,800 --> 00:31:39,640 Speaker 1: the powers that be, because the FCC now wants to 576 00:31:39,720 --> 00:31:44,040 Speaker 1: criminalize virtually all AI generated robocalls because of it. This 577 00:31:44,080 --> 00:31:48,440 Speaker 1: proposal would outlaw such robocalls under the Telephone Consumer Protection Act, 578 00:31:48,520 --> 00:31:51,720 Speaker 1: or TCPA, which is a nineteen ninety one law that 579 00:31:51,760 --> 00:31:55,880 Speaker 1: regulates automated political and marketing calls made without the receiver's consent. 580 00:31:56,320 --> 00:31:58,959 Speaker 1: If the TCPA sounds familiar, it is because it's been 581 00:31:59,080 --> 00:32:02,880 Speaker 1: used before a people who make illegal robocalls. A big 582 00:32:02,920 --> 00:32:05,920 Speaker 1: case happened last year, and the FCC imposed a five 583 00:32:05,960 --> 00:32:10,200 Speaker 1: million dollar penalty against conservative activists who arranged for black 584 00:32:10,280 --> 00:32:14,160 Speaker 1: voters to receive calls falsely telling them that voting could 585 00:32:14,160 --> 00:32:17,400 Speaker 1: expose them to deck collectors and police departments in twenty twenty. 586 00:32:17,560 --> 00:32:23,920 Speaker 1: So obviously a disinformation tactic really grounded in stoking very real, 587 00:32:24,520 --> 00:32:28,360 Speaker 1: highly racialized fears and traumas like oh, if you vote, 588 00:32:28,560 --> 00:32:30,000 Speaker 1: you know, the police are going to get you, deck 589 00:32:30,000 --> 00:32:32,360 Speaker 1: collectors are going to get you. I've heard of this 590 00:32:32,480 --> 00:32:35,280 Speaker 1: kind of thing happening all the time, like with like 591 00:32:35,360 --> 00:32:38,720 Speaker 1: paper flyers being left at black churches that say like, oh, 592 00:32:38,720 --> 00:32:41,120 Speaker 1: if you go to vote, you know they run your 593 00:32:41,200 --> 00:32:44,160 Speaker 1: name through the outstanding warrant database, which is not true, 594 00:32:44,480 --> 00:32:47,640 Speaker 1: or they run your name through the outstanding child support database, 595 00:32:47,680 --> 00:32:51,520 Speaker 1: which is not true. And obviously those are like racialized, 596 00:32:51,760 --> 00:32:55,440 Speaker 1: racially charged tactics meant to stoke at like very real 597 00:32:55,520 --> 00:32:59,840 Speaker 1: fears and traumas that our communities have. So that's bad enough, 598 00:33:00,040 --> 00:33:02,840 Speaker 1: and it's paper flyers being put on cars, But with 599 00:33:02,880 --> 00:33:05,880 Speaker 1: the power of AI and robo call technology, there is 600 00:33:05,920 --> 00:33:08,080 Speaker 1: no end to how to how damaging this could be 601 00:33:08,160 --> 00:33:08,840 Speaker 1: to democracy. 602 00:33:09,280 --> 00:33:14,440 Speaker 2: Yeah, that sounds like a like a good regulation, right, 603 00:33:14,440 --> 00:33:18,719 Speaker 2: like government using its authority to prohibit like a whole 604 00:33:18,760 --> 00:33:23,720 Speaker 2: category of AI generated deception. That seems great. 605 00:33:24,200 --> 00:33:27,600 Speaker 1: So NBC reports that basically this change will particularly empower 606 00:33:27,760 --> 00:33:31,760 Speaker 1: state attorneys general to take legal action against spammers who 607 00:33:31,880 --> 00:33:36,240 Speaker 1: use AI. So, yeah, I think if that Joe Biden 608 00:33:36,480 --> 00:33:39,880 Speaker 1: AI deep fake robo call was any indication we need 609 00:33:39,960 --> 00:33:42,120 Speaker 1: legislation like this to make sure people aren't out there 610 00:33:42,440 --> 00:33:43,600 Speaker 1: running wild with AI. 611 00:33:44,360 --> 00:33:46,520 Speaker 2: Yeah, and you know they're gonna It's already. 612 00:33:46,160 --> 00:33:48,760 Speaker 1: Clear they're gunna Like. We don't need to again, just 613 00:33:48,760 --> 00:33:50,640 Speaker 1: like what I was saying before, We don't need to 614 00:33:50,720 --> 00:33:54,280 Speaker 1: wait until the harm happens. We can see it like 615 00:33:54,400 --> 00:33:56,560 Speaker 1: they like, it's clear they're going to keep doing it. 616 00:33:56,680 --> 00:33:59,480 Speaker 1: We can make changes now to prevent it people. 617 00:34:00,080 --> 00:34:03,800 Speaker 2: Yeah, prevention, you know that's so much better than trying 618 00:34:03,800 --> 00:34:05,240 Speaker 2: to fix something after it's broken. 619 00:34:09,719 --> 00:34:20,880 Speaker 3: More after a quick break. 620 00:34:22,040 --> 00:34:28,880 Speaker 1: Let's get right back into it. So, speaking of AI, 621 00:34:29,280 --> 00:34:32,240 Speaker 1: let's go to Australia because this is a weird situation 622 00:34:32,360 --> 00:34:37,040 Speaker 1: happening with an Australian lawmaker. Victorious parliament member Georgie Purcell 623 00:34:37,160 --> 00:34:39,839 Speaker 1: saw a picture of herself on local news and was like, wait, 624 00:34:40,280 --> 00:34:43,879 Speaker 1: something is off with this picture. She was originally photographed 625 00:34:43,880 --> 00:34:47,080 Speaker 1: wearing a white sleeveless dress, but in the picture that 626 00:34:47,160 --> 00:34:50,200 Speaker 1: she saw on the local news, her torso or like 627 00:34:50,239 --> 00:34:52,480 Speaker 1: her mid drip, is exposed, and she was like, wait 628 00:34:52,480 --> 00:34:55,880 Speaker 1: a minute, I am heavily tattooed on my torso area. 629 00:34:56,400 --> 00:34:58,640 Speaker 1: The part of my body that is exposed in this 630 00:34:58,760 --> 00:35:01,960 Speaker 1: image doesn't have tattoos that I know I actually have. 631 00:35:02,520 --> 00:35:04,680 Speaker 1: The picture also made her chest look a little bit 632 00:35:04,719 --> 00:35:07,600 Speaker 1: fuller too, and her dress, which initially had been a 633 00:35:07,600 --> 00:35:09,840 Speaker 1: big a one piece, had been turned into like a 634 00:35:09,880 --> 00:35:13,080 Speaker 1: crop pap or. Her middrift was exposed. She says, they'd 635 00:35:13,080 --> 00:35:15,960 Speaker 1: given me chiseled abs and a boob job. I felt really, 636 00:35:16,000 --> 00:35:20,279 Speaker 1: really uncomfortable about it. Well nine News in Australia apologized 637 00:35:20,320 --> 00:35:23,120 Speaker 1: and said it was a quote graphics error and blamed 638 00:35:23,160 --> 00:35:27,040 Speaker 1: photoshops automation tool, saying that in trying to resize the image, 639 00:35:27,120 --> 00:35:30,080 Speaker 1: the automation by photoshop created an image that was not 640 00:35:30,280 --> 00:35:34,520 Speaker 1: consistent with the original. However, the New York Times was like, 641 00:35:35,160 --> 00:35:37,799 Speaker 1: I don't know about that. They talked to Adobe, the 642 00:35:37,880 --> 00:35:41,080 Speaker 1: company that makes Photoshop, and a representative for Adobe said 643 00:35:41,280 --> 00:35:44,320 Speaker 1: that the edits to the image would have required human 644 00:35:44,400 --> 00:35:47,880 Speaker 1: intervention and approval. So this New York Times write up 645 00:35:47,920 --> 00:35:52,040 Speaker 1: is actually very interesting, it says. Some commentators familiar with 646 00:35:52,080 --> 00:35:55,360 Speaker 1: working with Photoshop have suggested that if artificial intelligence is 647 00:35:55,360 --> 00:35:58,400 Speaker 1: at fault, the modifications could have been made using a 648 00:35:58,400 --> 00:36:01,600 Speaker 1: Photoshop tool that fills in space above or below an 649 00:36:01,600 --> 00:36:05,680 Speaker 1: image with an automatically generated continuation of the image. Others, 650 00:36:05,840 --> 00:36:08,560 Speaker 1: like Rob Nichols, a professorial fellow at the University of 651 00:36:08,600 --> 00:36:11,440 Speaker 1: Technology Sydney, said the changes could have been made with 652 00:36:11,480 --> 00:36:15,320 Speaker 1: an automatic enhancement function similar to selfie filters that modify 653 00:36:15,400 --> 00:36:19,360 Speaker 1: someone's facial features. The broadcast of the image seemingly without 654 00:36:19,360 --> 00:36:21,600 Speaker 1: someone checking that it was an accurate depiction of miss 655 00:36:21,640 --> 00:36:26,520 Speaker 1: Purcell shows that using AI without strong editorial controls runs 656 00:36:26,560 --> 00:36:30,120 Speaker 1: the risk of making very significant errors. The incident shows 657 00:36:30,120 --> 00:36:33,359 Speaker 1: that AI can replicate existing biases. I don't think it's 658 00:36:33,400 --> 00:36:36,719 Speaker 1: coincidental that these issues tend to be gendered. This is 659 00:36:36,719 --> 00:36:39,840 Speaker 1: something that we know about AI. If there was one 660 00:36:40,000 --> 00:36:42,600 Speaker 1: thing about AI that I wish people understood, it is 661 00:36:42,640 --> 00:36:47,719 Speaker 1: that it is not like robot computer brains making decisions 662 00:36:48,160 --> 00:36:52,600 Speaker 1: because they're hyper intelligent artificially. It is built by humans. 663 00:36:52,680 --> 00:36:56,000 Speaker 1: It is made by humans. Everything that we're seeing when 664 00:36:56,040 --> 00:36:59,040 Speaker 1: we use AI has been put there by a human. 665 00:36:59,160 --> 00:37:03,400 Speaker 1: So all of the biases and shortcomings that we know 666 00:37:03,680 --> 00:37:08,120 Speaker 1: humans have, AI is simply replicating those exact same biases 667 00:37:08,160 --> 00:37:11,279 Speaker 1: and stereotypes and shortcomings because it was put there by 668 00:37:11,360 --> 00:37:14,920 Speaker 1: a human. And so it's no wonder that people, particularly 669 00:37:15,480 --> 00:37:18,160 Speaker 1: people who are not CIS men, have reported that when 670 00:37:18,160 --> 00:37:22,359 Speaker 1: they use AI image generators of themselves, the images are 671 00:37:22,760 --> 00:37:27,120 Speaker 1: made more conventionally attractive. Their busts get bigger, their waists 672 00:37:27,160 --> 00:37:30,640 Speaker 1: get smaller. When I have used AI technology in the 673 00:37:30,719 --> 00:37:32,840 Speaker 1: few times that I have used it, it makes my 674 00:37:33,200 --> 00:37:35,800 Speaker 1: nose more narrow, my lips more narrow, It makes my 675 00:37:36,320 --> 00:37:39,880 Speaker 1: facial features more eurocentric. As a black woman, it makes 676 00:37:40,040 --> 00:37:42,520 Speaker 1: me feel like they're saying like, oh, well, we want 677 00:37:42,520 --> 00:37:44,640 Speaker 1: you to look pretty, and by pretty, we mean we 678 00:37:44,680 --> 00:37:47,680 Speaker 1: want you to look more white. Sometimes it lightens your skin. 679 00:37:48,200 --> 00:37:52,080 Speaker 1: All of these are biases that AI is replicating because 680 00:37:52,080 --> 00:37:55,080 Speaker 1: the people who built it have those biases, and I 681 00:37:55,120 --> 00:37:58,880 Speaker 1: think Rob Nichols is exactly right that AI is just 682 00:37:59,000 --> 00:38:02,600 Speaker 1: replicating egs. This did deeply gendered biases here. 683 00:38:03,200 --> 00:38:08,400 Speaker 2: Yeah, definitely, well said. You know, it's an algorithm that 684 00:38:08,640 --> 00:38:12,200 Speaker 2: is trying to replicate the data that it's fed and 685 00:38:12,280 --> 00:38:17,120 Speaker 2: trained on, and if the data is sexist and racist, yeah, 686 00:38:17,160 --> 00:38:19,840 Speaker 2: that's what you're going to end up with in the AI. 687 00:38:20,800 --> 00:38:23,160 Speaker 2: But it's not clear to me that this even I mean, 688 00:38:23,200 --> 00:38:27,080 Speaker 2: I guess it it was AI, but I feel like 689 00:38:27,160 --> 00:38:30,920 Speaker 2: the news company is really trying to pull a fast 690 00:38:30,960 --> 00:38:34,200 Speaker 2: one saying that like a human didn't do this, Like 691 00:38:34,520 --> 00:38:37,799 Speaker 2: when you resize an image, it doesn't photoshop doesn't like 692 00:38:37,880 --> 00:38:43,080 Speaker 2: automatically alter it in this way. Like there wasn't there. 693 00:38:43,280 --> 00:38:46,319 Speaker 2: It's not just a case that like the you know, 694 00:38:46,360 --> 00:38:50,959 Speaker 2: the AI is here is replicating biases from the world, 695 00:38:50,960 --> 00:38:53,520 Speaker 2: which it is, but also like there was a human 696 00:38:53,600 --> 00:38:57,720 Speaker 2: sitting at a desk clicking the button to like enhance 697 00:38:57,760 --> 00:38:58,960 Speaker 2: the image or something like. 698 00:38:58,960 --> 00:39:02,040 Speaker 1: That, sitting at a desk clicking a button dead haantsy 699 00:39:02,120 --> 00:39:05,760 Speaker 1: image and then most importantly not double checking the image 700 00:39:05,800 --> 00:39:08,799 Speaker 1: after that human clicks the button. Like that's the that's 701 00:39:08,800 --> 00:39:13,000 Speaker 1: the big thing here. Is like, Okay, using an automated 702 00:39:13,040 --> 00:39:17,200 Speaker 1: tool to fill in blank space, sure, but then you 703 00:39:17,280 --> 00:39:19,839 Speaker 1: have to look afterward. Like That's the thing about these 704 00:39:19,840 --> 00:39:22,040 Speaker 1: AI tools is like, if you're gonna use them, you 705 00:39:22,160 --> 00:39:24,600 Speaker 1: as the human that need to double check that if 706 00:39:24,600 --> 00:39:28,080 Speaker 1: you're showing this person's torso that it is an accurate 707 00:39:28,160 --> 00:39:30,800 Speaker 1: if you've made the if you've made the already weird 708 00:39:30,880 --> 00:39:33,680 Speaker 1: fucking choice to show this person's torso who did not 709 00:39:33,760 --> 00:39:36,640 Speaker 1: show their torso on the original image, at least be like, well, 710 00:39:37,200 --> 00:39:40,000 Speaker 1: is this an accurate representation of this person's torso? The 711 00:39:40,040 --> 00:39:41,240 Speaker 1: whole thing is already weird. 712 00:39:41,560 --> 00:39:44,319 Speaker 2: Yeah, it is weird. It's and it it feels like 713 00:39:44,320 --> 00:39:47,759 Speaker 2: a the like the dog ate my homework kind of excuse, 714 00:39:48,160 --> 00:39:52,720 Speaker 2: or like, you know, I couldn't figure out how to print, 715 00:39:52,840 --> 00:39:55,359 Speaker 2: so I didn't do my assignment. 716 00:39:55,719 --> 00:39:59,719 Speaker 1: For were you? What did you have microphones in conversations 717 00:39:59,719 --> 00:40:03,480 Speaker 1: I had with my parents and teachers in school. That's 718 00:40:03,520 --> 00:40:06,200 Speaker 1: a bridgid excuse if I ever heard one. 719 00:40:06,880 --> 00:40:10,960 Speaker 2: Yeah, that's what this news company is like basically pointing 720 00:40:11,000 --> 00:40:11,719 Speaker 2: to here. 721 00:40:11,560 --> 00:40:14,399 Speaker 1: Listen, as someone who is in my academic career used 722 00:40:14,400 --> 00:40:17,040 Speaker 1: every excuse in the book. Sometimes he's gonna be like, yeah, 723 00:40:17,040 --> 00:40:18,560 Speaker 1: I should have done a better job and this is 724 00:40:18,560 --> 00:40:23,320 Speaker 1: on me. So something else that Percell, that Australian lawmaker 725 00:40:23,320 --> 00:40:26,960 Speaker 1: whose image was distorted, says here is that she suspects 726 00:40:26,960 --> 00:40:29,479 Speaker 1: that her identity is the reason why the news would 727 00:40:29,480 --> 00:40:32,759 Speaker 1: feel comfortable using this kind of technology to make her 728 00:40:32,800 --> 00:40:35,560 Speaker 1: look a certain way without her permission at all. She says, 729 00:40:35,920 --> 00:40:38,960 Speaker 1: I'm young, I'm blonde, I'm covered in tattoos, I have 730 00:40:39,040 --> 00:40:41,960 Speaker 1: a past in sex work. So I think that she 731 00:40:42,160 --> 00:40:46,080 Speaker 1: is rightly picking up on something that whoever was in 732 00:40:46,160 --> 00:40:49,359 Speaker 1: charge of making this image just didn't feel like they 733 00:40:49,440 --> 00:40:52,880 Speaker 1: needed to have her consent to distort the way that 734 00:40:52,920 --> 00:40:54,879 Speaker 1: she looks in this way. And I do think there's 735 00:40:54,880 --> 00:40:57,160 Speaker 1: something It kind of gets back to that conversation we 736 00:40:57,160 --> 00:40:59,840 Speaker 1: had about the Taylor Swift deep fakes that there's some 737 00:41:00,080 --> 00:41:04,520 Speaker 1: thing about it that is like your body is public 738 00:41:04,640 --> 00:41:08,160 Speaker 1: property and I actually don't need your permission or consent 739 00:41:08,520 --> 00:41:11,160 Speaker 1: to manipulate it using the technology that I have to 740 00:41:11,239 --> 00:41:13,120 Speaker 1: do that. Like, I think I think that Percell is 741 00:41:13,200 --> 00:41:16,239 Speaker 1: onto something here that there are identity aspects that make 742 00:41:16,920 --> 00:41:19,279 Speaker 1: people feel like they don't need to get permission, they 743 00:41:19,280 --> 00:41:21,400 Speaker 1: don't need to get consent the way that your body 744 00:41:21,400 --> 00:41:25,319 Speaker 1: and physicality manifest is just up to me. I have 745 00:41:25,360 --> 00:41:26,839 Speaker 1: control over that, and I don't need to get your 746 00:41:26,840 --> 00:41:27,600 Speaker 1: permission to do that. 747 00:41:28,200 --> 00:41:31,960 Speaker 2: Yeah, this is like a trend that I've noticed with 748 00:41:32,960 --> 00:41:37,040 Speaker 2: news stories lately that you know, and we all on 749 00:41:37,600 --> 00:41:41,480 Speaker 2: my phone on social media. I have lots of places 750 00:41:41,480 --> 00:41:43,440 Speaker 2: where I'm looking at a feed of news stories, right, 751 00:41:43,440 --> 00:41:45,200 Speaker 2: and they're all trying to get me to click on them. 752 00:41:45,600 --> 00:41:47,719 Speaker 2: And I've noticed this trend where there will be a 753 00:41:47,760 --> 00:41:50,920 Speaker 2: lot of news stories in there that are not important 754 00:41:50,960 --> 00:41:56,960 Speaker 2: news stories, but they're about a woman who is conventionally attractive, 755 00:41:57,520 --> 00:42:00,239 Speaker 2: and you know, it'll be like, oh, this woman from 756 00:42:00,360 --> 00:42:03,960 Speaker 2: county in a different state hasn't paid her taxes. It's like, 757 00:42:04,160 --> 00:42:06,439 Speaker 2: why is this a news story? And I think it's 758 00:42:06,520 --> 00:42:12,200 Speaker 2: just because that's a way for whatever algorithm is serving 759 00:42:12,280 --> 00:42:16,440 Speaker 2: up content to get an attractive woman there and just 760 00:42:16,480 --> 00:42:21,400 Speaker 2: like use, yeah, use her body, use her image as clickbait. 761 00:42:22,800 --> 00:42:25,239 Speaker 2: It feels weird and kind of gross. 762 00:42:25,480 --> 00:42:27,719 Speaker 1: So I would actually argue that that is not a 763 00:42:27,840 --> 00:42:31,279 Speaker 1: bug of our current media landscape. It is very much 764 00:42:31,320 --> 00:42:35,400 Speaker 1: a feature using people, people who happen to all be 765 00:42:35,600 --> 00:42:41,200 Speaker 1: traditionally marginalized to generate engagement and thus clicks, and that's 766 00:42:41,320 --> 00:42:44,600 Speaker 1: money for somebody who is likely to not be traditionally marginalized. 767 00:42:44,840 --> 00:42:47,840 Speaker 1: That is like the system that keeps the whole that 768 00:42:47,920 --> 00:42:49,359 Speaker 1: is that the whole thing is built on. And so 769 00:42:49,400 --> 00:42:53,279 Speaker 1: when we have these situations where it exposes, you know, 770 00:42:53,960 --> 00:42:57,719 Speaker 1: that our internet landscape is deeply misogynistic or transphobic, or 771 00:42:57,800 --> 00:43:02,200 Speaker 1: queer phobic or racist, those are not you know, weird 772 00:43:02,280 --> 00:43:06,000 Speaker 1: instances of something happening. They are reminders that like, oh yeah, 773 00:43:06,120 --> 00:43:10,360 Speaker 1: that's because that those things are baked into our entire 774 00:43:10,480 --> 00:43:14,080 Speaker 1: internet landscape right now. So we're just gonna keep repeating that, 775 00:43:14,120 --> 00:43:16,560 Speaker 1: because that's the whole thing. That's what the whole thing, 776 00:43:16,600 --> 00:43:19,600 Speaker 1: the whole house of cards that is today's current digital 777 00:43:19,680 --> 00:43:22,560 Speaker 1: landscape is built on all of those things. And so 778 00:43:22,719 --> 00:43:26,200 Speaker 1: is it any wonder that this same system is not 779 00:43:26,560 --> 00:43:32,520 Speaker 1: fairly or accurately or consistently representing women, queer people, trans people, 780 00:43:32,520 --> 00:43:34,560 Speaker 1: black people, people of color, or anything like that. 781 00:43:35,440 --> 00:43:39,040 Speaker 2: Yeah, it's not surprising at all. So have you got 782 00:43:39,080 --> 00:43:41,719 Speaker 2: some good news to end to send us out with? 783 00:43:42,120 --> 00:43:44,960 Speaker 1: I sure do. I've been saving this story because I 784 00:43:45,200 --> 00:43:46,799 Speaker 1: it's it's one that is near and dear to me 785 00:43:46,960 --> 00:43:50,720 Speaker 1: because y'all know I love a good justice is Serbs story. 786 00:43:51,160 --> 00:43:54,520 Speaker 1: So basically Sandsbury's, which is a grocery store chain in 787 00:43:54,560 --> 00:43:57,319 Speaker 1: the UK, was supportive of Black Lives Matter, and like, 788 00:43:57,719 --> 00:43:59,759 Speaker 1: I think we're doing some sort of like black History 789 00:43:59,760 --> 00:44:04,160 Speaker 1: Monde thing for their black employees. Barely standard stuff. Well, 790 00:44:04,640 --> 00:44:07,960 Speaker 1: right wing activists Lawrence Fox, who founded the right wing 791 00:44:08,040 --> 00:44:11,360 Speaker 1: populist political party Reclaim and is generally the kind of 792 00:44:11,360 --> 00:44:14,800 Speaker 1: person who makes his whole thing like hating anything related 793 00:44:14,800 --> 00:44:16,759 Speaker 1: to diversity. You know, the type we don't need to like, 794 00:44:16,840 --> 00:44:19,759 Speaker 1: you know what I'm saying. Well, he didn't like this, 795 00:44:19,880 --> 00:44:22,279 Speaker 1: and so he called for a boycott of Sansbury's. So 796 00:44:22,320 --> 00:44:25,879 Speaker 1: he got into it on Twitter with Colin Seymour, who 797 00:44:25,960 --> 00:44:29,800 Speaker 1: does is a drag performer. Colin's drag performer name is Crystal. 798 00:44:30,239 --> 00:44:34,640 Speaker 1: So during this exchange on Twitter, Lawrence called Colin Seymour 799 00:44:34,680 --> 00:44:37,680 Speaker 1: a pedophile, which we know is like a dangerous smear, 800 00:44:38,040 --> 00:44:40,759 Speaker 1: This idea that people who do drag, or that LGBTQ 801 00:44:40,880 --> 00:44:44,600 Speaker 1: people or the people who support them are dangerous sexual 802 00:44:44,640 --> 00:44:47,960 Speaker 1: threats to children. It is absolutely baseless not to mention 803 00:44:48,239 --> 00:44:52,160 Speaker 1: dangerous in a time of QAnon. Well, Colin Seymour was like, 804 00:44:52,320 --> 00:44:55,520 Speaker 1: I don't think so and sued Lawrence for libel and 805 00:44:55,680 --> 00:44:58,960 Speaker 1: this week Q one inter ruling at the High Court 806 00:44:59,440 --> 00:45:03,640 Speaker 1: just as Colin described Fox calling him a pedophile as 807 00:45:03,960 --> 00:45:08,799 Speaker 1: seriously harmful, defamatory and baseless, saying the law affords few 808 00:45:08,840 --> 00:45:11,960 Speaker 1: defenses to decimation of this sort. Mister Fox did not 809 00:45:12,000 --> 00:45:14,440 Speaker 1: attempt to show these allegations were true, and he was 810 00:45:14,480 --> 00:45:16,880 Speaker 1: not able to bring himself on the facts within the 811 00:45:17,000 --> 00:45:19,759 Speaker 1: terms of any other defense recognized in the law. So 812 00:45:19,840 --> 00:45:23,600 Speaker 1: Fox tried to counter sue Colin Seymour for calling him 813 00:45:23,600 --> 00:45:27,200 Speaker 1: a racist, but the court dismissed that counterclaim, saying that 814 00:45:27,440 --> 00:45:30,040 Speaker 1: the tweets were unlikely to cause him serious harm to 815 00:45:30,080 --> 00:45:32,759 Speaker 1: his reputation. So this is a good story because I 816 00:45:32,800 --> 00:45:36,479 Speaker 1: love like justice is served some people. If you listen 817 00:45:36,520 --> 00:45:39,400 Speaker 1: to old, old old stuff, Mom ever told you you 818 00:45:39,520 --> 00:45:42,840 Speaker 1: might remember that in the episode that we did revealing 819 00:45:42,840 --> 00:45:46,319 Speaker 1: our problematic saves, Mike, you might know who mine was. 820 00:45:46,760 --> 00:45:48,200 Speaker 2: Oh that's right, who is your Oh? 821 00:45:48,200 --> 00:45:51,520 Speaker 1: Who is my number one lady who serves up justice 822 00:45:52,280 --> 00:45:53,560 Speaker 1: every every day? 823 00:45:54,560 --> 00:45:58,120 Speaker 2: Oh Judge Judy, of course. Ugh, I thought you meant 824 00:45:58,120 --> 00:45:59,359 Speaker 2: a different problematic fave. 825 00:45:59,560 --> 00:46:01,960 Speaker 1: Oh my god, I have so many we don't have 826 00:46:02,040 --> 00:46:04,840 Speaker 1: to get into it. Maybe that's Patreon content. I don't I 827 00:46:04,880 --> 00:46:06,839 Speaker 1: don't want. I don't want to get like we can 828 00:46:06,920 --> 00:46:07,200 Speaker 1: move on. 829 00:46:07,480 --> 00:46:09,960 Speaker 2: But yeah, so I it's already a long episode. We 830 00:46:10,000 --> 00:46:11,719 Speaker 2: don't have time to go through your long list of 831 00:46:11,760 --> 00:46:12,560 Speaker 2: problematic phases. 832 00:46:12,640 --> 00:46:14,680 Speaker 1: I know, I know, no, but I basically what I'm 833 00:46:14,680 --> 00:46:18,280 Speaker 1: saying is like, I love justice. I love watching somebody 834 00:46:18,320 --> 00:46:20,319 Speaker 1: get what they've got come into them, and so this 835 00:46:20,440 --> 00:46:23,880 Speaker 1: story I like, but I want to be clear that like, 836 00:46:24,280 --> 00:46:27,800 Speaker 1: this shit is dangerous. Seymour did an interview with The Independent, 837 00:46:27,840 --> 00:46:30,319 Speaker 1: which honestly the whole thing is worth reading, but in 838 00:46:30,360 --> 00:46:33,520 Speaker 1: it he was talking about like why he sued Fox 839 00:46:33,560 --> 00:46:37,319 Speaker 1: for defamation and basically talks about how all of this 840 00:46:37,440 --> 00:46:41,840 Speaker 1: anti gay bigotry is a real dangerous problem and that 841 00:46:42,200 --> 00:46:47,120 Speaker 1: calling someone a pedophile baselessly is basically the first line 842 00:46:47,200 --> 00:46:51,000 Speaker 1: of a very dangerous attack. He told The Independent. For 843 00:46:51,080 --> 00:46:54,200 Speaker 1: some anti gay bigotry never died, but they realized that 844 00:46:54,280 --> 00:46:57,000 Speaker 1: it becomes socially unacceptable for them to express it. So 845 00:46:57,040 --> 00:46:59,360 Speaker 1: now they found a new avenue where they feel emboldened 846 00:46:59,360 --> 00:47:01,520 Speaker 1: and think that they're to get away with it. Calling 847 00:47:01,520 --> 00:47:04,959 Speaker 1: somebody pedophile groomer or Nazi is often the first line 848 00:47:04,960 --> 00:47:07,919 Speaker 1: of attack, a new way of framing very old homophobia, 849 00:47:08,280 --> 00:47:11,880 Speaker 1: and it's all bound up in modern transpanic. The Independent 850 00:47:11,920 --> 00:47:15,680 Speaker 1: asked if Fox had given any kind of like remorse 851 00:47:16,040 --> 00:47:18,759 Speaker 1: during this case, and the answer is no, of course not. 852 00:47:19,120 --> 00:47:22,440 Speaker 1: Seymour says, it's always doubling down and digging the hole deeper. 853 00:47:22,640 --> 00:47:25,480 Speaker 1: And I think until he demonstrates some actual willingness to 854 00:47:25,560 --> 00:47:28,560 Speaker 1: examine his own behaviors, I can't feel anything for him 855 00:47:28,680 --> 00:47:32,080 Speaker 1: except contempt. And you know what, he is right because 856 00:47:32,360 --> 00:47:35,719 Speaker 1: rather than just like shutting up, Fox is back at it, 857 00:47:35,760 --> 00:47:39,680 Speaker 1: tweeting about the case. Quote. The lifeblood of progressivism is 858 00:47:39,719 --> 00:47:43,200 Speaker 1: the myth of white privilege and systemic racism. Both lies 859 00:47:43,680 --> 00:47:47,000 Speaker 1: certainly in this wonderful country which is being eaten away 860 00:47:47,040 --> 00:47:49,520 Speaker 1: at from the inside by those who would want to 861 00:47:49,560 --> 00:47:53,040 Speaker 1: replace a meritocracy with a melaninocracy. 862 00:47:54,800 --> 00:47:56,600 Speaker 2: Is that a word that you've seen before? This is 863 00:47:56,640 --> 00:47:57,600 Speaker 2: my first time. 864 00:47:58,040 --> 00:48:02,279 Speaker 1: I have not seen. I have not seen melananocracy. In 865 00:48:02,280 --> 00:48:04,640 Speaker 1: case you don't know, melanin is the thing that makes 866 00:48:04,760 --> 00:48:07,719 Speaker 1: people of color our skin, uh the way that it 867 00:48:07,760 --> 00:48:13,000 Speaker 1: is beautiful brown shades. I have not heard melananocracy before. 868 00:48:13,280 --> 00:48:16,239 Speaker 1: But best believe I'm about to steal it because this 869 00:48:16,440 --> 00:48:21,760 Speaker 1: whole month we are celebrating what we're now calling the Melananocracy. 870 00:48:22,120 --> 00:48:25,400 Speaker 2: So how does that work in a melananocracy? Like do 871 00:48:25,960 --> 00:48:28,680 Speaker 2: the people with the most melanin get to be in charge? 872 00:48:28,800 --> 00:48:30,960 Speaker 2: Is that? Is that what it is here? 873 00:48:31,360 --> 00:48:35,200 Speaker 1: I think that's what he is implying is how it works. 874 00:48:35,440 --> 00:48:37,879 Speaker 1: And you know what, I'm here for it. So we're 875 00:48:37,920 --> 00:48:42,560 Speaker 1: having a one month Melananocracy for February. Spread it around. 876 00:48:42,640 --> 00:48:44,600 Speaker 1: It's going to be the new thing. That's it. 877 00:48:45,200 --> 00:48:47,200 Speaker 2: Yeah all right, well, I mean it's your month, So 878 00:48:49,440 --> 00:48:58,640 Speaker 2: here we go, melanan Ate. I'll hail our new melanin overlords. 879 00:48:59,040 --> 00:49:02,680 Speaker 1: That's right, It's about time. Mike, thank you so much 880 00:49:02,719 --> 00:49:03,240 Speaker 1: for being. 881 00:49:03,040 --> 00:49:05,719 Speaker 2: Here, Thanks for having me Bridget, always a pleasure. 882 00:49:05,600 --> 00:49:07,280 Speaker 1: And thanks so much to all of you for listening. 883 00:49:07,480 --> 00:49:14,560 Speaker 1: Happy Black History Months. If you're looking for ways to 884 00:49:14,560 --> 00:49:17,200 Speaker 1: support the show, check out our March store at teangody 885 00:49:17,239 --> 00:49:20,840 Speaker 1: dot com slash store. Got a story about an interesting 886 00:49:20,880 --> 00:49:22,920 Speaker 1: thing in tech, or just want to say hi, You 887 00:49:22,920 --> 00:49:25,200 Speaker 1: can reach us at Hello at tegody dot com. You 888 00:49:25,239 --> 00:49:28,320 Speaker 1: can also find transcripts for today's episode at tenggody dot com. 889 00:49:28,440 --> 00:49:30,120 Speaker 1: There are No Girls on the Internet was created by 890 00:49:30,120 --> 00:49:33,480 Speaker 1: me Bridget Tod. It's a production of iHeartRadio and Unboss Creative, 891 00:49:34,000 --> 00:49:37,600 Speaker 1: edited by Joey Pat Jonathan Strickland as our executive producer. 892 00:49:37,920 --> 00:49:41,200 Speaker 1: Tari Harrison is our producer and sound engineer. Michael Almada 893 00:49:41,239 --> 00:49:44,239 Speaker 1: is our contributing producer. I'm your host, Bridget Todd. If 894 00:49:44,239 --> 00:49:45,960 Speaker 1: you want to help us grow, rate and review us 895 00:49:45,960 --> 00:49:49,600 Speaker 1: on Apple Podcasts. For more podcasts from iHeartRadio, check out 896 00:49:49,640 --> 00:49:57,080 Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.