1 00:00:02,680 --> 00:00:05,760 Speaker 1: Hi, it's Wescasova. We're taking a break this week, so 2 00:00:05,960 --> 00:00:09,320 Speaker 1: here's one of our favorite episodes you might have missed. 3 00:00:09,760 --> 00:00:16,480 Speaker 1: Thanks so much for listening. We've talked quite a lot 4 00:00:16,520 --> 00:00:20,000 Speaker 1: on this show lately about artificial intelligence because of how 5 00:00:20,079 --> 00:00:23,680 Speaker 1: quickly it's inserted itself into so many parts of our lives, 6 00:00:24,200 --> 00:00:27,520 Speaker 1: and how it's becoming more and more difficult to tell 7 00:00:27,720 --> 00:00:33,040 Speaker 1: what's real and what's fabricated. One place where that's increasingly 8 00:00:33,080 --> 00:00:37,320 Speaker 1: becoming a problem politics, especially now as the twenty twenty 9 00:00:37,320 --> 00:00:40,000 Speaker 1: four US presidential campaign heats up. 10 00:00:40,360 --> 00:00:45,920 Speaker 2: EI. Artificial intelligence is now hitting the US presidential campaign trail. 11 00:00:46,720 --> 00:00:50,960 Speaker 2: Donald Trump has been got in a series of aidep figs. 12 00:00:51,520 --> 00:00:55,600 Speaker 2: This time. Ron DeSantis is also in the spotlight. Reports 13 00:00:55,600 --> 00:00:59,680 Speaker 2: say DeSantis published fake images of Donald Trump and Anthony Fauci. 14 00:01:00,120 --> 00:01:05,400 Speaker 1: Already, candidates and their surrogates and even bad actors overseas 15 00:01:05,600 --> 00:01:07,920 Speaker 1: are reaching for every tool they can to try to 16 00:01:08,000 --> 00:01:12,280 Speaker 1: shape voters' perceptions and ultimately influence the outcome. 17 00:01:12,680 --> 00:01:15,839 Speaker 3: There is definitely an arms race when it comes to AI, 18 00:01:16,120 --> 00:01:19,319 Speaker 3: but there is an element of mutually assured destruction. 19 00:01:19,520 --> 00:01:22,200 Speaker 1: And the government is struggling to figure out how to 20 00:01:22,319 --> 00:01:24,880 Speaker 1: police the use of AI in campaigns. 21 00:01:25,319 --> 00:01:28,240 Speaker 4: What makes the FEC really interesting in this is that 22 00:01:28,280 --> 00:01:31,319 Speaker 4: they are by designing a partisan agency with a mix 23 00:01:31,319 --> 00:01:34,120 Speaker 4: of Republicans and Democrats, and so across the board you 24 00:01:34,160 --> 00:01:43,000 Speaker 4: really see them deadlocking on almost every issue. 25 00:01:44,360 --> 00:01:47,360 Speaker 1: I'm west casofa today on the Big Take, Bloomberg's Emily 26 00:01:47,440 --> 00:01:51,680 Speaker 1: Burnbaum and Laura Davison tell us how AI is transforming 27 00:01:52,040 --> 00:02:00,280 Speaker 1: and distorting our political reality. Emily, it seems like it 28 00:02:00,320 --> 00:02:04,880 Speaker 1: took about five minutes after AI really came forward as 29 00:02:04,880 --> 00:02:08,280 Speaker 1: a thing that was easily accessible for the political world 30 00:02:08,360 --> 00:02:11,240 Speaker 1: to say, hey, we can use this for all kinds 31 00:02:11,280 --> 00:02:14,560 Speaker 1: of reasons, some good and some really not so good. 32 00:02:15,960 --> 00:02:19,399 Speaker 3: Yeah, we're still really in the early stages of this. 33 00:02:19,560 --> 00:02:23,360 Speaker 3: We've yet to see the most dramatic examples of political 34 00:02:23,400 --> 00:02:27,120 Speaker 3: misinformation thanks to generative AI, but there are a couple 35 00:02:27,240 --> 00:02:31,520 Speaker 3: really big and well known examples that have been circulating. 36 00:02:31,760 --> 00:02:35,800 Speaker 3: So some far right politicians in Germany used AI to 37 00:02:36,120 --> 00:02:39,680 Speaker 3: generate images of immigrants that look very angry and violent. 38 00:02:40,400 --> 00:02:44,120 Speaker 3: No one was real, just you know, meant to incite 39 00:02:44,400 --> 00:02:48,519 Speaker 3: sort of nationalist fervor. Here back in the US, we're 40 00:02:48,560 --> 00:02:53,600 Speaker 3: seeing a lot more of that. So presidential candidate Ron DeSantis, 41 00:02:54,040 --> 00:02:58,560 Speaker 3: his campaign arm put out a video that included images 42 00:02:58,760 --> 00:03:03,880 Speaker 3: of Anthony Foul hugging former President Donald Trump and they 43 00:03:03,880 --> 00:03:08,040 Speaker 3: were not real. So Desantans's campaign didn't say that they 44 00:03:08,160 --> 00:03:13,040 Speaker 3: were AI generated, but experts kind of circled and identified 45 00:03:13,080 --> 00:03:16,520 Speaker 3: them as fake, and then within a couple hours there 46 00:03:16,600 --> 00:03:18,960 Speaker 3: was a Twitter note appended to it saying these are 47 00:03:19,000 --> 00:03:22,600 Speaker 3: not real. And the other big example is the Republican 48 00:03:22,680 --> 00:03:27,000 Speaker 3: National Committee put together an AI generated video that purported 49 00:03:27,040 --> 00:03:29,680 Speaker 3: to show, you know, here's the future that Biden wants, 50 00:03:29,720 --> 00:03:33,560 Speaker 3: and it was very dystopian and disturbing. They actually said 51 00:03:33,600 --> 00:03:34,800 Speaker 3: it was AI generated. 52 00:03:37,200 --> 00:03:38,960 Speaker 4: Yeah, this video was really quite disturbing. 53 00:03:38,960 --> 00:03:39,120 Speaker 2: You know. 54 00:03:39,200 --> 00:03:42,320 Speaker 4: It showed images of China attacking Taiwan. It showed the 55 00:03:42,360 --> 00:03:45,120 Speaker 4: streets of San Francisco with troops marching in as martial 56 00:03:45,200 --> 00:03:47,880 Speaker 4: law was taking over, and those looked very very real. 57 00:03:48,240 --> 00:03:50,480 Speaker 4: The one tailtale sign at the end of this video 58 00:03:50,640 --> 00:03:53,280 Speaker 4: that it was fake was it showed Joe Biden just 59 00:03:53,320 --> 00:03:56,400 Speaker 4: slumped over his desk, looking really dejected, and you could 60 00:03:56,400 --> 00:03:58,600 Speaker 4: see that his elbows weren't quite connecting with the edge 61 00:03:58,600 --> 00:04:01,040 Speaker 4: of the desk. So it also had a little disclaimer, 62 00:04:01,200 --> 00:04:04,000 Speaker 4: very very tiny in the corner saying this was AI generated. 63 00:04:04,040 --> 00:04:05,760 Speaker 4: But one of the things that consumers are going to 64 00:04:05,840 --> 00:04:08,440 Speaker 4: have to start doing is be really good about determining 65 00:04:08,480 --> 00:04:11,000 Speaker 4: what video is fake and what is real. And there's 66 00:04:11,040 --> 00:04:14,040 Speaker 4: always with AI, the technology is pretty good. But you know, 67 00:04:14,080 --> 00:04:17,479 Speaker 4: sometimes people have six fingers on the Fauci and Trump 68 00:04:17,560 --> 00:04:20,960 Speaker 4: hugging images. You know, it looked very airbrushed. Trump's hair 69 00:04:21,000 --> 00:04:23,640 Speaker 4: looked very weird in certain angles. So that's kind of 70 00:04:23,640 --> 00:04:26,159 Speaker 4: a telltale sign if you think something looks weird and 71 00:04:26,160 --> 00:04:27,600 Speaker 4: you're like, look at the hands, look at the feet, 72 00:04:27,600 --> 00:04:29,640 Speaker 4: look at the limbs, look at some of these features. 73 00:04:29,640 --> 00:04:32,159 Speaker 4: They look a little bit distorted, and that's usually a 74 00:04:32,200 --> 00:04:33,720 Speaker 4: sign that something is a little off. 75 00:04:34,320 --> 00:04:36,919 Speaker 1: I remember in the last election we started thinking about 76 00:04:36,960 --> 00:04:41,320 Speaker 1: deep face, these videos, these pictures which are indistinguishable from 77 00:04:41,560 --> 00:04:45,000 Speaker 1: what's real, and the technology wasn't quite there yet. And now, 78 00:04:45,040 --> 00:04:47,480 Speaker 1: of course we see every day that it is getting 79 00:04:47,520 --> 00:04:49,719 Speaker 1: much better. And I guess that raises this question that 80 00:04:49,880 --> 00:04:53,120 Speaker 1: as the campaign heats up, we're going to see so 81 00:04:53,360 --> 00:04:55,440 Speaker 1: many of these images that it's going to be really 82 00:04:55,520 --> 00:04:58,960 Speaker 1: hard to tell what's real and what's not correct. 83 00:04:59,000 --> 00:05:03,400 Speaker 4: And there's no regulation that requires any person, any advertiser, 84 00:05:03,440 --> 00:05:07,479 Speaker 4: any candidate to declare something as fake. Google has said 85 00:05:07,760 --> 00:05:09,560 Speaker 4: that in their tools that they are going to have 86 00:05:09,720 --> 00:05:11,960 Speaker 4: not a physical marker on it, but sort of a 87 00:05:12,000 --> 00:05:14,840 Speaker 4: digital marker, So if people go and reverse image search 88 00:05:14,880 --> 00:05:16,720 Speaker 4: it, it will flag it as something that's fake. But that's 89 00:05:16,880 --> 00:05:19,440 Speaker 4: how often do you go and actually reverse image search something. 90 00:05:20,000 --> 00:05:22,240 Speaker 4: Not that often, So it's going to really take a 91 00:05:22,240 --> 00:05:25,560 Speaker 4: lot of discerning, both from kind of the truth squatting 92 00:05:25,680 --> 00:05:28,400 Speaker 4: faction on Twitter as well as from individual consumers to 93 00:05:28,440 --> 00:05:30,240 Speaker 4: know if something's real or fake. And as we know, 94 00:05:30,279 --> 00:05:31,720 Speaker 4: a lot of people don't even know who's running for 95 00:05:31,720 --> 00:05:32,520 Speaker 4: president right now. 96 00:05:32,960 --> 00:05:37,480 Speaker 3: Yeah. Just on the topic of regulation not being in place, 97 00:05:37,800 --> 00:05:41,080 Speaker 3: the Federal Election Commission this month tried to vote on 98 00:05:41,279 --> 00:05:44,719 Speaker 3: a measure that would have enabled them to create rules 99 00:05:45,120 --> 00:05:49,839 Speaker 3: around AI generated political ads. Basically, the FEC would say 100 00:05:49,960 --> 00:05:53,440 Speaker 3: you have to declare when AI has generated your ad. 101 00:05:53,800 --> 00:05:56,760 Speaker 3: But they found themselves deadlocked. So there was a deep 102 00:05:56,800 --> 00:05:59,920 Speaker 3: disagreement among the commissioners, not even just a long partisan 103 00:06:00,080 --> 00:06:03,200 Speaker 3: lines about if they even have the authority to do that. 104 00:06:03,680 --> 00:06:09,279 Speaker 3: So the agencies that we have are currently deadlocked, unsure 105 00:06:09,279 --> 00:06:12,239 Speaker 3: how to proceed, and on Capitol Hill they're only beginning 106 00:06:12,240 --> 00:06:16,920 Speaker 3: to have these conversations. 107 00:06:16,120 --> 00:06:19,960 Speaker 1: Emily, why would the Federal Election Commission have any doubts 108 00:06:20,000 --> 00:06:22,039 Speaker 1: about what to do? Is there anyone in favor of 109 00:06:22,080 --> 00:06:25,320 Speaker 1: allowing fake stuff to be spread all over the place 110 00:06:25,320 --> 00:06:26,479 Speaker 1: without having to be labeled. 111 00:06:27,000 --> 00:06:30,239 Speaker 3: It's less about the actual measure and more about if 112 00:06:30,360 --> 00:06:33,240 Speaker 3: the FEC has the authority to do this. So we 113 00:06:33,279 --> 00:06:36,440 Speaker 3: actually run into this with tech related issues and our 114 00:06:36,480 --> 00:06:39,920 Speaker 3: government all the time. Most of these agencies weren't set 115 00:06:40,000 --> 00:06:44,520 Speaker 3: up to deal with issues like deep fakes, misinformation scams online, 116 00:06:44,640 --> 00:06:47,080 Speaker 3: and so they're having to adapt in real time, and 117 00:06:47,120 --> 00:06:50,880 Speaker 3: the FEC, some of the commissioners at least say that 118 00:06:50,920 --> 00:06:54,120 Speaker 3: they just don't have power over this part of the lot. 119 00:06:54,560 --> 00:06:58,359 Speaker 3: There are a lot of protections for political speech that 120 00:06:58,600 --> 00:07:02,520 Speaker 3: is an important bedrock of communication in America, so that 121 00:07:02,640 --> 00:07:05,040 Speaker 3: makes this issue even more difficult to deal with. 122 00:07:05,560 --> 00:07:08,480 Speaker 4: What makes the FEC really interesting in this is that 123 00:07:08,480 --> 00:07:11,520 Speaker 4: they are by designing a partisan agency with a mix 124 00:07:11,560 --> 00:07:14,360 Speaker 4: of Republicans and Democrats, and so across the board you 125 00:07:14,400 --> 00:07:17,840 Speaker 4: really see them deadlocking on almost every issue. The FEC 126 00:07:17,880 --> 00:07:21,000 Speaker 4: has had very little success doing anything, which just points 127 00:07:21,000 --> 00:07:23,880 Speaker 4: to that the FEC realistically isn't going to be able 128 00:07:23,920 --> 00:07:25,880 Speaker 4: to take this one on based on all the other 129 00:07:26,000 --> 00:07:27,680 Speaker 4: issues facing the agency. 130 00:07:27,800 --> 00:07:31,640 Speaker 1: And what about the candidates themselves. It's easy to put 131 00:07:31,680 --> 00:07:34,840 Speaker 1: something out that's fake about your opponent and maybe it 132 00:07:34,840 --> 00:07:36,760 Speaker 1: gets you all kinds of attention, but then you know 133 00:07:36,800 --> 00:07:38,880 Speaker 1: they're going to be doing it about you too, and 134 00:07:38,960 --> 00:07:42,600 Speaker 1: so there's this kind of AI fakery arms race that 135 00:07:42,640 --> 00:07:45,160 Speaker 1: we seem to be setting up. Is there any recognition 136 00:07:45,200 --> 00:07:46,840 Speaker 1: on the part of the campaigns that this is a 137 00:07:46,880 --> 00:07:48,560 Speaker 1: really bad line to cross? 138 00:07:49,920 --> 00:07:53,080 Speaker 3: So there is definitely an arms race when it comes 139 00:07:53,160 --> 00:07:56,120 Speaker 3: to AI, But there is an element of mutually assured 140 00:07:56,160 --> 00:08:00,200 Speaker 3: destruction where as soon as one campaign really goes out 141 00:08:00,360 --> 00:08:02,840 Speaker 3: put some words in their opponent's mouth that makes them 142 00:08:02,840 --> 00:08:07,600 Speaker 3: look bad, distributes images videos widely, then that sort of 143 00:08:07,800 --> 00:08:10,800 Speaker 3: opens this whole can of worms, and so there's going 144 00:08:10,880 --> 00:08:13,600 Speaker 3: to be tons of these kinds of ads, and actually, 145 00:08:13,600 --> 00:08:17,120 Speaker 3: no one really wants that. So there's some internal reckoning 146 00:08:17,200 --> 00:08:21,440 Speaker 3: that's happening among political consultants and campaigns right now. 147 00:08:21,640 --> 00:08:23,840 Speaker 1: I guess they've been coming from these outside groups that 148 00:08:23,920 --> 00:08:26,280 Speaker 1: support campaigns so that they're able to have a certain 149 00:08:26,320 --> 00:08:30,720 Speaker 1: amount of deniability even though it's very clear which candidate 150 00:08:30,720 --> 00:08:31,400 Speaker 1: they're supporting. 151 00:08:31,840 --> 00:08:35,199 Speaker 4: But it creates for what we'd call like dark money groups. 152 00:08:35,200 --> 00:08:37,520 Speaker 4: So five oh on and C four political advacy groups. 153 00:08:37,800 --> 00:08:39,760 Speaker 4: This is a real wide opening for you know, groups 154 00:08:39,760 --> 00:08:42,600 Speaker 4: that aren't necessarily representing a specific person but an interest 155 00:08:42,679 --> 00:08:45,840 Speaker 4: area or a larger party. The dark money groups, they 156 00:08:45,840 --> 00:08:47,640 Speaker 4: can go out and take all these images and they're 157 00:08:47,640 --> 00:08:50,480 Speaker 4: super cheap and easy to produce, so they can react 158 00:08:50,520 --> 00:08:52,720 Speaker 4: in real time. If something is said in a debate 159 00:08:52,760 --> 00:08:54,520 Speaker 4: about a candidate they don't like, they can have an 160 00:08:54,520 --> 00:08:56,960 Speaker 4: image or a video out there online by the end 161 00:08:57,000 --> 00:08:58,840 Speaker 4: of the debate. So that's what we're going to start 162 00:08:58,880 --> 00:09:00,960 Speaker 4: to see this really rapid response. You know, what we 163 00:09:01,040 --> 00:09:03,040 Speaker 4: used to just see with with tweets or emails can 164 00:09:03,120 --> 00:09:05,640 Speaker 4: now be done with images and videos. 165 00:09:08,200 --> 00:09:10,920 Speaker 1: Laurie, we've been talking about how we're seeing this happening 166 00:09:10,960 --> 00:09:14,719 Speaker 1: at the national level, at the presidential level, but it's 167 00:09:14,760 --> 00:09:18,440 Speaker 1: also filtering down throughout politics in the US. 168 00:09:19,080 --> 00:09:19,320 Speaker 2: Yeah. 169 00:09:19,360 --> 00:09:21,679 Speaker 4: So AI is really cheap, and so that makes it 170 00:09:21,720 --> 00:09:24,680 Speaker 4: a really great tool for these smaller campaigns that don't 171 00:09:24,679 --> 00:09:26,040 Speaker 4: have a lot of money, that don't have a lot 172 00:09:26,040 --> 00:09:29,360 Speaker 4: of staff. So there's been in some mayoral elections recently, 173 00:09:29,559 --> 00:09:32,800 Speaker 4: AI used both by campaigns to sort of support themselves 174 00:09:32,920 --> 00:09:35,560 Speaker 4: as well as attack ads. There was a really great 175 00:09:35,559 --> 00:09:38,200 Speaker 4: example in Shreveport, Louisiana, where there was an attack ad 176 00:09:38,280 --> 00:09:41,920 Speaker 4: against the incumbent mayor, Adrian Perkins, and it showed this 177 00:09:42,040 --> 00:09:44,280 Speaker 4: scene where it was you know, it was his face 178 00:09:44,400 --> 00:09:46,840 Speaker 4: on a body and his voice was talking, his mouth 179 00:09:46,960 --> 00:09:48,480 Speaker 4: was moving. It wasn't him, it was an actor, and 180 00:09:48,520 --> 00:09:50,560 Speaker 4: then they had used AI to create it to make 181 00:09:50,600 --> 00:09:52,800 Speaker 4: it look like him. He ended up losing this race, 182 00:09:52,840 --> 00:09:55,280 Speaker 4: not necessarily because of this ad, but just a really 183 00:09:55,280 --> 00:09:57,760 Speaker 4: great example of how, you know, someone a developer in 184 00:09:57,800 --> 00:10:00,440 Speaker 4: town who didn't particularly care for the mayor come in 185 00:10:00,480 --> 00:10:03,079 Speaker 4: and create this ad really quickly, easily and cheaply, and 186 00:10:03,640 --> 00:10:05,520 Speaker 4: get it on the airwaves and really make a splash 187 00:10:05,520 --> 00:10:08,320 Speaker 4: with it. In Toronto we also saw the same thing 188 00:10:08,360 --> 00:10:12,360 Speaker 4: in their mayoral race, where candidate Anthony Fury used artificial 189 00:10:12,400 --> 00:10:15,080 Speaker 4: intelligence images in sort of his campaign materials, you know, 190 00:10:15,160 --> 00:10:18,440 Speaker 4: showing packages of crack pipe kits that were sponsored by 191 00:10:18,440 --> 00:10:20,360 Speaker 4: the City of Toronto, you know, using this to criticize 192 00:10:20,440 --> 00:10:21,959 Speaker 4: policies of his opponents. 193 00:10:22,320 --> 00:10:26,160 Speaker 1: Emily, you talked about how difficult it is for the 194 00:10:26,200 --> 00:10:28,400 Speaker 1: government to try and figure out how and whether they 195 00:10:28,440 --> 00:10:32,600 Speaker 1: can regulate this, is there anything illegal about a candidate 196 00:10:33,320 --> 00:10:38,080 Speaker 1: making another candidate appear to be doing something completely false? 197 00:10:38,840 --> 00:10:43,000 Speaker 3: One use of AI that could be illegal, and we 198 00:10:43,120 --> 00:10:46,200 Speaker 3: can remember this is still an untested area of the 199 00:10:46,280 --> 00:10:49,199 Speaker 3: law in many ways, so we're not positive. But one 200 00:10:49,520 --> 00:10:52,320 Speaker 3: thing that could be illegal is if you use a 201 00:10:52,360 --> 00:10:58,200 Speaker 3: candidate's likeness to get money that the candidate doesn't actually receive. 202 00:10:58,720 --> 00:11:01,960 Speaker 3: So that is one thing that the FBC was discussing 203 00:11:02,240 --> 00:11:05,480 Speaker 3: that seems like a clear violation of our campaign laws 204 00:11:05,559 --> 00:11:08,600 Speaker 3: when you are not allowed to impersonate anyone else and 205 00:11:08,640 --> 00:11:11,680 Speaker 3: not allowed to lie about where money is going. There 206 00:11:11,720 --> 00:11:15,240 Speaker 3: are also laws cropping up in the states across the 207 00:11:15,280 --> 00:11:20,000 Speaker 3: country that ban certain kinds of quote unquote deep fakes. 208 00:11:20,040 --> 00:11:23,800 Speaker 3: So a lot of those laws pertained to pornography. So 209 00:11:23,840 --> 00:11:27,080 Speaker 3: there have been some reports that show over ninety percent 210 00:11:27,320 --> 00:11:30,760 Speaker 3: of the deep fakes on the Internet are pornographic images, 211 00:11:31,120 --> 00:11:36,240 Speaker 3: mostly featuring women, and so a bunch of states have 212 00:11:37,160 --> 00:11:39,840 Speaker 3: rushed in to fill this gap in the law, basically 213 00:11:39,920 --> 00:11:43,920 Speaker 3: just improving victims' rights, so people who were featured in 214 00:11:43,960 --> 00:11:48,080 Speaker 3: pornographic images on the Internet can sue and have the 215 00:11:48,200 --> 00:11:51,000 Speaker 3: right to stop the proliferation of those But there are 216 00:11:51,080 --> 00:11:55,280 Speaker 3: far fewer rules about election related deep fakes. There are 217 00:11:55,360 --> 00:12:01,199 Speaker 3: some they probably won't withstand legal challenges, So it's still 218 00:12:01,280 --> 00:12:04,600 Speaker 3: kind of complicated whenever you try to touch political speech, 219 00:12:04,960 --> 00:12:07,680 Speaker 3: but there are clearer violations. 220 00:12:08,400 --> 00:12:11,920 Speaker 1: After the break. How AI is changing the way campaigns 221 00:12:12,160 --> 00:12:13,559 Speaker 1: get their message. 222 00:12:13,200 --> 00:12:24,840 Speaker 5: Out, Laurie, you hinted a little bit before about how 223 00:12:24,880 --> 00:12:28,679 Speaker 5: campaigns are using AI for non nefarious reasons, that it's 224 00:12:28,720 --> 00:12:31,520 Speaker 5: just like become a useful tool being used by campaigns 225 00:12:31,559 --> 00:12:33,839 Speaker 5: the way it's used by a lot of different industries. 226 00:12:34,240 --> 00:12:36,280 Speaker 4: Yeah, so there's a bunch of different things that campaigns 227 00:12:36,280 --> 00:12:38,319 Speaker 4: are using and some that people would kind of clearly 228 00:12:38,360 --> 00:12:41,680 Speaker 4: put into a non nefarious camp. So things like helping 229 00:12:41,960 --> 00:12:44,680 Speaker 4: use AI to analyze voter roles of people that they 230 00:12:44,720 --> 00:12:48,400 Speaker 4: should be targeting, organizing some of their data, figuring out 231 00:12:48,400 --> 00:12:52,040 Speaker 4: what kind of messages would resonate with which voters. Another 232 00:12:52,120 --> 00:12:54,720 Speaker 4: thing that they're doing is using tools like chat GBT, 233 00:12:54,880 --> 00:12:57,640 Speaker 4: not necessarily chat GPT because that tool has some restrictions 234 00:12:57,679 --> 00:13:00,360 Speaker 4: on political speech, but to write the first draft of 235 00:13:00,440 --> 00:13:04,160 Speaker 4: press releases of fundraising emails. And they found a really 236 00:13:04,200 --> 00:13:08,120 Speaker 4: great cost savings instead of having five people who are 237 00:13:08,200 --> 00:13:11,240 Speaker 4: copywriters on a campaign working on all these messages that 238 00:13:11,280 --> 00:13:14,040 Speaker 4: you'd have one person that is just telling the tool, okay, 239 00:13:14,080 --> 00:13:18,000 Speaker 4: write a fundraising email with a focus on democracy, one 240 00:13:18,040 --> 00:13:19,760 Speaker 4: with a focus on the environment, right, one with a 241 00:13:19,800 --> 00:13:24,040 Speaker 4: focus on defeating Republicans, for example, And then they can 242 00:13:24,120 --> 00:13:26,200 Speaker 4: get something on the page and then just edit it 243 00:13:26,240 --> 00:13:29,760 Speaker 4: and it goes much much quicker. The one campaign consultant 244 00:13:29,800 --> 00:13:32,080 Speaker 4: described this as solving the blank page problem. Anyone who's 245 00:13:32,080 --> 00:13:34,439 Speaker 4: ever written anything can probably sympathize with that if it's 246 00:13:34,480 --> 00:13:36,120 Speaker 4: much easier to get something on the paper and then 247 00:13:36,200 --> 00:13:39,160 Speaker 4: edit it versus just starting from scratch. So they're finding 248 00:13:39,160 --> 00:13:41,200 Speaker 4: really big cost savings in that of they're able to 249 00:13:41,679 --> 00:13:44,640 Speaker 4: both raise more money because they're more effectively targeting people 250 00:13:44,679 --> 00:13:47,120 Speaker 4: as well as cut down on staff cost for that 251 00:13:47,200 --> 00:13:50,720 Speaker 4: particular function of writing. That's been really widespread, and one 252 00:13:50,840 --> 00:13:54,000 Speaker 4: consultant estimated about fifty percent of campaigns are doing this, 253 00:13:54,080 --> 00:13:56,480 Speaker 4: but expect that number to be somewhere closer to one 254 00:13:56,559 --> 00:13:58,320 Speaker 4: hundred by the end of the twenty twenty four cycle. 255 00:13:58,760 --> 00:14:02,240 Speaker 3: But Congress just said new rules on how staffers can 256 00:14:02,360 --> 00:14:05,920 Speaker 3: use chat GPT, and the main concern is that a 257 00:14:05,960 --> 00:14:10,440 Speaker 3: lot of what you put into chat GPT can become 258 00:14:10,720 --> 00:14:13,360 Speaker 3: available in other ways. You know, there are a lot 259 00:14:13,400 --> 00:14:17,200 Speaker 3: of privacy concerns around chat GPT, So the new rules say, 260 00:14:17,679 --> 00:14:20,080 Speaker 3: first of all, you have to use the paid for 261 00:14:20,360 --> 00:14:24,400 Speaker 3: version of chat GPT, which is better protective of privacy. 262 00:14:24,800 --> 00:14:28,520 Speaker 3: Second of all, you can't put confidential information into chat GPT, 263 00:14:28,920 --> 00:14:32,120 Speaker 3: which congressional staffers are often working with. So they're kind 264 00:14:32,200 --> 00:14:34,760 Speaker 3: of like making it up as they go along. But 265 00:14:35,000 --> 00:14:38,320 Speaker 3: congressional staffers work all day and night. They are never 266 00:14:38,400 --> 00:14:40,400 Speaker 3: off the clock, and so anything that cuts back on 267 00:14:40,640 --> 00:14:41,960 Speaker 3: time is really valuable. 268 00:14:45,040 --> 00:14:49,040 Speaker 1: Open AI. The makers of chat GPT, they are kind 269 00:14:49,040 --> 00:14:52,720 Speaker 1: of alarmed at the way campaigns could be using their 270 00:14:52,760 --> 00:14:54,920 Speaker 1: technology and or trying to figure out a way to 271 00:14:55,000 --> 00:14:55,480 Speaker 1: limit it. 272 00:14:55,960 --> 00:14:58,280 Speaker 4: If you put in really anything related to politics and 273 00:14:58,320 --> 00:15:01,680 Speaker 4: chat GEPT, they're sort of the standard disclaimer language coming 274 00:15:01,760 --> 00:15:03,320 Speaker 4: up of saying, you know, look, I don't have an 275 00:15:03,320 --> 00:15:06,040 Speaker 4: opinion on this. There's nuance to this, and so chat 276 00:15:06,080 --> 00:15:09,800 Speaker 4: gibt is being very careful with anything related to politics. 277 00:15:09,840 --> 00:15:11,560 Speaker 4: But if you push it a little bit, like if 278 00:15:11,560 --> 00:15:14,160 Speaker 4: you type who are the wokest Democrats, it will name 279 00:15:14,360 --> 00:15:19,480 Speaker 4: Alexandria Kazi Kortez, Rashida talib Aana Presley Bilhan, omar interesting 280 00:15:19,480 --> 00:15:20,960 Speaker 4: that all like the top four or five names that 281 00:15:21,000 --> 00:15:23,560 Speaker 4: came up were all women. Sort of treading this careful line, 282 00:15:23,600 --> 00:15:26,360 Speaker 4: it's not completely staying out of the political arena. So 283 00:15:26,480 --> 00:15:29,840 Speaker 4: for a lot of political things, chat gibt has basically 284 00:15:29,920 --> 00:15:33,800 Speaker 4: stopped cataloging language from after September twenty twenty one, So 285 00:15:33,880 --> 00:15:36,600 Speaker 4: when you ask chat gibt a political question will be like, 286 00:15:36,600 --> 00:15:39,040 Speaker 4: here is my information as of September twenty twenty one. 287 00:15:39,280 --> 00:15:40,920 Speaker 4: You may need to do more research if you want 288 00:15:40,920 --> 00:15:43,160 Speaker 4: something more recent. So it's kind of just clamping down 289 00:15:43,200 --> 00:15:45,440 Speaker 4: and not feeding the beast, so to speak, with more 290 00:15:45,440 --> 00:15:48,160 Speaker 4: information to make it sort of an obsolete tool when 291 00:15:48,160 --> 00:15:49,280 Speaker 4: it comes to political speech. 292 00:15:49,680 --> 00:15:54,000 Speaker 3: They're also putting together something called classifiers, so open ai 293 00:15:54,240 --> 00:15:57,640 Speaker 3: is able to see what people put into chat gpt, 294 00:15:58,160 --> 00:16:00,880 Speaker 3: and so they're working hard now to figure out what 295 00:16:00,960 --> 00:16:05,840 Speaker 3: are the classifiers or identifying freezes that people use, where 296 00:16:05,920 --> 00:16:07,760 Speaker 3: when they're using those, you know that it's for a 297 00:16:07,800 --> 00:16:11,080 Speaker 3: political purpose, And then if that's happening, how can you 298 00:16:11,320 --> 00:16:15,040 Speaker 3: control what chat gpt says in response, they really don't 299 00:16:15,040 --> 00:16:18,240 Speaker 3: want their product to be used to create lies and 300 00:16:18,280 --> 00:16:19,840 Speaker 3: misinformation that they're blamed for. 301 00:16:20,680 --> 00:16:22,640 Speaker 4: To sort of add those. Chat GIBT is sort of 302 00:16:22,680 --> 00:16:26,200 Speaker 4: the big juggernaut and the industry of generative text AI, 303 00:16:26,480 --> 00:16:28,800 Speaker 4: but there's so many other tools that are being developed 304 00:16:28,840 --> 00:16:32,240 Speaker 4: and tools that are being developed specifically for political uses. 305 00:16:32,280 --> 00:16:35,000 Speaker 4: So usually these are paid tools that campaigns would subscribe to. 306 00:16:35,360 --> 00:16:37,640 Speaker 4: But just because chat GBT is sort of clamping down 307 00:16:37,640 --> 00:16:40,120 Speaker 4: on this, it doesn't mean that other actors can go 308 00:16:40,160 --> 00:16:43,080 Speaker 4: buy these tools that are specifically designed for this to 309 00:16:43,720 --> 00:16:45,480 Speaker 4: create some political misinformation. 310 00:16:46,320 --> 00:16:50,520 Speaker 1: Laura earlier Emily painted this picture of mutual assured destruction 311 00:16:50,720 --> 00:16:53,160 Speaker 1: where if one candidate attacks the other and the other 312 00:16:53,280 --> 00:16:56,040 Speaker 1: attacks back, then everybody gets hurt. And in you're reporting, 313 00:16:56,080 --> 00:17:00,000 Speaker 1: you found that both Democrats and Republicans are using this technology, 314 00:17:00,080 --> 00:17:01,400 Speaker 1: but in slightly different ways. 315 00:17:02,520 --> 00:17:04,800 Speaker 4: So what we've seen in terms of the visual and 316 00:17:04,920 --> 00:17:07,800 Speaker 4: video side as Republicans using this more. We saw big 317 00:17:07,840 --> 00:17:10,600 Speaker 4: first video ad was the RNC attacking Biden. We saw 318 00:17:10,680 --> 00:17:14,040 Speaker 4: DeSantis attacking Trump with this. But both parties are definitely 319 00:17:14,119 --> 00:17:16,679 Speaker 4: using it, and probably Democrats are using it more. When 320 00:17:16,720 --> 00:17:19,239 Speaker 4: it comes to the text and generative AI, they have 321 00:17:19,400 --> 00:17:22,840 Speaker 4: firms that are using the text side a lot more. 322 00:17:23,040 --> 00:17:26,000 Speaker 4: That's really widespread among campaigns. This is a really hard 323 00:17:26,000 --> 00:17:28,560 Speaker 4: thing that's hard to track you know exactly how widespread 324 00:17:28,560 --> 00:17:32,119 Speaker 4: it is within either party. Democrats are definitely more cautious 325 00:17:32,320 --> 00:17:35,399 Speaker 4: on using the visual and image side. They kind of 326 00:17:35,440 --> 00:17:37,800 Speaker 4: like to say, look, we're being responsible here, We're not 327 00:17:37,840 --> 00:17:42,320 Speaker 4: going to create misinformation or deep fakes. Republicans, particularly because 328 00:17:42,320 --> 00:17:44,640 Speaker 4: they're going into a contested primary, so they have more 329 00:17:44,680 --> 00:17:46,600 Speaker 4: going on right now, but they're a little bit more 330 00:17:46,600 --> 00:17:49,040 Speaker 4: willing to try out this tool and see how it works, 331 00:17:49,040 --> 00:17:51,600 Speaker 4: while Democrats are a little bit hanging back on the sidelines. 332 00:17:52,080 --> 00:17:55,080 Speaker 3: I spoke to a spokesperson for the Democratic National Committee 333 00:17:55,359 --> 00:18:00,800 Speaker 3: who said Democrats are wary of misinformation. There's a history 334 00:18:00,840 --> 00:18:04,560 Speaker 3: there that goes back to the twenty sixteen election. There's 335 00:18:04,800 --> 00:18:09,480 Speaker 3: a whole team dedicated to stopping misinformation at the DNC, 336 00:18:10,080 --> 00:18:13,000 Speaker 3: So that makes them extra cautious as they begin to 337 00:18:13,080 --> 00:18:16,800 Speaker 3: implement AI slowly into things like fundraising. 338 00:18:19,080 --> 00:18:22,120 Speaker 1: Emily, that's an interesting point that the Democrats are saying 339 00:18:22,119 --> 00:18:23,959 Speaker 1: that they're going to really need to be monitoring this. 340 00:18:24,040 --> 00:18:25,560 Speaker 1: Do you think that we're going to have now this 341 00:18:25,600 --> 00:18:30,560 Speaker 1: whole campaign industry where groups are doing AI checks similar 342 00:18:30,600 --> 00:18:32,200 Speaker 1: to the way we have fact checkers. 343 00:18:32,920 --> 00:18:38,520 Speaker 3: Yeah, there is this anxiety and wariness among political consultants 344 00:18:38,720 --> 00:18:42,520 Speaker 3: about how AI is going to be deployed. So the 345 00:18:42,680 --> 00:18:48,040 Speaker 3: trade Association for Political Consultants, which exists, shockingly came out 346 00:18:48,080 --> 00:18:51,280 Speaker 3: with this statement saying, we are against the use of 347 00:18:51,320 --> 00:18:54,800 Speaker 3: deep fakes in political campaigns and we really shouldn't be 348 00:18:54,800 --> 00:18:58,640 Speaker 3: messing too much with this technology. It's quite dangerous. So 349 00:18:58,840 --> 00:19:02,800 Speaker 3: I do think that incre there is desire to call 350 00:19:02,880 --> 00:19:07,359 Speaker 3: out misinformation, to call out when generative AI is being used. 351 00:19:07,680 --> 00:19:10,560 Speaker 3: I also think that a lot of technology companies are 352 00:19:10,600 --> 00:19:13,160 Speaker 3: about to make a lot of money off of basically 353 00:19:13,240 --> 00:19:16,640 Speaker 3: tools that allow you to identify what's real. Those are 354 00:19:16,720 --> 00:19:20,760 Speaker 3: going to be commonly used, at least among political campaigns, 355 00:19:20,760 --> 00:19:22,200 Speaker 3: if not the general population. 356 00:19:23,280 --> 00:19:26,360 Speaker 1: When we come back, what can we do to avoid 357 00:19:26,400 --> 00:19:39,520 Speaker 1: getting duped? So what are we supposed to do about this? 358 00:19:39,840 --> 00:19:44,720 Speaker 1: Just as citizens watching a campaign trying to make good decisions, 359 00:19:44,920 --> 00:19:49,520 Speaker 1: trying to figure out what's real and what's not. I mean, Laura, 360 00:19:49,640 --> 00:19:52,760 Speaker 1: you kind of jokingly at the beginning said, oh, look 361 00:19:52,800 --> 00:19:55,760 Speaker 1: at the handslet's eve it as six fingers. But this 362 00:19:55,840 --> 00:19:58,720 Speaker 1: is a pretty serious thing. How are we going to 363 00:19:58,760 --> 00:20:01,080 Speaker 1: be able to discern what's real and what's not. 364 00:20:02,240 --> 00:20:04,240 Speaker 4: This is probably when tech companies are going to have 365 00:20:04,280 --> 00:20:07,160 Speaker 4: to start stepping in. We've seen Twitter and Facebook come 366 00:20:07,200 --> 00:20:11,480 Speaker 4: in with labels on misinformation regarding vaccines or other things. 367 00:20:11,600 --> 00:20:14,680 Speaker 4: Regarding the twenty twenty election. We're probably going to see 368 00:20:14,680 --> 00:20:16,199 Speaker 4: those start to come in. I don't know that the 369 00:20:16,240 --> 00:20:19,240 Speaker 4: tech companies have They don't have fully fledged policies yet 370 00:20:19,280 --> 00:20:21,200 Speaker 4: on how to do that, and part of it is 371 00:20:21,200 --> 00:20:24,280 Speaker 4: that it is really difficult to check. We see companies 372 00:20:24,400 --> 00:20:26,560 Speaker 4: like Google saying, Okay, we're going to put these markers 373 00:20:26,800 --> 00:20:29,640 Speaker 4: in these images. That's probably the next step that we're 374 00:20:29,640 --> 00:20:31,920 Speaker 4: going to see of each company, because they want to 375 00:20:31,960 --> 00:20:34,120 Speaker 4: be seen as responsible and because they don't want an 376 00:20:34,160 --> 00:20:36,359 Speaker 4: image created on their platform to be the thing that 377 00:20:36,560 --> 00:20:39,160 Speaker 4: blows up an election, for example. You're going to see 378 00:20:39,280 --> 00:20:42,120 Speaker 4: pressure in the industry to do more, but it's right 379 00:20:42,160 --> 00:20:44,800 Speaker 4: now it's really incumbent upon the viewer. So it is 380 00:20:44,880 --> 00:20:46,359 Speaker 4: kind of looking at the image, look to see if 381 00:20:46,359 --> 00:20:48,840 Speaker 4: there's a little tag in the corner that mentions it's 382 00:20:48,880 --> 00:20:51,240 Speaker 4: AI generated, or a screen at the very end of 383 00:20:51,280 --> 00:20:53,440 Speaker 4: an ad that says this was created by AI. 384 00:20:53,840 --> 00:20:56,760 Speaker 1: You know, in previous campaigns, we've seen Facebook, We've seen 385 00:20:56,800 --> 00:21:00,199 Speaker 1: Twitter make big public announcements they've hired all of these 386 00:21:00,240 --> 00:21:02,119 Speaker 1: people who are going to be monitoring, they're going to 387 00:21:02,119 --> 00:21:04,159 Speaker 1: try to crack down on fakery, and yet they just 388 00:21:04,359 --> 00:21:06,959 Speaker 1: could not possibly do it. In the case of Twitter, 389 00:21:07,080 --> 00:21:10,480 Speaker 1: we're seeing all kinds of fakery all over the platform, 390 00:21:10,600 --> 00:21:14,480 Speaker 1: especially since Elon Musk took over the company and kind 391 00:21:14,480 --> 00:21:17,600 Speaker 1: of opened it up in a way that previously people 392 00:21:17,600 --> 00:21:18,760 Speaker 1: couldn't post stuff on it. 393 00:21:19,600 --> 00:21:23,160 Speaker 3: Yes, but we're also seeing tools like Twitter's community notes, 394 00:21:23,200 --> 00:21:27,479 Speaker 3: which allows everyday users to weigh in on whether something 395 00:21:27,600 --> 00:21:31,879 Speaker 3: is true or false, that was already under construction before 396 00:21:31,920 --> 00:21:35,000 Speaker 3: Elon Musk came in. It's actually proven to be a 397 00:21:35,040 --> 00:21:40,760 Speaker 3: really effective obstacle for the spread of generative AI misinformation. 398 00:21:40,960 --> 00:21:43,200 Speaker 3: So if we think about the ad Byron de Sampas's 399 00:21:43,200 --> 00:21:46,440 Speaker 3: campaign within hours that had a Twitter note on it 400 00:21:46,680 --> 00:21:49,840 Speaker 3: saying tech experts have looked at this and these images 401 00:21:49,880 --> 00:21:53,040 Speaker 3: are not real. So if you think about what you 402 00:21:53,119 --> 00:21:56,439 Speaker 3: can do as an individual, it's a mitzvah to just 403 00:21:56,720 --> 00:22:02,040 Speaker 3: get out the word that something is fake, that maybe 404 00:22:02,359 --> 00:22:08,360 Speaker 3: tech experts are weighing in and just spreading that information together. 405 00:22:08,840 --> 00:22:10,960 Speaker 3: People create media literacy that way. 406 00:22:11,640 --> 00:22:15,880 Speaker 4: And the thing too is ethicists are less concerned about 407 00:22:15,880 --> 00:22:18,280 Speaker 4: people like Biden and Donald Trump that have swarms of 408 00:22:18,320 --> 00:22:21,000 Speaker 4: press following everywhere they go and everything they do. But 409 00:22:21,080 --> 00:22:24,440 Speaker 4: on the local level, where local news isn't as strong, 410 00:22:24,480 --> 00:22:27,399 Speaker 4: where there are people and candidates and politicians who aren't 411 00:22:27,400 --> 00:22:31,080 Speaker 4: necessarily followed as closely, it's way easier to spread misinformation 412 00:22:31,160 --> 00:22:33,720 Speaker 4: in these areas where there is more of a news vacuum. 413 00:22:34,080 --> 00:22:36,560 Speaker 1: Emily Laura, thanks so much for coming on the show. 414 00:22:36,920 --> 00:22:39,240 Speaker 3: Thanks so much for having us, Thank. 415 00:22:39,080 --> 00:22:42,160 Speaker 1: You, thanks for listening to us here at The Big Take. 416 00:22:42,320 --> 00:22:45,680 Speaker 1: It's a daily podcast from Bloomberg and iHeartRadio. For more 417 00:22:45,680 --> 00:22:49,919 Speaker 1: shows from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or 418 00:22:49,960 --> 00:22:52,560 Speaker 1: wherever you listen, and we'd love to hear from you. 419 00:22:52,880 --> 00:22:56,040 Speaker 1: Email us questions or comments to Big Take at Bloomberg 420 00:22:56,080 --> 00:22:59,399 Speaker 1: dot net. The supervising producer of The Big Take is 421 00:22:59,480 --> 00:23:04,240 Speaker 1: Vicky verg Our senior producer is Catherine Fink. Federica Romanello 422 00:23:04,359 --> 00:23:09,080 Speaker 1: is our producer. Our associate producer is Zeneb Sidiki. Rafael 423 00:23:09,240 --> 00:23:12,800 Speaker 1: mcili is our engineer. Our original music was composed by 424 00:23:12,880 --> 00:23:16,240 Speaker 1: Leo Sidrin. I'm west Kosova. We'll be back tomorrow with 425 00:23:16,359 --> 00:23:17,320 Speaker 1: another Big Take.