1 00:00:03,200 --> 00:00:08,000 Speaker 1: This is Bloomberg Law with June Brusso from Bloomberg Radio, 2 00:00:10,160 --> 00:00:13,840 Speaker 1: fresh on victories in other legal cases. Donald Trump is 3 00:00:13,920 --> 00:00:17,200 Speaker 1: now asking a New York appeals court to overturn the 4 00:00:17,280 --> 00:00:21,440 Speaker 1: nearly five hundred million dollars civil fraud judgment against him 5 00:00:21,800 --> 00:00:25,120 Speaker 1: after a trial. Judge Arthur and Goren found in February 6 00:00:25,480 --> 00:00:29,520 Speaker 1: that Trump, his company, and top executives, including his sons, 7 00:00:29,880 --> 00:00:33,800 Speaker 1: schemed for years to inflate his wealth on financial statements 8 00:00:34,159 --> 00:00:37,559 Speaker 1: used to secure loans and make deals. In the appeal, 9 00:00:37,760 --> 00:00:42,560 Speaker 1: Trump's lawyers called the judge's decisions legally bereft and untethered 10 00:00:42,560 --> 00:00:47,120 Speaker 1: to the law or to commercial reality, raising several arguments, 11 00:00:47,520 --> 00:00:50,960 Speaker 1: many that echoed the gripes that Trump complained about over 12 00:00:51,000 --> 00:00:52,400 Speaker 1: and over during the trial. 13 00:00:52,960 --> 00:00:55,560 Speaker 2: There were no victims because the banks made a lot 14 00:00:55,600 --> 00:00:58,160 Speaker 2: of money. They made one hundred million dollars and by 15 00:00:58,200 --> 00:01:03,000 Speaker 2: the way, I paid three hundred million dollars in taxes. 16 00:01:03,360 --> 00:01:06,280 Speaker 1: Joining me is Bloomberg Legal reporter Eric Larson, who has 17 00:01:06,319 --> 00:01:09,920 Speaker 1: been covering the New York Trump trials. Eric, before we 18 00:01:10,000 --> 00:01:12,520 Speaker 1: talk about the appeal, tell us about the judge's decision. 19 00:01:13,480 --> 00:01:17,760 Speaker 3: Sure, so this is now several months later after Trump 20 00:01:18,000 --> 00:01:21,840 Speaker 3: was found libel for fraud and the civil fraud case 21 00:01:21,880 --> 00:01:24,000 Speaker 3: brought against him by the New York Attorney General. Of course, 22 00:01:24,040 --> 00:01:27,200 Speaker 3: this stems from his asset valuations of all of his 23 00:01:27,360 --> 00:01:30,880 Speaker 3: various assets that the State of New York alleged had 24 00:01:30,880 --> 00:01:35,319 Speaker 3: been wildly inflated in order to get Trump better terms 25 00:01:35,360 --> 00:01:39,160 Speaker 3: on loans from banks for over a decade. And so 26 00:01:39,319 --> 00:01:41,839 Speaker 3: the state won this trial. It was a non jury trial, 27 00:01:42,360 --> 00:01:45,560 Speaker 3: and in that time, of course, the former president had 28 00:01:45,680 --> 00:01:49,480 Speaker 3: vowed to appeal, and this was his appeal brief filed 29 00:01:49,520 --> 00:01:53,200 Speaker 3: with an intermediate appeals court, which really spelled out his 30 00:01:53,440 --> 00:01:56,760 Speaker 3: case for trying to overturn the verdict and the five 31 00:01:56,840 --> 00:02:00,760 Speaker 3: hundred and fifty four million dollar penalty last him. 32 00:02:00,960 --> 00:02:04,559 Speaker 1: He raises a lot of arguments here, one being that 33 00:02:04,640 --> 00:02:07,640 Speaker 1: there were no victims and no losses right. 34 00:02:07,720 --> 00:02:10,720 Speaker 3: That is one of the top arguments that Trump makes 35 00:02:10,800 --> 00:02:14,720 Speaker 3: in this filing. He argues that the New York Attorney General, 36 00:02:14,800 --> 00:02:17,680 Speaker 3: Letitia James, didn't have any right to bring this case 37 00:02:17,760 --> 00:02:20,600 Speaker 3: under the New York executive law, in part because there 38 00:02:20,600 --> 00:02:23,240 Speaker 3: were no victims. This is a consumer fraud type case. 39 00:02:23,280 --> 00:02:25,240 Speaker 3: They're saying, where are the victims? Where are the consumers 40 00:02:25,280 --> 00:02:28,600 Speaker 3: who are hurt by this. Of course, we will get 41 00:02:28,600 --> 00:02:31,840 Speaker 3: a full response from the Attorney General to this argument 42 00:02:31,880 --> 00:02:35,080 Speaker 3: and others in the brief, but she has previously responded 43 00:02:35,120 --> 00:02:36,799 Speaker 3: to this, and I should note that all of these 44 00:02:36,880 --> 00:02:38,919 Speaker 3: arguments in this brief have sort of been made before 45 00:02:39,040 --> 00:02:42,520 Speaker 3: by Trump. But the Attorney General says that she had 46 00:02:42,560 --> 00:02:45,320 Speaker 3: a right to sue because it's up to the state 47 00:02:45,360 --> 00:02:48,880 Speaker 3: to make sure that documents used in financial transactions, like 48 00:02:48,919 --> 00:02:51,640 Speaker 3: the loan documents at issue in this case, have to 49 00:02:51,680 --> 00:02:56,000 Speaker 3: be accurate and they can't knowingly contain false information. So 50 00:02:56,560 --> 00:02:58,520 Speaker 3: while the banks didn't come out and say, hey, we 51 00:02:58,520 --> 00:03:02,400 Speaker 3: were defrauded here, the idea for Leticia James is is 52 00:03:02,440 --> 00:03:06,760 Speaker 3: that you have to prevent future violations by other individuals 53 00:03:06,800 --> 00:03:10,040 Speaker 3: and companies by showing that they do enforce the law 54 00:03:10,080 --> 00:03:13,160 Speaker 3: here when there's obvious violations according to the state. 55 00:03:13,919 --> 00:03:18,160 Speaker 1: And we have heard this point since the very beginning 56 00:03:18,200 --> 00:03:22,000 Speaker 1: that the Attorney General is politically motivated. 57 00:03:22,280 --> 00:03:25,680 Speaker 3: Right, So that's another one of the arguments spelled out 58 00:03:25,680 --> 00:03:28,680 Speaker 3: in this brief. They say that the only reason that 59 00:03:28,760 --> 00:03:31,560 Speaker 3: Leticia James brought this case is because she campaigned as 60 00:03:31,600 --> 00:03:34,320 Speaker 3: a Democrat on going after Trump, and that she had 61 00:03:34,320 --> 00:03:37,080 Speaker 3: promised to do that, And in a sense, Trump is 62 00:03:37,160 --> 00:03:40,160 Speaker 3: arguing here that this entire investigation and the lawsuit was 63 00:03:40,160 --> 00:03:42,240 Speaker 3: sort of pretextual, that she had to come up with 64 00:03:42,280 --> 00:03:45,400 Speaker 3: something in order to please the voters who voted for her. 65 00:03:45,840 --> 00:03:48,320 Speaker 3: As with some of the other arguments, you know, Trump 66 00:03:48,360 --> 00:03:51,720 Speaker 3: made this argument repeatedly before this ever went to trial, 67 00:03:51,760 --> 00:03:55,400 Speaker 3: and the judge shot that down repeatedly and allowed the 68 00:03:55,440 --> 00:03:57,960 Speaker 3: case to go to trial. So that's kind of kind 69 00:03:58,000 --> 00:04:00,440 Speaker 3: of a tough one there for Trump to be because 70 00:04:00,440 --> 00:04:02,360 Speaker 3: I believe the appeals court has already kind of heard 71 00:04:02,360 --> 00:04:04,040 Speaker 3: this argument before as well. 72 00:04:04,480 --> 00:04:08,040 Speaker 1: Yes, they heard a lot of these arguments, because Trump's 73 00:04:08,120 --> 00:04:11,280 Speaker 1: lawyers went to the Appellate Division at least ten times 74 00:04:11,640 --> 00:04:15,760 Speaker 1: to challenge the judge's rulings, including during the trial. Most 75 00:04:15,800 --> 00:04:19,279 Speaker 1: of those appeals were, of course unsuccessful. And yet another 76 00:04:19,440 --> 00:04:23,920 Speaker 1: argument that has resurfaced was based on the Statute of limitations. 77 00:04:24,720 --> 00:04:27,560 Speaker 3: Right. So Trump makes a big point in this filing 78 00:04:27,600 --> 00:04:31,159 Speaker 3: of saying that the last time the appeals court heard 79 00:04:31,160 --> 00:04:33,359 Speaker 3: this case, I believe it was when the motion to 80 00:04:33,400 --> 00:04:36,480 Speaker 3: dismiss was denied and the appeals court reviewed that decision. 81 00:04:37,160 --> 00:04:40,760 Speaker 3: It reversed the judge, the trial judge on a few things. First, 82 00:04:40,800 --> 00:04:43,599 Speaker 3: it granted dismissal for Ivanka Trump. Of course left the 83 00:04:43,640 --> 00:04:46,520 Speaker 3: claims pending against Donald Trump and his two sons, Don 84 00:04:46,600 --> 00:04:50,480 Speaker 3: Junior and Eric Trump. But they did reverse on Ivanka 85 00:04:50,480 --> 00:04:53,160 Speaker 3: Trump in part because of the statute of limitations, and 86 00:04:53,680 --> 00:04:57,240 Speaker 3: the court said, you can't go as far back as 87 00:04:57,240 --> 00:05:01,000 Speaker 3: you wanted to in looking at these documents, these transactions. 88 00:05:01,480 --> 00:05:04,559 Speaker 3: So the appeals court didn't say exactly what the trial 89 00:05:04,600 --> 00:05:06,880 Speaker 3: judge had to do with that information and left it 90 00:05:06,880 --> 00:05:08,960 Speaker 3: to the judge to sort of decide himself what to 91 00:05:09,000 --> 00:05:12,080 Speaker 3: do with it. And the judge, Arthur Engron, decided that 92 00:05:12,720 --> 00:05:16,839 Speaker 3: even with that statute of limitations determination, that the older 93 00:05:16,880 --> 00:05:21,000 Speaker 3: documents that were too old had been used repeatedly as 94 00:05:21,040 --> 00:05:25,000 Speaker 3: the years went on to verify Trump's finances and the 95 00:05:25,000 --> 00:05:29,120 Speaker 3: company's finances as the loans continued. So that's how he 96 00:05:29,640 --> 00:05:32,760 Speaker 3: kept the claims alive to go to trial. And Trump 97 00:05:32,839 --> 00:05:34,680 Speaker 3: argues in this document that he never should have done 98 00:05:34,680 --> 00:05:37,719 Speaker 3: that and claims that the judge, you know, just ignored 99 00:05:37,920 --> 00:05:40,840 Speaker 3: the appeals court order for no apparent reason. So that's 100 00:05:40,920 --> 00:05:42,920 Speaker 3: kind of what that part of the argument boils down to. 101 00:05:43,720 --> 00:05:47,720 Speaker 1: They're also questioning the size of the judgment, calling it 102 00:05:47,839 --> 00:05:53,039 Speaker 1: disproportionate and saying that it violates state and US constitutional 103 00:05:53,080 --> 00:05:55,400 Speaker 1: guarantees against excessive punishments. 104 00:05:55,680 --> 00:05:57,680 Speaker 3: Right, So this seems like a little bit more of 105 00:05:57,720 --> 00:05:59,640 Speaker 3: a typical type of thing he might see in an 106 00:05:59,640 --> 00:06:03,840 Speaker 3: appeal where a huge judgment is going to be scrutinized 107 00:06:03,839 --> 00:06:06,080 Speaker 3: by an appeals court and they're going to look at 108 00:06:06,080 --> 00:06:09,640 Speaker 3: whether or not the Attorney General calculated these damages correctly. 109 00:06:09,880 --> 00:06:12,560 Speaker 3: Of course, a lot of that includes interest. So we 110 00:06:12,640 --> 00:06:14,960 Speaker 3: saw a lot of arguments back and forth between the 111 00:06:14,960 --> 00:06:18,400 Speaker 3: parties just leading up to the final award being handed down, 112 00:06:19,080 --> 00:06:22,120 Speaker 3: and the Attorney General is confidence. She says she's confident 113 00:06:22,160 --> 00:06:25,159 Speaker 3: that they calculated all of this correctly. They're clawing back 114 00:06:25,800 --> 00:06:29,320 Speaker 3: what they say was the profit that Trump made off 115 00:06:29,360 --> 00:06:32,400 Speaker 3: of two deals in particular, including the old post office, 116 00:06:32,400 --> 00:06:35,760 Speaker 3: that luxury hotel in DC, and also saying that if 117 00:06:35,760 --> 00:06:39,320 Speaker 3: they hadn't had that money, that profit in Trump's bank account, 118 00:06:39,320 --> 00:06:41,200 Speaker 3: then he wouldn't have been able to do additional deals. 119 00:06:41,240 --> 00:06:43,880 Speaker 3: So they're sort of like clawing back what they say 120 00:06:44,000 --> 00:06:48,040 Speaker 3: was an illegal profit or an improper profit that Trump 121 00:06:48,080 --> 00:06:51,000 Speaker 3: got by lying about the value of his assets and 122 00:06:51,040 --> 00:06:53,560 Speaker 3: getting a better terms on his loans, and then, of course, 123 00:06:53,600 --> 00:06:55,400 Speaker 3: as I said, a lot of that is interest as well. 124 00:06:55,480 --> 00:06:58,359 Speaker 3: So I'm sure we'll see the appeals court scrutinize this 125 00:06:58,440 --> 00:07:00,480 Speaker 3: a lot and perhaps come up with a different poculation. 126 00:07:00,600 --> 00:07:02,240 Speaker 3: But you know, who knows at this point. 127 00:07:02,440 --> 00:07:05,000 Speaker 1: Now, there are some things that you don't normally see 128 00:07:05,000 --> 00:07:08,920 Speaker 1: in appeals Reef sounds sort of like campaigning or Trump 129 00:07:08,960 --> 00:07:12,160 Speaker 1: outside the courthouse that the verdict would be a disaster 130 00:07:12,280 --> 00:07:15,560 Speaker 1: for New York as businesses flee from an attorney general 131 00:07:15,680 --> 00:07:20,239 Speaker 1: targeting victimless transactions, and also talking about him as among 132 00:07:20,280 --> 00:07:24,800 Speaker 1: the most visionary and iconic real estate developers in American history. 133 00:07:25,240 --> 00:07:27,920 Speaker 1: Was there a lot of fluff? I'll call it fluff. 134 00:07:29,240 --> 00:07:33,000 Speaker 3: Well, yes, you could probably call it that, because I 135 00:07:33,000 --> 00:07:35,440 Speaker 3: don't I don't know that the appeals court judges care 136 00:07:35,560 --> 00:07:38,200 Speaker 3: about that in terms of determining whether or not this 137 00:07:38,320 --> 00:07:41,120 Speaker 3: was all correctly done here. But actually that did form 138 00:07:41,160 --> 00:07:43,920 Speaker 3: a big part of the defense case. At trial, his 139 00:07:44,000 --> 00:07:47,280 Speaker 3: lawyers put on a big presentation, you know, a PowerPoint 140 00:07:47,280 --> 00:07:50,120 Speaker 3: looking presentation that went on for quite some time, detailing 141 00:07:50,440 --> 00:07:53,800 Speaker 3: essentially every deal that Trump had ever done, starting from 142 00:07:53,840 --> 00:07:56,040 Speaker 3: the beginning of his career up to the you know, 143 00:07:56,080 --> 00:07:58,480 Speaker 3: the old post office in DC, and you know, putting 144 00:07:58,480 --> 00:08:00,760 Speaker 3: it in these these terms of him being such an 145 00:08:00,800 --> 00:08:04,760 Speaker 3: impressive real estate businessman. But then also they say this 146 00:08:04,880 --> 00:08:06,760 Speaker 3: to make the point that the banks that did business 147 00:08:06,760 --> 00:08:08,800 Speaker 3: with him were eager to do business with him, that 148 00:08:08,840 --> 00:08:12,480 Speaker 3: he was seen as visionary by these lenders that fought 149 00:08:12,520 --> 00:08:14,280 Speaker 3: for his business, and they did put up that kind 150 00:08:14,320 --> 00:08:17,520 Speaker 3: of testimony during the trial to show to make the 151 00:08:17,560 --> 00:08:19,880 Speaker 3: point again, as we said earlier, about there not being 152 00:08:19,920 --> 00:08:23,600 Speaker 3: any victims. So yeah, I mean, it's sort of inevitable 153 00:08:23,640 --> 00:08:26,080 Speaker 3: that you see stuff like that pop up in Trump's filings. 154 00:08:26,280 --> 00:08:28,400 Speaker 3: But I noticed that when he said that, you know, 155 00:08:28,520 --> 00:08:31,840 Speaker 3: businesses were fleeing New York as a result of Letitia 156 00:08:31,920 --> 00:08:35,520 Speaker 3: James's enforcement of this law, that there was no footnote there. 157 00:08:36,080 --> 00:08:38,600 Speaker 3: It didn't lead to any examples of other businesses that 158 00:08:38,640 --> 00:08:41,760 Speaker 3: were fleeing the state as a result of this case 159 00:08:41,800 --> 00:08:44,720 Speaker 3: and this trial. And I remember that after the verdict 160 00:08:44,760 --> 00:08:47,360 Speaker 3: was handed down, Trump had a press conference in which 161 00:08:47,360 --> 00:08:51,800 Speaker 3: he's said that Exxon Mobile had fled New York as 162 00:08:51,800 --> 00:08:54,640 Speaker 3: a result of a lawsuit brought by the New York 163 00:08:54,679 --> 00:08:57,960 Speaker 3: Attorney General, a case that actually the Attorney General lost, 164 00:08:58,360 --> 00:09:01,160 Speaker 3: but at any rate. He said that that excellent had 165 00:09:01,160 --> 00:09:03,280 Speaker 3: fled and that's that's actually just not true. 166 00:09:03,760 --> 00:09:06,680 Speaker 1: Trump had a problem coming up with the cash or 167 00:09:06,840 --> 00:09:10,080 Speaker 1: the money for the appealed bond. Remind us what happened there. 168 00:09:10,320 --> 00:09:14,600 Speaker 3: Yeah, So in order to put that judgment on hold 169 00:09:14,840 --> 00:09:18,480 Speaker 3: during his appeal, he had to put up an appeal 170 00:09:18,480 --> 00:09:22,480 Speaker 3: bond about one hundred and seventy five million dollars. Initially, 171 00:09:22,520 --> 00:09:25,000 Speaker 3: the state wanted an appeal bond of the full amounts 172 00:09:25,240 --> 00:09:28,320 Speaker 3: and plus i think an extra twenty percent, which they 173 00:09:28,360 --> 00:09:30,640 Speaker 3: said that they were entitled to because Trump couldn't be 174 00:09:30,679 --> 00:09:34,400 Speaker 3: trusted to pay up if his appeal failed. But the 175 00:09:34,440 --> 00:09:38,400 Speaker 3: courts did not agree and lowered the size of the 176 00:09:38,440 --> 00:09:41,960 Speaker 3: appeal bond. And of course Trump was able to do 177 00:09:42,000 --> 00:09:44,840 Speaker 3: that by finding a company that provides these bonds based 178 00:09:44,840 --> 00:09:47,800 Speaker 3: out of California, that a Trump supporter essentially owns the 179 00:09:47,800 --> 00:09:50,760 Speaker 3: company and agreed to do that for him. There was 180 00:09:50,800 --> 00:09:54,080 Speaker 3: still some contention between the parties over whether or not 181 00:09:54,760 --> 00:09:58,120 Speaker 3: that company could pay up if necessary, but for all 182 00:09:58,160 --> 00:10:00,199 Speaker 3: intents and purposes that that debate was sort of put 183 00:10:00,240 --> 00:10:03,280 Speaker 3: to rest and the judgment is on hold. Trump's assets 184 00:10:03,280 --> 00:10:05,560 Speaker 3: were not seized, as the Attorney General is threatening to 185 00:10:05,559 --> 00:10:08,000 Speaker 3: do if he didn't pay up. So that bit of 186 00:10:08,040 --> 00:10:11,720 Speaker 3: drama is over, and now it's sort of turning to 187 00:10:12,160 --> 00:10:16,320 Speaker 3: the actual appeals court case, which itself could potentially drag 188 00:10:16,360 --> 00:10:17,200 Speaker 3: on for quite some time. 189 00:10:17,559 --> 00:10:20,880 Speaker 1: Eric Trump's sentencing for his conviction in the hush money 190 00:10:20,880 --> 00:10:25,040 Speaker 1: case has been put off because he's asking the judge 191 00:10:25,080 --> 00:10:29,480 Speaker 1: to toss out his conviction due to the controversial Supreme 192 00:10:29,559 --> 00:10:33,520 Speaker 1: Court opinion on presidential immunity. Where does that stand? 193 00:10:34,280 --> 00:10:37,840 Speaker 3: So, of course, Trump lost his first criminal trial here 194 00:10:37,840 --> 00:10:40,400 Speaker 3: in New York, the so called hush money case over 195 00:10:40,520 --> 00:10:43,120 Speaker 3: the falsified records that hid his hush money payment to 196 00:10:43,240 --> 00:10:46,600 Speaker 3: porn star Stormy Daniels. And even though that verdict was 197 00:10:46,600 --> 00:10:51,120 Speaker 3: handed down by a jury before this Supreme Court's immunity ruling, 198 00:10:51,280 --> 00:10:53,080 Speaker 3: Trump has circled back and said, hey, this has to 199 00:10:53,120 --> 00:10:55,840 Speaker 3: be tossed out. He has asked the judge to do so, 200 00:10:56,360 --> 00:11:00,440 Speaker 3: and the Manhattan District Attorney, Alvin Bragg is to file 201 00:11:00,520 --> 00:11:03,680 Speaker 3: his response to that request today. We may not necessarily 202 00:11:03,760 --> 00:11:06,160 Speaker 3: see it right away today, but we can expect that 203 00:11:06,200 --> 00:11:09,360 Speaker 3: the prosecutors are going to urge the court to uphold 204 00:11:09,360 --> 00:11:11,200 Speaker 3: the verdicts and say, hey, even though some of this 205 00:11:11,280 --> 00:11:14,680 Speaker 3: evidence in testimony potentially could be impacted by the immunity 206 00:11:14,720 --> 00:11:17,040 Speaker 3: ruling from the Supreme Court. All of the other evidence 207 00:11:17,040 --> 00:11:20,400 Speaker 3: in testimony in the case justify this verdict. I think 208 00:11:20,400 --> 00:11:22,400 Speaker 3: that's probably what we can expect to hear from the 209 00:11:22,400 --> 00:11:23,200 Speaker 3: district attorney. 210 00:11:23,360 --> 00:11:24,640 Speaker 1: Thanks so much for coming on the show. 211 00:11:24,760 --> 00:11:24,960 Speaker 3: Eric. 212 00:11:25,080 --> 00:11:28,320 Speaker 1: That's Bloomberg Legal reporter Eric Larson coming up next on 213 00:11:28,320 --> 00:11:32,280 Speaker 1: the Bloomberg Lawn Show. The fallout continues in the Alaska 214 00:11:32,400 --> 00:11:36,600 Speaker 1: judge scandal. Just how many cases might be reversed. I'm 215 00:11:36,679 --> 00:11:43,120 Speaker 1: June Grosso and you're listening to Bloomberg. Federal prosecutors in 216 00:11:43,160 --> 00:11:47,839 Speaker 1: Alaska have identified nearly two dozen criminal cases with potentially 217 00:11:48,000 --> 00:11:53,200 Speaker 1: undisclosed conflicts of interest involving former federal judge Joshua Kindred 218 00:11:53,400 --> 00:11:56,680 Speaker 1: and the attorneys who worked on cases before him. The 219 00:11:56,720 --> 00:12:01,679 Speaker 1: Trump appointee resigned this month amid allegation of sexual misconduct. 220 00:12:02,000 --> 00:12:06,920 Speaker 1: A judiciary investigation found that Kindred had an inappropriate relationship 221 00:12:07,000 --> 00:12:09,800 Speaker 1: with a female clerk who later worked for the US 222 00:12:09,880 --> 00:12:13,440 Speaker 1: Attorney's office in Alaska. The battle also found the judge 223 00:12:13,480 --> 00:12:17,800 Speaker 1: created a hostile work environment, including by discussing his romantic 224 00:12:17,920 --> 00:12:20,960 Speaker 1: life in the workplace and lied about his conduct to 225 00:12:21,080 --> 00:12:25,440 Speaker 1: judiciary officials conducting the inquiry. The Federal Public Defender's Office 226 00:12:25,480 --> 00:12:29,520 Speaker 1: is undertaking its own review. Federal Defender Jamie McGrady has 227 00:12:29,559 --> 00:12:33,680 Speaker 1: said she believes the number of potentially conflicted cases is 228 00:12:33,800 --> 00:12:37,800 Speaker 1: higher than the twenty three identified by prosecutors. The inquiry 229 00:12:37,920 --> 00:12:41,400 Speaker 1: could lead to the reopening of multiple criminal cases that 230 00:12:41,600 --> 00:12:44,400 Speaker 1: came before Kindred during his more than four years on 231 00:12:44,440 --> 00:12:47,960 Speaker 1: the bench. Joining me is Abby Smith, director of Georgetown 232 00:12:48,040 --> 00:12:52,600 Speaker 1: Law's Criminal Defense and Prisoner Advocacy Clinic. The US Attorney 233 00:12:52,760 --> 00:12:56,280 Speaker 1: is conducting an investigation. The Federal Public Defender is conducting 234 00:12:56,280 --> 00:13:00,480 Speaker 1: an investigation to identify potential conflicts of interest. Would it 235 00:13:00,520 --> 00:13:03,880 Speaker 1: be any case in which the judge had a relationship 236 00:13:04,080 --> 00:13:06,520 Speaker 1: with the attorney? Would it be more than that? 237 00:13:07,360 --> 00:13:11,679 Speaker 4: Well, any investigation would start with the judge having a 238 00:13:11,720 --> 00:13:15,400 Speaker 4: relationship with a prosecutor in an ongoing case in which 239 00:13:15,440 --> 00:13:20,440 Speaker 4: the prosecutor is one of counsel. That needed to have 240 00:13:20,480 --> 00:13:25,040 Speaker 4: been disclosed at the very least, and it was you know, 241 00:13:25,200 --> 00:13:29,520 Speaker 4: up to either side could have asked for recusal or disqualification. 242 00:13:29,960 --> 00:13:32,520 Speaker 4: But I think this case goes beyond that. I mean, 243 00:13:32,559 --> 00:13:36,120 Speaker 4: it sounds like there is a pattern of misbehavior on 244 00:13:36,160 --> 00:13:40,319 Speaker 4: the part of the judge that some would or you 245 00:13:40,400 --> 00:13:44,040 Speaker 4: made him an unfit judge while he was presiding over 246 00:13:44,120 --> 00:13:48,360 Speaker 4: cases period. You know, the case that came to mind 247 00:13:48,880 --> 00:13:52,160 Speaker 4: for me when I read about what's happening in Alaska 248 00:13:52,480 --> 00:13:55,240 Speaker 4: is it's a little different, but it led to the 249 00:13:55,360 --> 00:14:00,240 Speaker 4: dismissal of several dozen cases. It involved a judge who 250 00:14:00,400 --> 00:14:05,480 Speaker 4: had been confronted by the FBI for having taken bribes 251 00:14:05,640 --> 00:14:10,360 Speaker 4: from a leading union in Philadelphia, and they offered her 252 00:14:10,400 --> 00:14:14,839 Speaker 4: a chance to be an informant to earn some mitigation 253 00:14:15,320 --> 00:14:19,200 Speaker 4: and you know, perhaps non prosecution. Anyhow, she wore a 254 00:14:19,200 --> 00:14:21,560 Speaker 4: wire and proceeded to start talking to a bunch of 255 00:14:21,600 --> 00:14:24,920 Speaker 4: other judges as she's a sitting judge presiding over cases 256 00:14:25,480 --> 00:14:28,680 Speaker 4: and basically in the pocket of the FBI, and with 257 00:14:28,760 --> 00:14:32,680 Speaker 4: a deep need to ingratiate herself with the government so 258 00:14:32,760 --> 00:14:37,120 Speaker 4: that she not be prosecuted. So she was wearing many hats. 259 00:14:37,160 --> 00:14:40,120 Speaker 4: And the challenge to all of the cases that she 260 00:14:40,400 --> 00:14:43,080 Speaker 4: ruled upon during that time period was a couple of things. 261 00:14:43,120 --> 00:14:46,440 Speaker 4: As you know, broadly, a problem under the due process 262 00:14:46,440 --> 00:14:50,800 Speaker 4: clause of the US Constitution is this good and fair behavior. 263 00:14:50,960 --> 00:14:54,040 Speaker 4: There were also questions about separation of powers. Can you 264 00:14:54,080 --> 00:14:58,080 Speaker 4: be both a judge and essentially a prosecutorial agent at 265 00:14:58,120 --> 00:15:01,760 Speaker 4: the same time. You know, the idea that she was 266 00:15:01,880 --> 00:15:04,200 Speaker 4: attempting to curry favor, that is, she needed to be 267 00:15:04,240 --> 00:15:07,640 Speaker 4: the best informant she could be meant that some of 268 00:15:07,680 --> 00:15:10,720 Speaker 4: that was going to go down hard on the defendants 269 00:15:10,760 --> 00:15:13,680 Speaker 4: appearing before, so she would be more and more appealing 270 00:15:13,920 --> 00:15:17,600 Speaker 4: to the prosecution. So here's this judge in Alaska who, 271 00:15:17,880 --> 00:15:20,800 Speaker 4: according to what I've read, and one of the more 272 00:15:20,840 --> 00:15:23,000 Speaker 4: troubling aspects of the case is that he was found 273 00:15:23,040 --> 00:15:27,880 Speaker 4: to applied to the higher court investigating him, that he 274 00:15:27,920 --> 00:15:31,880 Speaker 4: had engaged in widespread misconduct, you know, an improper relationship 275 00:15:32,200 --> 00:15:37,400 Speaker 4: with one currently employed prosecutor and being wildly inappropriate, engaging 276 00:15:37,400 --> 00:15:41,240 Speaker 4: in what probably would be hostile workplace environment behavior, maybe 277 00:15:41,280 --> 00:15:45,040 Speaker 4: sexual harassment with others. You know, I'm interested in whether 278 00:15:45,080 --> 00:15:48,239 Speaker 4: there could be a challenge as to general unfitness. 279 00:15:48,920 --> 00:15:51,960 Speaker 1: Is it a no brainer that those cases involving the 280 00:15:52,040 --> 00:15:55,440 Speaker 1: prosecutor he was having the sexual relationship with, or the 281 00:15:55,480 --> 00:15:58,400 Speaker 1: one who sent him the nude photos, will those cases 282 00:15:58,520 --> 00:16:00,640 Speaker 1: easily be reversed? 283 00:16:01,160 --> 00:16:03,000 Speaker 4: Yes, it seems to be. That's the that's the low 284 00:16:03,040 --> 00:16:06,440 Speaker 4: hanging fruit. That should be pretty easy. But the question 285 00:16:06,560 --> 00:16:10,160 Speaker 4: is who's going to start that process. So, just to 286 00:16:10,200 --> 00:16:14,800 Speaker 4: give you the defense perspective, depends. You know, quite often 287 00:16:15,000 --> 00:16:18,920 Speaker 4: it's useful to challenge any and all convictions because it's 288 00:16:18,960 --> 00:16:21,680 Speaker 4: harder to prove a case the more time goes by. 289 00:16:21,920 --> 00:16:25,760 Speaker 4: So arguably there's some strategic advantage and kind of always 290 00:16:25,800 --> 00:16:27,440 Speaker 4: if you get a second bite of the apple, to 291 00:16:27,480 --> 00:16:30,200 Speaker 4: go for it. On the other hand, I don't know. 292 00:16:30,600 --> 00:16:33,640 Speaker 4: You know, maybe this judge was a fairly lenient sentence, 293 00:16:33,680 --> 00:16:36,760 Speaker 4: or maybe you know, there was a plea before him, 294 00:16:36,760 --> 00:16:39,400 Speaker 4: and a better that the defendant be sentenced by this judge, 295 00:16:39,400 --> 00:16:43,160 Speaker 4: no matter how corrupt or bizarre his behavior, than the 296 00:16:43,240 --> 00:16:45,760 Speaker 4: luck of the draw the next time around. So you know, 297 00:16:45,920 --> 00:16:49,280 Speaker 4: I guess question number one will be who brings the challenge? 298 00:16:49,360 --> 00:16:53,600 Speaker 4: Will the federal defenders seek to undo every conviction that 299 00:16:53,680 --> 00:16:56,760 Speaker 4: came before this judge? Will the prosecution, which has an 300 00:16:56,800 --> 00:17:01,240 Speaker 4: ethical responsibility to do justice, will they, on their own accord, 301 00:17:01,680 --> 00:17:05,280 Speaker 4: you know, move to dismiss those cases or somehow seeks 302 00:17:05,400 --> 00:17:08,400 Speaker 4: the ability to not prost those cases after the fact. 303 00:17:08,440 --> 00:17:10,240 Speaker 4: I don't know, and I don't want to paint this 304 00:17:10,359 --> 00:17:15,760 Speaker 4: too broadly, because Alaska's a relatively small jurisdiction as jurisdictions go, 305 00:17:16,240 --> 00:17:19,440 Speaker 4: and you know, the people tend to know each other 306 00:17:19,520 --> 00:17:22,280 Speaker 4: in courthouses, the criminal bar tends to know each other. 307 00:17:22,359 --> 00:17:24,960 Speaker 4: It's not unheard of that a judge is married to 308 00:17:25,000 --> 00:17:28,400 Speaker 4: a prosecutor, or to a defense lawyer, or a prosecutor 309 00:17:28,440 --> 00:17:31,800 Speaker 4: is married to a defense lawyer, because that's the society 310 00:17:31,880 --> 00:17:35,760 Speaker 4: they keep. But it needs to be disclosed, and the 311 00:17:35,840 --> 00:17:38,600 Speaker 4: prudent course would be to not have that person appear 312 00:17:38,640 --> 00:17:41,200 Speaker 4: before you ever if you're married to them. You know, 313 00:17:41,240 --> 00:17:44,120 Speaker 4: I'm not saying that like it doesn't happen. I don't 314 00:17:44,160 --> 00:17:46,680 Speaker 4: need to sound like a Pollyanna about it. But it's 315 00:17:46,720 --> 00:17:49,359 Speaker 4: just that there needs to be disclosure, and the people 316 00:17:49,400 --> 00:17:53,720 Speaker 4: involved need to be wise about ensuring fairness to other people, 317 00:17:53,720 --> 00:17:56,400 Speaker 4: including to the public, because that's the thing about judges 318 00:17:56,600 --> 00:18:01,119 Speaker 4: and prosecutors. Appearance of proprieties the most important thing, because 319 00:18:01,119 --> 00:18:03,800 Speaker 4: that has to do with people regarding the system as legitimate. 320 00:18:03,960 --> 00:18:06,920 Speaker 1: I mean, it seems like the US Attorney's office could 321 00:18:06,960 --> 00:18:09,560 Speaker 1: also be at faull Tier. Yes, three of the lawyers 322 00:18:09,600 --> 00:18:13,439 Speaker 1: were from that office, and apparently the office was aware 323 00:18:13,520 --> 00:18:15,119 Speaker 1: of a conflict. 324 00:18:14,960 --> 00:18:18,000 Speaker 4: Yes, they should have disclosed. They should have said on 325 00:18:18,160 --> 00:18:20,760 Speaker 4: the record so the events could at least consider it 326 00:18:21,240 --> 00:18:23,400 Speaker 4: and want to reveal for the record that or disclosed 327 00:18:23,440 --> 00:18:25,880 Speaker 4: for the record that you know. I have a colleague 328 00:18:25,880 --> 00:18:28,720 Speaker 4: who's currently involved in a relationship with a judge. Here's 329 00:18:28,760 --> 00:18:32,040 Speaker 4: what happens pretty commonly, and I just don't understand why 330 00:18:32,040 --> 00:18:35,960 Speaker 4: this is a problem, because it's so ordinary for judges 331 00:18:36,119 --> 00:18:39,639 Speaker 4: who've had law clerks who then either join the local 332 00:18:39,720 --> 00:18:43,399 Speaker 4: prosecutor's office or the local public defender office or haines 333 00:18:43,480 --> 00:18:46,720 Speaker 4: a shingle. It's usually an arrangement where that judge doesn't 334 00:18:47,119 --> 00:18:50,600 Speaker 4: take cases involving that lawyer who was so recently a 335 00:18:50,680 --> 00:18:54,280 Speaker 4: judge's clerk. Now, depending upon the size of the jurisdiction 336 00:18:54,480 --> 00:18:58,119 Speaker 4: and the years that go by, that could change. But 337 00:18:58,200 --> 00:19:00,640 Speaker 4: I think that's the kind of prudent thing to do. 338 00:19:01,320 --> 00:19:04,560 Speaker 4: And if the judge forgets, then the lawyer should you 339 00:19:04,760 --> 00:19:07,920 Speaker 4: reveal that that's the relationship. It's a little trickier when 340 00:19:07,960 --> 00:19:11,679 Speaker 4: you're defense counsel because defense because our ethical obligation is 341 00:19:11,680 --> 00:19:14,720 Speaker 4: to pursue our client's interests, and there's no ethical rule 342 00:19:14,800 --> 00:19:17,399 Speaker 4: that says you have to disclose that you had some 343 00:19:17,560 --> 00:19:20,359 Speaker 4: sort of relationships with a judge, you know. I mean, 344 00:19:20,400 --> 00:19:22,520 Speaker 4: there are some rules that have to do with what 345 00:19:22,680 --> 00:19:28,720 Speaker 4: proper relationships you know, exist between lawyer and client. But frankly, 346 00:19:28,840 --> 00:19:31,240 Speaker 4: the ethical rules don't have a whole lot to say 347 00:19:31,280 --> 00:19:35,640 Speaker 4: about relationships among lawyers or judges. But the US Attorney's 348 00:19:35,680 --> 00:19:38,240 Speaker 4: office should have disclosed and the judge should have disclosed 349 00:19:38,960 --> 00:19:39,760 Speaker 4: apparently period. 350 00:19:40,000 --> 00:19:42,240 Speaker 1: One of the attorneys of the potential conflict is a 351 00:19:42,280 --> 00:19:43,360 Speaker 1: defense lawyer. 352 00:19:44,600 --> 00:19:47,000 Speaker 4: Well, that, you know, the judge needed to have disclosed. 353 00:19:47,520 --> 00:19:49,600 Speaker 4: So do you understand what I'm saying about The defense 354 00:19:49,680 --> 00:19:52,760 Speaker 4: is in a really different position. I'm not gonna you know, 355 00:19:52,800 --> 00:19:55,280 Speaker 4: I might have a I might have a counseling session 356 00:19:55,280 --> 00:19:58,720 Speaker 4: with my client where I say, look, I have this, 357 00:19:58,880 --> 00:20:01,919 Speaker 4: you know, particular relationtionship with this judge. I want you 358 00:20:02,000 --> 00:20:06,119 Speaker 4: to know that, and if you're uncomfortable with that, you know, 359 00:20:06,200 --> 00:20:09,240 Speaker 4: let's talk. That's true. I mean, you might have a conversation, 360 00:20:09,400 --> 00:20:12,120 Speaker 4: and you probably should have a conversation with your clients. 361 00:20:12,160 --> 00:20:17,920 Speaker 4: But defense lawyer doesn't have to disclose having any sort 362 00:20:17,960 --> 00:20:21,919 Speaker 4: of social relationship with a judge. That would be for 363 00:20:21,960 --> 00:20:25,520 Speaker 4: the judge to do unless the lawyer does that, because 364 00:20:25,520 --> 00:20:28,720 Speaker 4: it serves the client's interest. I mean, I've had judges say, 365 00:20:28,960 --> 00:20:31,359 Speaker 4: you know, I'm a law professor who else appears in court. 366 00:20:32,080 --> 00:20:35,760 Speaker 4: One time, I was at some very large fundraising event 367 00:20:36,200 --> 00:20:39,280 Speaker 4: in Washington, d C. There are probably several hundred people there, 368 00:20:39,280 --> 00:20:42,320 Speaker 4: and I ran into a judge who I sometimes appear before. 369 00:20:42,600 --> 00:20:45,639 Speaker 4: I'm not friends with this judge. I've never socialized with 370 00:20:45,720 --> 00:20:49,639 Speaker 4: this judge. I wouldn't even characterize our contact at the 371 00:20:49,680 --> 00:20:53,200 Speaker 4: event as having a drink together. It didn't go that far. 372 00:20:53,359 --> 00:20:56,240 Speaker 4: It's more like a little small suck. Then the next 373 00:20:56,240 --> 00:20:58,119 Speaker 4: time I appear in court, the judges, I, you know, 374 00:20:58,160 --> 00:21:02,040 Speaker 4: I feel compelled to disclose that that Miss Smith and I, 375 00:21:02,400 --> 00:21:05,240 Speaker 4: you know, are are social acquaintances or something. And I thought, 376 00:21:05,359 --> 00:21:08,240 Speaker 4: oh my god, it's going to hurt my reputation. I'm 377 00:21:08,280 --> 00:21:11,240 Speaker 4: not good friends with this judge, trust me. But no 378 00:21:11,440 --> 00:21:14,520 Speaker 4: judges do that. And I think careful judges would err 379 00:21:14,520 --> 00:21:17,600 Speaker 4: on the side of overdisclosure and let the party stuff 380 00:21:17,680 --> 00:21:19,840 Speaker 4: it out. But I can't believe this judge was keeping 381 00:21:19,880 --> 00:21:22,720 Speaker 4: it secret. And if the US Attorney's office knew, that's 382 00:21:22,880 --> 00:21:26,239 Speaker 4: really bad for him. I'm sure that smart people in 383 00:21:26,280 --> 00:21:29,199 Speaker 4: the local federal defender are thinking of all kinds of 384 00:21:29,280 --> 00:21:32,560 Speaker 4: creative motions to get a second crack in some of 385 00:21:32,560 --> 00:21:35,639 Speaker 4: those cases that were decided by that US Attorney's office 386 00:21:35,640 --> 00:21:36,480 Speaker 4: and that judge. 387 00:21:36,720 --> 00:21:38,639 Speaker 1: Do you think that it will take years for this 388 00:21:38,800 --> 00:21:41,920 Speaker 1: to unwind all the repercussions. 389 00:21:42,320 --> 00:21:45,200 Speaker 4: I don't know, because I betshit things move more quickly 390 00:21:45,240 --> 00:21:48,520 Speaker 4: in Alaska. I don't think the docket is his. If 391 00:21:48,520 --> 00:21:51,600 Speaker 4: this was, you know, the Southern District of New York, 392 00:21:51,880 --> 00:21:54,479 Speaker 4: then I would say could take a long time. But 393 00:21:54,560 --> 00:21:56,520 Speaker 4: I'm not sure it will take that long a time. 394 00:21:56,920 --> 00:22:01,600 Speaker 4: It's really interesting that the judge was not very few judges, 395 00:22:01,720 --> 00:22:04,560 Speaker 4: especially so junior and their tenure, would give up a 396 00:22:04,600 --> 00:22:08,320 Speaker 4: lifetime appointment. There must be dirt there. So you know, 397 00:22:08,520 --> 00:22:11,879 Speaker 4: I think investigation is wise. We may only have the 398 00:22:11,880 --> 00:22:12,800 Speaker 4: tip of the icebergs. 399 00:22:13,119 --> 00:22:16,280 Speaker 1: Let's say you're a defense lawyer looking at this. Your 400 00:22:16,280 --> 00:22:20,000 Speaker 1: client had a case before this judge involving the prosecutor, 401 00:22:20,200 --> 00:22:23,280 Speaker 1: but your client got a light sentence. How do you 402 00:22:23,400 --> 00:22:26,399 Speaker 1: weigh whether to retry the case. I mean, that seems 403 00:22:26,400 --> 00:22:27,560 Speaker 1: like it's a tough decision. 404 00:22:28,200 --> 00:22:30,840 Speaker 4: It is. That's a really good question. I would go 405 00:22:30,880 --> 00:22:32,960 Speaker 4: out and see the client say, look, all hell is 406 00:22:33,000 --> 00:22:36,040 Speaker 4: broken loose. This judge is now resigned, you know, under 407 00:22:36,080 --> 00:22:41,159 Speaker 4: a cloud, and we may have an opportunity to seek 408 00:22:41,200 --> 00:22:46,880 Speaker 4: to revisit your case to maybe get the conviction set 409 00:22:46,920 --> 00:22:50,560 Speaker 4: aside and or get you resentenced. But let's think about this. 410 00:22:51,119 --> 00:22:53,479 Speaker 4: Let's look at the other judges that you could appear 411 00:22:53,520 --> 00:22:57,320 Speaker 4: before for sentencing. Here's the sentence you got. Is it 412 00:22:57,400 --> 00:22:59,840 Speaker 4: worth the risk? Is this what you want to do? 413 00:23:00,119 --> 00:23:03,800 Speaker 4: Or should we consider ourselves having cut our losses. That's 414 00:23:03,800 --> 00:23:06,080 Speaker 4: the kind of discussion any good defense lawyer would have 415 00:23:06,520 --> 00:23:10,280 Speaker 4: that even though in the initial moment of kind of 416 00:23:10,880 --> 00:23:16,000 Speaker 4: surprise and maybe even hilarity about a judge, you know, 417 00:23:16,160 --> 00:23:18,639 Speaker 4: blowing up the way this one did. You know, you 418 00:23:18,720 --> 00:23:21,320 Speaker 4: may want to say, oh, let's bring every single case, 419 00:23:21,480 --> 00:23:24,639 Speaker 4: but you know, let's challenge every single case that this 420 00:23:24,760 --> 00:23:27,159 Speaker 4: judge had a hand in. Might not be good for 421 00:23:27,200 --> 00:23:29,399 Speaker 4: the client. You got to take each case individually. 422 00:23:30,040 --> 00:23:32,720 Speaker 1: It's really interesting seeing it from the defense point of view. 423 00:23:32,920 --> 00:23:37,080 Speaker 1: Thanks so much, Abby. That's Abby Smith, director of Georgetown Laws, 424 00:23:37,119 --> 00:23:41,320 Speaker 1: Criminal Defense and Prisoner Advocacy Clinic. Coming up next on 425 00:23:41,320 --> 00:23:44,640 Speaker 1: The Bloomberg Lawn Show, the Federal Trade Commission is looking 426 00:23:44,720 --> 00:23:49,000 Speaker 1: into what it calls the opaque market of surveillance pricing, 427 00:23:49,400 --> 00:23:53,560 Speaker 1: using consumer data to charge different customers different prices for 428 00:23:53,640 --> 00:23:56,800 Speaker 1: the same goods. I'm June Grasso, and this is Bloomberg. 429 00:23:58,359 --> 00:24:03,960 Speaker 1: Federal regulators are examined how companies including MasterCard, JP, Morgan Chase, 430 00:24:04,320 --> 00:24:09,400 Speaker 1: and McKinsey provide clients with algorithms that use consumers personal 431 00:24:09,520 --> 00:24:14,040 Speaker 1: data to set prices based on a shopper's individual characteristics. 432 00:24:14,200 --> 00:24:16,800 Speaker 1: The Federal Trade Commission said it was seeking to better 433 00:24:16,920 --> 00:24:22,440 Speaker 1: understand the opaque market of surveillance pricing practices using consumer 434 00:24:22,560 --> 00:24:27,359 Speaker 1: data including credit information, location, and browsing history, to charge 435 00:24:27,400 --> 00:24:31,600 Speaker 1: different customers different prices for the same goods. To do this, 436 00:24:31,760 --> 00:24:36,680 Speaker 1: the agency noted third party intermediaries claimed to use advanced algorithms, 437 00:24:37,000 --> 00:24:42,080 Speaker 1: artificial intelligence and other technology. FTC chair Lina Khan said, 438 00:24:42,359 --> 00:24:46,960 Speaker 1: quote firms that harvest Americans' personal data can't put people's 439 00:24:47,000 --> 00:24:50,760 Speaker 1: privacy at risk. Now, firms could be exploiting this vast 440 00:24:50,800 --> 00:24:55,359 Speaker 1: trove of personal information to charge people higher prices. Joining 441 00:24:55,400 --> 00:25:00,600 Speaker 1: me is Bloomberg? Anti trust reporter Leah Nylan exactly is 442 00:25:00,680 --> 00:25:02,159 Speaker 1: surveillance pricing? 443 00:25:02,840 --> 00:25:05,600 Speaker 5: Surveillance pricing is something that goes under a lot of 444 00:25:05,600 --> 00:25:10,320 Speaker 5: different names. Sometimes people call it price discrimination. Sometimes they 445 00:25:10,359 --> 00:25:14,800 Speaker 5: call it online pricing, dynamic pricing, a couple different things. Essentially, 446 00:25:14,800 --> 00:25:17,399 Speaker 5: the idea is that the price isn't one thing. It 447 00:25:17,480 --> 00:25:20,240 Speaker 5: changes based on who is buying it and when they're 448 00:25:20,240 --> 00:25:23,840 Speaker 5: buying it. The best example of this probably airline pricing. 449 00:25:24,040 --> 00:25:26,680 Speaker 5: You know, people are very familiar with the idea that 450 00:25:26,720 --> 00:25:30,280 Speaker 5: the price of the airline ticket changes depending on when 451 00:25:30,320 --> 00:25:32,840 Speaker 5: you're buying it, like how close to the flight it is, 452 00:25:33,280 --> 00:25:35,480 Speaker 5: and sometimes whether you're logged in or not. So whether 453 00:25:35,560 --> 00:25:38,600 Speaker 5: it knows that you have like rewards or status with 454 00:25:38,640 --> 00:25:40,840 Speaker 5: that particular airline, it might change the price. 455 00:25:41,119 --> 00:25:44,520 Speaker 1: Does it also happen with like consumer products? Let's say 456 00:25:44,600 --> 00:25:46,879 Speaker 1: I don't know, vacuum cleaners. People are used to hearing 457 00:25:46,920 --> 00:25:49,879 Speaker 1: that about airlines, you know, when you book makes a difference, 458 00:25:49,880 --> 00:25:52,720 Speaker 1: et cetera. But what about like consumer products. 459 00:25:53,160 --> 00:25:56,399 Speaker 5: Yeah, So that's why that FTC is conducting the study 460 00:25:56,480 --> 00:25:59,400 Speaker 5: because they have heard from a lot of people that 461 00:25:59,560 --> 00:26:04,200 Speaker 5: companies are now sort of using these types of pricing 462 00:26:04,240 --> 00:26:07,119 Speaker 5: techniques on more and more consumer products. So one of 463 00:26:07,160 --> 00:26:10,280 Speaker 5: the biggest examples that people know about also is McDonald's. 464 00:26:10,320 --> 00:26:13,119 Speaker 5: For example, if you walk up to the counter or 465 00:26:13,119 --> 00:26:16,280 Speaker 5: go through the drive through at McDonald's, you're actually probably 466 00:26:16,320 --> 00:26:18,840 Speaker 5: going to be paying a little bit more money than 467 00:26:18,920 --> 00:26:22,080 Speaker 5: if you ordered a head through the apps because McDonald's 468 00:26:22,119 --> 00:26:26,520 Speaker 5: often gives out different types of rewards or you know, 469 00:26:26,600 --> 00:26:29,639 Speaker 5: price discounts if you're ordering through the app. So it 470 00:26:29,720 --> 00:26:32,760 Speaker 5: is becoming more and more common for these types of 471 00:26:33,160 --> 00:26:36,520 Speaker 5: consumer products to have some kind of dynamic pricing elements 472 00:26:36,560 --> 00:26:40,840 Speaker 5: to them, and they're very interested in how companies are 473 00:26:40,880 --> 00:26:44,000 Speaker 5: deciding when they want to use these types of discounts 474 00:26:44,080 --> 00:26:47,800 Speaker 5: or price hikes, and whether they might be using information 475 00:26:47,880 --> 00:26:51,479 Speaker 5: about individuals that's not technically allowed, so things like you 476 00:26:51,480 --> 00:26:54,160 Speaker 5: know your gender, your race, things like that that are 477 00:26:54,200 --> 00:26:56,680 Speaker 5: prevented by various discrimination laws. 478 00:26:56,920 --> 00:27:00,600 Speaker 1: Are they allowed to use the consumers online footprint? 479 00:27:01,160 --> 00:27:03,399 Speaker 5: So that's another thing that FEC is going to be 480 00:27:03,440 --> 00:27:07,480 Speaker 5: looking at, how much are these companies using your online footprint? Because, 481 00:27:07,480 --> 00:27:10,800 Speaker 5: for example, when you go online, a lot of that 482 00:27:10,960 --> 00:27:13,439 Speaker 5: is tracked through your browser history, and it's not that 483 00:27:13,560 --> 00:27:16,600 Speaker 5: hard sometimes for companies to like figure out maybe you 484 00:27:16,600 --> 00:27:19,240 Speaker 5: were looking at this pair of shoes online, so maybe 485 00:27:19,240 --> 00:27:21,280 Speaker 5: they should when you go into the store offer you 486 00:27:21,320 --> 00:27:23,479 Speaker 5: a discount or maybe a price hike on it. You 487 00:27:23,520 --> 00:27:26,800 Speaker 5: see it in different types of things like surge pricing 488 00:27:26,960 --> 00:27:29,800 Speaker 5: on Uber. There was a really interesting study that found 489 00:27:30,160 --> 00:27:33,800 Speaker 5: if your phone battery is low, Uber is much more 490 00:27:34,000 --> 00:27:39,720 Speaker 5: likely to put surge pricing on a particular fare because 491 00:27:40,160 --> 00:27:42,080 Speaker 5: you really know that you really need to get where 492 00:27:42,119 --> 00:27:47,960 Speaker 5: you're going before your phone dies. So that's outrageous to me. Yeah, 493 00:27:48,040 --> 00:27:51,959 Speaker 5: they found, like, there was a study that had a 494 00:27:52,000 --> 00:27:55,280 Speaker 5: few people at the same place ask for a ride 495 00:27:55,440 --> 00:27:58,440 Speaker 5: to the same location, and then they looked at how 496 00:27:58,480 --> 00:28:00,679 Speaker 5: much they were charging those people if they had a 497 00:28:00,720 --> 00:28:03,320 Speaker 5: full battery versus if they had a really low battery, 498 00:28:03,440 --> 00:28:06,160 Speaker 5: and the person with a really low battery was often 499 00:28:06,160 --> 00:28:09,200 Speaker 5: getting a much higher fare because Uber sort of knew 500 00:28:09,240 --> 00:28:11,680 Speaker 5: that they had more urgency and sort of getting to 501 00:28:11,720 --> 00:28:14,000 Speaker 5: the location before their phone died. So these are the 502 00:28:14,080 --> 00:28:16,479 Speaker 5: sorts of things that the SEC is looking at, Like, 503 00:28:16,600 --> 00:28:20,720 Speaker 5: how are companies determining the prices when they're when they're 504 00:28:20,720 --> 00:28:24,520 Speaker 5: doing these sorts of surge pricing or dynamic pricing options. 505 00:28:24,760 --> 00:28:27,560 Speaker 1: An app like that knows the strength of your battery. 506 00:28:27,960 --> 00:28:30,679 Speaker 5: Yeah, in terms of your phone, like there's actually a 507 00:28:30,720 --> 00:28:33,000 Speaker 5: lot of when you open an app, it knows a 508 00:28:33,000 --> 00:28:35,520 Speaker 5: lot of information about your phone. It knows you know, 509 00:28:35,560 --> 00:28:37,800 Speaker 5: what your battery level is. It might know some of 510 00:28:37,840 --> 00:28:40,800 Speaker 5: your contacts. They might know some other apps that you've 511 00:28:40,800 --> 00:28:43,560 Speaker 5: gone to on the phone. So for example, if you 512 00:28:44,080 --> 00:28:46,000 Speaker 5: open up the Uber app and then you open up 513 00:28:46,000 --> 00:28:49,640 Speaker 5: the Lift app, sometimes it might, you know, change the 514 00:28:49,680 --> 00:28:51,960 Speaker 5: price that's available because it knows that you might be 515 00:28:52,080 --> 00:28:55,040 Speaker 5: like checking between the two, sort of triaging the price. 516 00:28:55,480 --> 00:28:57,600 Speaker 1: So I do that all the time. 517 00:28:57,800 --> 00:28:59,880 Speaker 5: Yeah, I don't know if you've ever if you've ever 518 00:29:00,200 --> 00:29:02,480 Speaker 5: the food delivery app. This happens to me all the time. 519 00:29:02,520 --> 00:29:04,400 Speaker 5: Like if I open one of the food delivery apps, 520 00:29:04,400 --> 00:29:06,080 Speaker 5: all of a sudden, I start getting pop ups from 521 00:29:06,120 --> 00:29:09,000 Speaker 5: the other ones being like hungry, try this offer. 522 00:29:09,360 --> 00:29:12,120 Speaker 1: We're laughing, but it's scary that so much of your 523 00:29:12,120 --> 00:29:15,600 Speaker 1: personal information is exposed by an app. 524 00:29:15,920 --> 00:29:18,640 Speaker 5: Yeah, and that's part of what the FEC is trying 525 00:29:18,680 --> 00:29:20,239 Speaker 5: to look at here, they said, you know, they're not 526 00:29:20,320 --> 00:29:23,920 Speaker 5: necessarily implying that companies have done anything wrong, but they 527 00:29:23,920 --> 00:29:27,400 Speaker 5: do want to know how often companies are using these 528 00:29:27,440 --> 00:29:31,000 Speaker 5: types of information, what information is sort of going into 529 00:29:31,040 --> 00:29:34,880 Speaker 5: these types of algorithms that are determining price. A lot 530 00:29:34,880 --> 00:29:37,120 Speaker 5: of the companies that they sent these inquiries to, some 531 00:29:37,200 --> 00:29:39,760 Speaker 5: of them you've heard of like MasterCard and Jason Morgan, 532 00:29:40,040 --> 00:29:42,040 Speaker 5: but a lot of them are probably companies. You never 533 00:29:42,080 --> 00:29:46,239 Speaker 5: heard of something called Reveonics, Task Group Pros, but they 534 00:29:46,280 --> 00:29:49,400 Speaker 5: actually work for a lot of very household names. Taskworks 535 00:29:49,400 --> 00:29:54,080 Speaker 5: for Starbucks and McDonald's. Pros works for Luftanza the airline, 536 00:29:54,240 --> 00:29:57,840 Speaker 5: and Neshleie Revionics works for home Depot. Like all sorts 537 00:29:57,880 --> 00:30:00,400 Speaker 5: of companies are doing this more and more a sort 538 00:30:00,440 --> 00:30:03,800 Speaker 5: of trying to personalize pricing. You know, maybe you've gone 539 00:30:03,800 --> 00:30:05,520 Speaker 5: to Home Depot and they know that you're working on 540 00:30:05,560 --> 00:30:08,760 Speaker 5: some home projects, so like the next time you come in, 541 00:30:08,880 --> 00:30:11,479 Speaker 5: maybe they'll try and offer you a discount on you know, 542 00:30:11,480 --> 00:30:13,680 Speaker 5: whatever it is that you're working on, or maybe they 543 00:30:13,720 --> 00:30:16,040 Speaker 5: know that you're much more likely to like really need that, 544 00:30:16,120 --> 00:30:19,240 Speaker 5: so they might hike the price on something. It's very interesting, 545 00:30:19,240 --> 00:30:22,080 Speaker 5: we're in this new world sort of with pricing because 546 00:30:22,080 --> 00:30:24,960 Speaker 5: they now know a lot more about you and maybe 547 00:30:25,000 --> 00:30:26,320 Speaker 5: what you're more willing to pay for. 548 00:30:26,920 --> 00:30:30,000 Speaker 1: So they send subpoenas. What kind of information are they 549 00:30:30,040 --> 00:30:30,760 Speaker 1: asking for? 550 00:30:31,600 --> 00:30:34,280 Speaker 5: Yes, this is an FTP study, so they are allowed 551 00:30:34,280 --> 00:30:37,240 Speaker 5: to compel the companies to provide information, but they ask 552 00:30:37,320 --> 00:30:39,920 Speaker 5: them for a lot of details about one the services 553 00:30:39,920 --> 00:30:42,560 Speaker 5: that they offer with software. So some of these companies 554 00:30:42,600 --> 00:30:45,840 Speaker 5: call it like price optimization or search pricing. They have 555 00:30:45,920 --> 00:30:47,720 Speaker 5: a lot of different terms for what they do. So 556 00:30:47,760 --> 00:30:51,239 Speaker 5: they're asking them what specifically do you offer and how 557 00:30:51,240 --> 00:30:53,680 Speaker 5: would you describe it? And then what sort of information 558 00:30:53,800 --> 00:30:56,720 Speaker 5: goes into that sort of algorithm because often this is 559 00:30:56,760 --> 00:30:59,320 Speaker 5: done by computer algorithms, So they want to know what 560 00:30:59,360 --> 00:31:01,520 Speaker 5: are the inputs into that, so that they would want 561 00:31:01,520 --> 00:31:03,520 Speaker 5: to know, for example, are they looking at the phone 562 00:31:03,560 --> 00:31:06,120 Speaker 5: battery to determine this, Are they looking at you know, 563 00:31:06,280 --> 00:31:08,680 Speaker 5: your contacts and what other things you've looked at? And then, 564 00:31:09,000 --> 00:31:11,360 Speaker 5: as I said, are they looking at your browsing history? 565 00:31:11,960 --> 00:31:15,400 Speaker 5: And then they wanted to know how they choose which 566 00:31:15,440 --> 00:31:19,720 Speaker 5: offers because they do have some concerns that maybe companies 567 00:31:19,960 --> 00:31:23,280 Speaker 5: are sort of targeting richer consumers to give lots of 568 00:31:23,440 --> 00:31:26,040 Speaker 5: price hikes and not giving them to, you know, more 569 00:31:26,120 --> 00:31:30,000 Speaker 5: low income consumers, which might be potentially discriminatory. Whenever you 570 00:31:30,000 --> 00:31:32,920 Speaker 5: have your phone, it has some information about where you're located, 571 00:31:33,040 --> 00:31:36,920 Speaker 5: and geography could be going into this. Perhaps if you're 572 00:31:37,200 --> 00:31:40,080 Speaker 5: located in a lower income area, you're not as likely 573 00:31:40,160 --> 00:31:41,920 Speaker 5: to get some of these offers as you are if 574 00:31:41,960 --> 00:31:44,320 Speaker 5: you live in a rich suburb, So they really want 575 00:31:44,360 --> 00:31:46,680 Speaker 5: to know all of the inputs that are going into 576 00:31:46,680 --> 00:31:49,880 Speaker 5: how they decide what the offers are and who gets them. 577 00:31:50,480 --> 00:31:52,320 Speaker 1: You know, we think of the FTC, I think most 578 00:31:52,360 --> 00:31:55,440 Speaker 1: of us in a different way. They're doing this under 579 00:31:55,560 --> 00:31:57,360 Speaker 1: the so called six B Authority. 580 00:31:57,880 --> 00:32:00,680 Speaker 5: Yeah, so the FDC is an enforcement agency, but it 581 00:32:00,720 --> 00:32:03,920 Speaker 5: also has this really interesting research arm. Congress gave it 582 00:32:03,960 --> 00:32:06,560 Speaker 5: the ability to do these six PE studies. They're sort 583 00:32:06,600 --> 00:32:09,840 Speaker 5: of like market studies and they're allowed to compel information 584 00:32:09,880 --> 00:32:12,680 Speaker 5: from companies to sort of do a deep dive into 585 00:32:12,720 --> 00:32:16,120 Speaker 5: specific areas of the economy or areas of the market. 586 00:32:16,320 --> 00:32:18,640 Speaker 5: So they've done some of these on all sorts of things. 587 00:32:18,640 --> 00:32:21,480 Speaker 5: They've done them on pharmaceutical benefit managers, which are sort 588 00:32:21,480 --> 00:32:24,920 Speaker 5: of the drug middlemen who come up with pharmaceutical pricing 589 00:32:25,000 --> 00:32:28,160 Speaker 5: between the pharma companies and the health insurance. They've done 590 00:32:28,200 --> 00:32:33,240 Speaker 5: them on patentrols and like how much those were impacting innovation. 591 00:32:33,760 --> 00:32:40,080 Speaker 5: They've been doing ones on pricing of online Internet pricing. 592 00:32:40,360 --> 00:32:42,360 Speaker 5: That was one that came up sort of during the pandemic. 593 00:32:42,400 --> 00:32:44,760 Speaker 5: And they did one on supply chains, like what caused 594 00:32:44,840 --> 00:32:47,480 Speaker 5: some of the supply chain crises of the pandemic. So 595 00:32:47,680 --> 00:32:50,640 Speaker 5: the idea behind these studies is that, like the SPC 596 00:32:50,760 --> 00:32:53,920 Speaker 5: really does a deep dive into what is really happening, 597 00:32:53,960 --> 00:32:57,280 Speaker 5: and then they publish a study with the information and 598 00:32:57,320 --> 00:33:00,720 Speaker 5: that can help Congress as it is considering new laws, 599 00:33:00,840 --> 00:33:03,840 Speaker 5: or if the STC finds something that's a little bit questionable, 600 00:33:03,880 --> 00:33:06,920 Speaker 5: it can lead them to open an investigation into something 601 00:33:06,920 --> 00:33:07,560 Speaker 5: related to it. 602 00:33:07,800 --> 00:33:09,520 Speaker 1: How long do these studies usually take? 603 00:33:09,720 --> 00:33:11,440 Speaker 5: These studies do you tend to take a little bit 604 00:33:11,480 --> 00:33:14,000 Speaker 5: of time. Oftentimes when they start them, it takes like 605 00:33:14,040 --> 00:33:16,160 Speaker 5: maybe six months or so for the companies to send 606 00:33:16,160 --> 00:33:18,320 Speaker 5: them the information and then usually they spend a little 607 00:33:18,320 --> 00:33:20,960 Speaker 5: bit of time looking at it. So it's not unusual 608 00:33:21,080 --> 00:33:23,160 Speaker 5: for it to be a year or two before the 609 00:33:23,280 --> 00:33:25,200 Speaker 5: SPC publishes its findings. 610 00:33:25,520 --> 00:33:29,360 Speaker 1: Well, this has really opened my eyes about being tracked 611 00:33:29,400 --> 00:33:32,760 Speaker 1: by apps on your phone. Thanks so much, Leah. That's 612 00:33:32,800 --> 00:33:36,280 Speaker 1: Bloomberg Anti Trust reporter Leah Nylan And that's it for 613 00:33:36,280 --> 00:33:39,320 Speaker 1: this edition of the Bloomberg Law Podcast. Remember you can 614 00:33:39,360 --> 00:33:42,280 Speaker 1: always get the latest legal news by subscribing and listening 615 00:33:42,320 --> 00:33:46,040 Speaker 1: to the show on Apple Podcasts, Spotify, and at Bloomberg 616 00:33:46,080 --> 00:33:50,120 Speaker 1: dot com, Slash Podcast, Slash Law. I'm June Grosso and 617 00:33:50,280 --> 00:33:51,520 Speaker 1: this is Bloomberg.