1 00:00:00,000 --> 00:00:01,208 ROLAND FRYER: Good afternoon. 2 00:00:01,208 --> 00:00:03,758 What I'm most proud of actually is that at my advanced age, 3 00:00:03,758 --> 00:00:06,716 I still spend two hours a morning working out with the Harvard football 4 00:00:06,716 --> 00:00:10,478 team, so that's the most proud I am. 5 00:00:10,478 --> 00:00:14,212 How many of you actually want to change the world? 6 00:00:14,212 --> 00:00:15,628 It's OK, you can raise your hands. 7 00:00:15,628 --> 00:00:16,919 You don't have to be like this. 8 00:00:16,919 --> 00:00:19,768 OK, how are you going to change the world? 9 00:00:19,768 --> 00:00:20,268 You. 10 00:00:20,268 --> 00:00:24,588 11 00:00:24,588 --> 00:00:26,458 Well, but that was rude, you're right. 12 00:00:26,458 --> 00:00:28,628 I'm Roland, how are you going to change the world? 13 00:00:28,628 --> 00:00:31,502 14 00:00:31,502 --> 00:00:33,418 AUDIENCE: I'm Hassad [INAUDIBLE]. 15 00:00:33,418 --> 00:00:37,737 I want to change the world by being an attorney. 16 00:00:37,737 --> 00:00:38,808 ROLAND FRYER: Awesome. 17 00:00:38,808 --> 00:00:41,224 You guys went simultaneously on the hand, so I'll go both. 18 00:00:41,224 --> 00:00:42,462 What about you? 19 00:00:42,462 --> 00:00:45,088 AUDIENCE: I want to be an astronaut and learn more about space. 20 00:00:45,088 --> 00:00:47,212 ROLAND FRYER: Astronaut and learn more about space. 21 00:00:47,212 --> 00:00:50,318 Sorry, the lights are super in my face, so I can't really see upstairs. 22 00:00:50,318 --> 00:00:54,048 But anyone upstairs want to change the world? 23 00:00:54,048 --> 00:00:55,907 There we go. 24 00:00:55,907 --> 00:00:58,866 AUDIENCE: I want to build robots that save people in natural disasters. 25 00:00:58,866 --> 00:01:00,157 ROLAND FRYER: Wow, that's cool. 26 00:01:00,157 --> 00:01:01,578 Build robots-- that's amazing. 27 00:01:01,578 --> 00:01:04,558 28 00:01:04,558 --> 00:01:09,008 A million years ago when I was your age, I also wanted to change the world. 29 00:01:09,008 --> 00:01:10,268 I wanted to go to the NFL. 30 00:01:10,268 --> 00:01:12,818 Let me rephrase that, I wanted to change my world. 31 00:01:12,818 --> 00:01:15,418 32 00:01:15,418 --> 00:01:18,078 Maybe that could have some spill-overs to other people. 33 00:01:18,078 --> 00:01:19,502 And I was interested two things. 34 00:01:19,502 --> 00:01:20,918 I wanted to wear t-shirts to work. 35 00:01:20,918 --> 00:01:22,618 I thought that would be cool. 36 00:01:22,618 --> 00:01:25,438 And I wanted to have a room full of shoes. 37 00:01:25,438 --> 00:01:27,673 I don't know what the second one is about. 38 00:01:27,673 --> 00:01:29,908 And to be clear, I've accomplished both of them, 39 00:01:29,908 --> 00:01:34,208 but I really wanted a room full of shoes. 40 00:01:34,208 --> 00:01:39,448 Now, what I do for a living is that I raise lots of private resources 41 00:01:39,448 --> 00:01:44,475 to run programs in communities across the United States. 42 00:01:44,475 --> 00:01:47,058 And I'm an economist, but I'm interested in racial inequality. 43 00:01:47,058 --> 00:01:48,827 That's like a little bit of a weird thing. 44 00:01:48,827 --> 00:01:51,118 That's like an oxymoron like jumbo shrimp or something. 45 00:01:51,118 --> 00:01:52,438 That's weird, right? 46 00:01:52,438 --> 00:01:56,728 Economists-- we're really careful and analytical, 47 00:01:56,728 --> 00:02:01,108 but caring about other people's not typically our thing. 48 00:02:01,108 --> 00:02:01,778 But what I do-- 49 00:02:01,778 --> 00:02:03,538 I love the economic method. 50 00:02:03,538 --> 00:02:05,668 I really love-- like when I was in graduate school, 51 00:02:05,668 --> 00:02:11,878 I got totally fascinated with the tools like the separating hyperplane theorem. 52 00:02:11,878 --> 00:02:15,988 I was just blown away with the beautiful mathematics of economics, 53 00:02:15,988 --> 00:02:19,228 and I thought it was being used to study problems 54 00:02:19,228 --> 00:02:24,958 that were not as interesting to me like the optimal cake eating problem. 55 00:02:24,958 --> 00:02:26,368 You see my size. 56 00:02:26,368 --> 00:02:28,064 I'm 6' 2", 215-- 57 00:02:28,064 --> 00:02:28,938 I eat the whole cake. 58 00:02:28,938 --> 00:02:31,558 59 00:02:31,558 --> 00:02:35,098 But I thought maybe these things can be used on problems that I deeply 60 00:02:35,098 --> 00:02:39,688 care about like why all the kids in my neighborhood-- not a lot of them 61 00:02:39,688 --> 00:02:44,128 went to college even though I thought they had great talent, why 62 00:02:44,128 --> 00:02:48,418 many of my friends ended up incarcerated at young ages 63 00:02:48,418 --> 00:02:52,178 even though I thought there was a lot of potential. 64 00:02:52,178 --> 00:02:55,198 And so what I do is I go around and we raise a lot of dollars. 65 00:02:55,198 --> 00:02:58,031 We've probably raised a couple hundred million dollars at this point 66 00:02:58,031 --> 00:03:00,928 over the last 10 years to run experiments. 67 00:03:00,928 --> 00:03:02,638 That's the economist in me, right? 68 00:03:02,638 --> 00:03:06,248 I don't tie my shoes without a treatment and control group. 69 00:03:06,248 --> 00:03:11,848 But to run experiments so that we can really understand what works 70 00:03:11,848 --> 00:03:18,598 and what doesn't with the idea that we want to lower inequality in America 71 00:03:18,598 --> 00:03:22,955 by expanding opportunity. 72 00:03:22,955 --> 00:03:24,788 There's a thing here somewhere, isn't there. 73 00:03:24,788 --> 00:03:26,954 So I'm going to show you a project I've been working 74 00:03:26,954 --> 00:03:30,028 on for the last year and a half. 75 00:03:30,028 --> 00:03:32,703 It hasn't made me the most popular, and that's 76 00:03:32,703 --> 00:03:35,078 what happens sometimes when you're dealing with big data. 77 00:03:35,078 --> 00:03:39,808 Sometimes, you get answers that aren't what you expected when you started. 78 00:03:39,808 --> 00:03:44,038 Like many of you, I was really and I am really 79 00:03:44,038 --> 00:03:49,258 concerned with what's going on between race and police 80 00:03:49,258 --> 00:03:51,308 in communities across America. 81 00:03:51,308 --> 00:03:53,188 We had started with-- 82 00:03:53,188 --> 00:03:55,228 I should say it started with. 83 00:03:55,228 --> 00:04:01,468 It became more salient for us with Michael Brown in Ferguson, 84 00:04:01,468 --> 00:04:07,258 Eric Garner in New York City, Laquan McDonald in Chicago, I could go on. 85 00:04:07,258 --> 00:04:10,108 And I was frustrated by this, to be honest with you. 86 00:04:10,108 --> 00:04:13,138 I was tired of looking at my TV at night and seeing 87 00:04:13,138 --> 00:04:17,638 the protests and silly debates. 88 00:04:17,638 --> 00:04:21,568 They weren't silly in their content, they were silly in their execution 89 00:04:21,568 --> 00:04:24,668 about what was actually going on. 90 00:04:24,668 --> 00:04:30,998 And as you might imagine, I'm kind of too weak to be protesting. 91 00:04:30,998 --> 00:04:34,838 I mean, it's like, hot out there, so I decided maybe what I should do-- 92 00:04:34,838 --> 00:04:37,422 93 00:04:37,422 --> 00:04:38,588 my skin's too fair for that. 94 00:04:38,588 --> 00:04:41,508 95 00:04:41,508 --> 00:04:44,498 But what I can do is go into our lab and roll up our sleeves, 96 00:04:44,498 --> 00:04:46,988 and see if we can actually make progress. 97 00:04:46,988 --> 00:04:54,008 OK, and so the key thing was like how do you make progress on something 98 00:04:54,008 --> 00:04:57,068 that is so deeply emotional? 99 00:04:57,068 --> 00:05:00,188 People on both sides of this issue are deeply emotional about it 100 00:05:00,188 --> 00:05:01,778 and rightly so. 101 00:05:01,778 --> 00:05:03,068 So here's what I did. 102 00:05:03,068 --> 00:05:05,268 I'll just tell you the full story. 103 00:05:05,268 --> 00:05:10,658 I called up Bud Riley, who turns out to be the police chief of the Harvard 104 00:05:10,658 --> 00:05:12,488 University Police. 105 00:05:12,488 --> 00:05:13,958 And I said, hey, bud. 106 00:05:13,958 --> 00:05:18,578 I'm interested in race and police, but I've got to be honest with you. 107 00:05:18,578 --> 00:05:22,268 I spent my youth running from dudes like you. 108 00:05:22,268 --> 00:05:27,578 So I don't really like you all that well, but I'd like to learn. 109 00:05:27,578 --> 00:05:29,948 And Bud said, I got just the thing for you. 110 00:05:29,948 --> 00:05:30,968 OK, true story. 111 00:05:30,968 --> 00:05:33,968 So Bud says, why don't you come over to my office tomorrow at 3 o'clock, 112 00:05:33,968 --> 00:05:35,259 and I've got something for you. 113 00:05:35,259 --> 00:05:38,948 I said, OK, Bud, but you know my grandmother always taught me not 114 00:05:38,948 --> 00:05:40,388 to just go hang out with police. 115 00:05:40,388 --> 00:05:41,387 This seems like a setup. 116 00:05:41,387 --> 00:05:45,128 117 00:05:45,128 --> 00:05:47,318 So I brought one of my white students with me, 118 00:05:47,318 --> 00:05:49,818 and I was like, come on with me-- you can film this. 119 00:05:49,818 --> 00:05:52,288 OK, that's actually not true. 120 00:05:52,288 --> 00:05:54,048 That's not true. 121 00:05:54,048 --> 00:05:56,443 That part's not true. 122 00:05:56,443 --> 00:05:58,818 If I had thought about it first, it would have been true, 123 00:05:58,818 --> 00:06:01,988 but it's not true. 124 00:06:01,988 --> 00:06:02,678 I risked it. 125 00:06:02,678 --> 00:06:05,668 I went and had coffee with Bud in the afternoon, 126 00:06:05,668 --> 00:06:07,167 and Bud took me to the simulator. 127 00:06:07,167 --> 00:06:09,458 So I went into the police simulator where they actually 128 00:06:09,458 --> 00:06:13,078 train police officers because I thought this was easy. 129 00:06:13,078 --> 00:06:15,677 Because I got this fancy European wife who's 130 00:06:15,677 --> 00:06:18,468 like, why don't they just shoot him in the leg, and I'm like, well, 131 00:06:18,468 --> 00:06:18,818 I don't know. 132 00:06:18,818 --> 00:06:20,318 Why do they shoot him in the leg? 133 00:06:20,318 --> 00:06:27,608 So I go over and he puts on the utility belt like I'm going deep in here. 134 00:06:27,608 --> 00:06:29,728 Like, now, I'm in the sociology. 135 00:06:29,728 --> 00:06:34,208 I got the utility belt on, I've got my pistol on, I've got my nightlight 136 00:06:34,208 --> 00:06:37,688 or whatever police have, I got that stuff on, 137 00:06:37,688 --> 00:06:42,428 and I'm in this kind of simulator where bad guys are jumping out. 138 00:06:42,428 --> 00:06:44,598 And you've got to decide who to shoot. 139 00:06:44,598 --> 00:06:47,087 OK, so my first interaction-- 140 00:06:47,087 --> 00:06:49,628 I only have 10 minutes, so I can't bore you with all of them. 141 00:06:49,628 --> 00:06:52,128 Bottom line is I'm the worst police officer ever imaginable. 142 00:06:52,128 --> 00:06:57,128 But the first guy comes up-- true story-- 143 00:06:57,128 --> 00:07:04,328 and it's a mid-forties, maybe 50, white gentleman, long hair. 144 00:07:04,328 --> 00:07:09,248 And I'm going to a suburban pool party because there had been complaints. 145 00:07:09,248 --> 00:07:11,888 So I get there and this person is clearly 146 00:07:11,888 --> 00:07:15,458 inebriated and is a little handsy. 147 00:07:15,458 --> 00:07:17,588 You know, he's, why are you bothering me? 148 00:07:17,588 --> 00:07:19,378 What are you doing? 149 00:07:19,378 --> 00:07:22,988 And so I get a little nervous even though I'm in the simulator. 150 00:07:22,988 --> 00:07:26,308 And the simulator has got some artificial intelligence parts to it, 151 00:07:26,308 --> 00:07:29,978 so as I talk him down using de-escalation techniques, 152 00:07:29,978 --> 00:07:33,968 this image on the screen is supposed to relax. 153 00:07:33,968 --> 00:07:39,428 So I say to him, sir, I'm just doing my patrols here. 154 00:07:39,428 --> 00:07:41,408 You've got to relax. 155 00:07:41,408 --> 00:07:43,898 He says, well, you're always bothering me. 156 00:07:43,898 --> 00:07:45,357 I said, sir-- 157 00:07:45,357 --> 00:07:46,648 I'm talking to the screen now-- 158 00:07:46,648 --> 00:07:47,588 you've got to relax. 159 00:07:47,588 --> 00:07:51,348 And he put his hands in his pocket, and I decide that's enough for me. 160 00:07:51,348 --> 00:07:54,358 So I pull my gun on the guy. 161 00:07:54,358 --> 00:07:59,254 Bud turns on the lights, Professor, what are you doing? 162 00:07:59,254 --> 00:08:00,878 I was like, isn't that what you all do? 163 00:08:00,878 --> 00:08:04,558 164 00:08:04,558 --> 00:08:07,733 Could have fooled me. 165 00:08:07,733 --> 00:08:12,038 And so we go through this training and I have to say in all seriousness, 166 00:08:12,038 --> 00:08:12,718 it was-- 167 00:08:12,718 --> 00:08:16,238 not just the training, but this whole experience 168 00:08:16,238 --> 00:08:19,628 I think I learned it was the most important lessons 169 00:08:19,628 --> 00:08:22,928 I learned in life since my grandmother taught me how to read. 170 00:08:22,928 --> 00:08:26,948 It turns out, police have a really tough job. 171 00:08:26,948 --> 00:08:32,762 And so my next thing was I learned how to shoot police issued weapons 172 00:08:32,762 --> 00:08:35,678 just because I wanted to understand, and then actually embedded myself 173 00:08:35,678 --> 00:08:38,458 in the Camden Police Department. 174 00:08:38,458 --> 00:08:40,368 Now, I know the police chief here in Boston, 175 00:08:40,368 --> 00:08:43,325 and I highly recommend anyone do a ride along with police just 176 00:08:43,325 --> 00:08:44,908 to understand their side of the story. 177 00:08:44,908 --> 00:08:47,958 I do not recommend riding along embedding yourself 178 00:08:47,958 --> 00:08:50,428 in Camden, New Jersey. 179 00:08:50,428 --> 00:08:53,938 OK, it was a crazy experience. 180 00:08:53,938 --> 00:08:56,758 We were responding to 911 calls. 181 00:08:56,758 --> 00:09:00,868 We were responding to shots fired. 182 00:09:00,868 --> 00:09:06,408 Now all of us thinkers, if there are some shots over here, 183 00:09:06,408 --> 00:09:10,668 our way to respond is to go over here, but it turns out, 184 00:09:10,668 --> 00:09:12,918 when you're in the police car and they start shooting, 185 00:09:12,918 --> 00:09:16,058 you've got to go in there. 186 00:09:16,058 --> 00:09:19,438 And it was an unbelievable experience. 187 00:09:19,438 --> 00:09:23,108 And in part because I've got to say, I'm not proud of it, 188 00:09:23,108 --> 00:09:26,788 but I would say it took five to six hours 189 00:09:26,788 --> 00:09:33,048 to get pretty desensitized because you're always seeing people in crisis. 190 00:09:33,048 --> 00:09:36,208 And that's not a part of policing that I ever thought about. 191 00:09:36,208 --> 00:09:41,848 You show up to a Wendy's and someone has threatened the cashier, 192 00:09:41,848 --> 00:09:45,658 and you show up three hours late because the Wendy's threatening thing 193 00:09:45,658 --> 00:09:48,238 was way down on your list of things to show up for. 194 00:09:48,238 --> 00:09:50,678 And you get there and you get yelled at for 30 minutes, 195 00:09:50,678 --> 00:09:53,677 and then you end up saying something nonsensical like, if they come back 196 00:09:53,677 --> 00:09:54,957 and threaten again, call us. 197 00:09:54,957 --> 00:09:56,123 Give us a three hour window. 198 00:09:56,123 --> 00:09:59,808 199 00:09:59,808 --> 00:10:03,438 It was incredible, and so I did all this because I wanted to do a few things. 200 00:10:03,438 --> 00:10:06,078 One-- I'm going to show some data as I go-- 201 00:10:06,078 --> 00:10:09,218 I wanted to learn how to talk cop. 202 00:10:09,218 --> 00:10:10,938 That was important. 203 00:10:10,938 --> 00:10:14,478 I wanted to build trust with police departments, 204 00:10:14,478 --> 00:10:20,528 and most importantly for an economist, I needed some data. 205 00:10:20,528 --> 00:10:25,258 And so what we've done is we have collected now 206 00:10:25,258 --> 00:10:30,718 in places like New York City, 6 million data observations over the last 15 207 00:10:30,718 --> 00:10:33,538 years on use of force that ranges anywhere 208 00:10:33,538 --> 00:10:38,818 from policemen putting their hands on you to hitting you with a baton. 209 00:10:38,818 --> 00:10:42,778 We've also worked with lots of cities-- 210 00:10:42,778 --> 00:10:52,498 Houston, Arlington, Austin, most counties in Florida, Boston, Camden-- 211 00:10:52,498 --> 00:10:57,698 to collect data on officer-involved shootings because it's not enough, 212 00:10:57,698 --> 00:11:00,448 and you guys know this from high school statistics. 213 00:11:00,448 --> 00:11:08,488 It's not enough to just look at numbers of people who are being shot. 214 00:11:08,488 --> 00:11:11,328 That's also awful, but that's not enough statistically. 215 00:11:11,328 --> 00:11:15,358 What you really want to understand is in a similar situation, 216 00:11:15,358 --> 00:11:20,218 does race impact the probability of a police officer pulling the trigger? 217 00:11:20,218 --> 00:11:22,378 That's a very different question. 218 00:11:22,378 --> 00:11:26,998 In that question, the burden of the data is strong because you need 219 00:11:26,998 --> 00:11:31,018 to understand all of the police shootings that could have happened 220 00:11:31,018 --> 00:11:33,378 but didn't. 221 00:11:33,378 --> 00:11:36,488 That's far more complicated. 222 00:11:36,488 --> 00:11:45,248 In fact, just in one city alone, we spent 50 minutes per observation, 223 00:11:45,248 --> 00:11:47,558 hand-typing them in the police department 224 00:11:47,558 --> 00:11:49,868 because they didn't trust us to leave with the data. 225 00:11:49,868 --> 00:11:55,578 We had to pay a sergeant $50 an hour to watch us type in the data. 226 00:11:55,578 --> 00:11:58,633 OK, and we typed in roughly 2,000 observations. 227 00:11:58,633 --> 00:12:00,393 It took us nearly a year. 228 00:12:00,393 --> 00:12:01,268 Here's what we found. 229 00:12:01,268 --> 00:12:04,598 230 00:12:04,598 --> 00:12:10,628 This is the odds ratio for blacks versus whites, so anything over one 231 00:12:10,628 --> 00:12:12,668 shows a racial difference. 232 00:12:12,668 --> 00:12:16,668 On the X-axis here, I have the amount of force used. 233 00:12:16,668 --> 00:12:21,368 Yeah, so one is hands, seven is baton. 234 00:12:21,368 --> 00:12:22,988 This is the same for Hispanics. 235 00:12:22,988 --> 00:12:25,118 On the Y-axis is the odds ratio. 236 00:12:25,118 --> 00:12:27,998 Anything over one means that Hispanics are 237 00:12:27,998 --> 00:12:33,608 more likely to have that use of force, and on the X-axis, it's the same. 238 00:12:33,608 --> 00:12:35,628 These are uses of force. 239 00:12:35,628 --> 00:12:39,138 OK, now this is important because a couple of reasons. 240 00:12:39,138 --> 00:12:42,998 One, we usually don't have data on uses of force. 241 00:12:42,998 --> 00:12:45,817 Particularly like, do you put your hands on someone? 242 00:12:45,817 --> 00:12:48,358 We even have data that I can't show you today because of time 243 00:12:48,358 --> 00:12:52,398 where I know just if the police officer shouted at you. 244 00:12:52,398 --> 00:12:57,648 OK, and so what you find here is something pretty interesting. 245 00:12:57,648 --> 00:13:02,298 When it comes to any force being used, blacks 246 00:13:02,298 --> 00:13:05,598 are 53% more likely to have any force used 247 00:13:05,598 --> 00:13:09,618 on them in any interaction with police. 248 00:13:09,618 --> 00:13:13,328 Now, this is reported by the police. 249 00:13:13,328 --> 00:13:16,265 So maybe this is an underestimate, maybe it's not, 250 00:13:16,265 --> 00:13:17,598 but it's reported by the police. 251 00:13:17,598 --> 00:13:19,389 This is not people complaining on a survey. 252 00:13:19,389 --> 00:13:23,258 This is administrative data from the police. 253 00:13:23,258 --> 00:13:27,968 As the use of force increases, the racial difference 254 00:13:27,968 --> 00:13:32,398 remains pretty constant at about 25%. 255 00:13:32,398 --> 00:13:35,718 Here's a pretty interesting fact, even when 256 00:13:35,718 --> 00:13:39,918 you look at individuals who are perfectly compliant as reported 257 00:13:39,918 --> 00:13:43,848 by the police, perfectly compliant-- 258 00:13:43,848 --> 00:13:47,478 not arrested, they don't have contraband, 259 00:13:47,478 --> 00:13:55,518 there is nothing in the record to show that any ill was done-- 260 00:13:55,518 --> 00:13:58,668 the racial differences in whether or not force was used in that interaction 261 00:13:58,668 --> 00:13:59,568 is roughly 24%. 262 00:13:59,568 --> 00:14:02,348 263 00:14:02,348 --> 00:14:07,518 So there are large racial differences between Hispanics and African-Americans 264 00:14:07,518 --> 00:14:12,608 in the US on lower level uses of force that I cannot explain with the data, 265 00:14:12,608 --> 00:14:16,388 and many, many police departments as I've talked to them about this over 266 00:14:16,388 --> 00:14:19,208 the last few months agree with this. 267 00:14:19,208 --> 00:14:20,368 They understand. 268 00:14:20,368 --> 00:14:23,558 Some police departments-- sometimes when you get into more rural areas to be 269 00:14:23,558 --> 00:14:24,433 very blunt about it-- 270 00:14:24,433 --> 00:14:27,548 271 00:14:27,548 --> 00:14:31,838 they don't want to debate with these data, but the vast majority of them 272 00:14:31,838 --> 00:14:37,438 understand that there may be an issue on the use of force. 273 00:14:37,438 --> 00:14:40,018 What's interesting, however, is that when you actually 274 00:14:40,018 --> 00:14:44,648 look at police shootings, we find no racial differences. 275 00:14:44,648 --> 00:14:48,528 We find racial differences in the numbers. 276 00:14:48,528 --> 00:14:53,388 More African-Americans are shot by police than whites, 277 00:14:53,388 --> 00:14:57,808 but not once you actually take more context into account. 278 00:14:57,808 --> 00:15:00,948 OK, so this is where we went to places like Houston and others 279 00:15:00,948 --> 00:15:06,509 and collected very, very detailed data on racial differences 280 00:15:06,509 --> 00:15:07,758 in officer-involved shootings. 281 00:15:07,758 --> 00:15:13,008 Now, here's a very important caveat, we don't have every city. 282 00:15:13,008 --> 00:15:14,088 We don't have Chicago. 283 00:15:14,088 --> 00:15:15,108 I'd love Chicago. 284 00:15:15,108 --> 00:15:16,347 We don't have Chicago. 285 00:15:16,347 --> 00:15:17,388 They won't give it to me. 286 00:15:17,388 --> 00:15:21,078 I've asked many times. 287 00:15:21,078 --> 00:15:27,198 After we released this paper, myself and a couple of the folks 288 00:15:27,198 --> 00:15:29,838 who started the Black Lives Matter movement 289 00:15:29,838 --> 00:15:33,378 met with President Obama for five hours in the White House. 290 00:15:33,378 --> 00:15:36,682 Five hours and after it was over-- 291 00:15:36,682 --> 00:15:39,848 and that's a long time to meet with the president, and frankly I had to pee. 292 00:15:39,848 --> 00:15:40,639 And what do you do? 293 00:15:40,639 --> 00:15:45,138 Like, Mr. President-- this was one of the big issues in that meeting. 294 00:15:45,138 --> 00:15:49,808 I was sitting there like, Mr. President, that's really interesting. 295 00:15:49,808 --> 00:15:53,303 Finally, I-- this is true, ask Loretta Lynch. 296 00:15:53,303 --> 00:15:55,928 I have a little handwritten note that I wrote to Loretta Lynch. 297 00:15:55,928 --> 00:15:57,368 Like, this is the first interaction you have with her, 298 00:15:57,368 --> 00:15:58,888 you want to be really good and bold and like, 299 00:15:58,888 --> 00:16:00,929 this is the thing my grandmother prepared me for. 300 00:16:00,929 --> 00:16:03,588 And my little notes says, where do I pee? 301 00:16:03,588 --> 00:16:10,105 Anyway, so we met and we debated this a lot, and I asked him-- 302 00:16:10,105 --> 00:16:12,188 I said, Mr. President, is there anything the White 303 00:16:12,188 --> 00:16:13,608 House can do to further this research? 304 00:16:13,608 --> 00:16:15,316 I said, you can get me data from Chicago. 305 00:16:15,316 --> 00:16:19,257 He said, is there anything else the White House can do? 306 00:16:19,257 --> 00:16:20,048 So we don't see it. 307 00:16:20,048 --> 00:16:23,558 We don't see it whether it's on the shootings, 308 00:16:23,558 --> 00:16:29,948 so we look at similar situations in the data and calculate the times where 309 00:16:29,948 --> 00:16:31,958 shooting could have taken place but didn't. 310 00:16:31,958 --> 00:16:33,278 We don't see it there. 311 00:16:33,278 --> 00:16:36,488 Houston has a really interesting thing because on the strong arm in Houston, 312 00:16:36,488 --> 00:16:37,448 there is a pistol. 313 00:16:37,448 --> 00:16:41,172 On the weak arm, there's a Taser, so in economics 314 00:16:41,172 --> 00:16:42,838 lingo, that's a discrete choice problem. 315 00:16:42,838 --> 00:16:44,258 You either shoot or taser. 316 00:16:44,258 --> 00:16:47,048 We actually don't see any racial differences on Tasers. 317 00:16:47,048 --> 00:16:52,258 We don't see any racial differences on any city in which we have data. 318 00:16:52,258 --> 00:16:55,198 Again, with the caveat being, they gave us the data. 319 00:16:55,198 --> 00:16:55,708 Now. 320 00:16:55,708 --> 00:16:58,008 Why is this important? 321 00:16:58,008 --> 00:17:00,828 It's important because you've heard several times, 322 00:17:00,828 --> 00:17:04,848 you have great choices, awesome. 323 00:17:04,848 --> 00:17:09,408 So do a lot of us, but the key thing that's awesome about being here-- 324 00:17:09,408 --> 00:17:12,308 I've been here for 15 years, and I will always 325 00:17:12,308 --> 00:17:15,128 stay here as long as ideas are my only constraint. 326 00:17:15,128 --> 00:17:17,498 And that's what's so wonderful about this place. 327 00:17:17,498 --> 00:17:20,708 Ideas truly are your only constraint, and so-- 328 00:17:20,708 --> 00:17:21,698 and data. 329 00:17:21,698 --> 00:17:25,258 Ideas are really your only constraint, and I really 330 00:17:25,258 --> 00:17:29,698 believe that this is the future for understanding racial inequality 331 00:17:29,698 --> 00:17:32,908 in America and around the world. 332 00:17:32,908 --> 00:17:37,738 It's using the passion that fueled our parents, 333 00:17:37,738 --> 00:17:40,618 and our grandparents, and our great grandparents, 334 00:17:40,618 --> 00:17:47,758 but using the tools of computer science, and economics, 335 00:17:47,758 --> 00:17:50,398 and artificial intelligence, and those disciplines 336 00:17:50,398 --> 00:17:55,318 who have been set silent when it comes to racial inequality in America 337 00:17:55,318 --> 00:17:56,998 to truly solve these problems. 338 00:17:56,998 --> 00:17:58,828 We actually need you. 339 00:17:58,828 --> 00:18:03,578 Your generation thinks about race differently than mine, 340 00:18:03,578 --> 00:18:08,658 and we need your insights and your mental capabilities 341 00:18:08,658 --> 00:18:15,708 to finally put this old problem to bed through data, through analysis, 342 00:18:15,708 --> 00:18:17,098 and through objective thinking. 343 00:18:17,098 --> 00:18:18,432 Thanks, enjoy your visit. 344 00:18:18,432 --> 00:18:21,396 [APPLAUSE]