1 99:59:59,999 --> 99:59:59,999 Some years ago, 2 99:59:59,999 --> 99:59:59,999 I was on an airplane with my son who was just five years old at the time. 3 99:59:59,999 --> 99:59:59,999 My son was so excited about being on this airplane with mommy. 4 99:59:59,999 --> 99:59:59,999 He's looking all around and he's checking things out 5 99:59:59,999 --> 99:59:59,999 and he's checking people out, 6 99:59:59,999 --> 99:59:59,999 and he sees this man, and he says, "Hey, 7 99:59:59,999 --> 99:59:59,999 that guy looks like Daddy." 8 99:59:59,999 --> 99:59:59,999 And I look at the man, 9 99:59:59,999 --> 99:59:59,999 and he didn't look anything at all like my husband, 10 99:59:59,999 --> 99:59:59,999 nothing at all. 11 99:59:59,999 --> 99:59:59,999 And so then I start looking around on the plane, 12 99:59:59,999 --> 99:59:59,999 and I notice this man 13 99:59:59,999 --> 99:59:59,999 was the only black guy on the plane. 14 99:59:59,999 --> 99:59:59,999 And I thought, 15 99:59:59,999 --> 99:59:59,999 all right. 16 99:59:59,999 --> 99:59:59,999 I'm going to have to have a little talk with my son 17 99:59:59,999 --> 99:59:59,999 about how not all black people look alike. 18 99:59:59,999 --> 99:59:59,999 My son, he lifts his head up, and he says to me, 19 99:59:59,999 --> 99:59:59,999 "I hope he doesn't rob the plane." 20 99:59:59,999 --> 99:59:59,999 And I said, "What? What did you say?" 21 99:59:59,999 --> 99:59:59,999 And he says, "Well, I hope that man doesn't rob the plane." 22 99:59:59,999 --> 99:59:59,999 And I said, "Well, why would you say that? 23 99:59:59,999 --> 99:59:59,999 You know Daddy wouldn't rob a plane." 24 99:59:59,999 --> 99:59:59,999 And he says, "Yeah, yeah, yeah, well, I know." 25 99:59:59,999 --> 99:59:59,999 And I said, "Well, why would you say that?" 26 99:59:59,999 --> 99:59:59,999 And he looked at me with this really sad face, 27 99:59:59,999 --> 99:59:59,999 and he says, 28 99:59:59,999 --> 99:59:59,999 "I don't know why I said that. 29 99:59:59,999 --> 99:59:59,999 I don't know why I was thinking that." 30 99:59:59,999 --> 99:59:59,999 We are living with such severe racial stratification 31 99:59:59,999 --> 99:59:59,999 that even a five-year old 32 99:59:59,999 --> 99:59:59,999 can tell us what's supposed to happen next, 33 99:59:59,999 --> 99:59:59,999 even with no evildoer, 34 99:59:59,999 --> 99:59:59,999 even with no explicit hatred. 35 99:59:59,999 --> 99:59:59,999 This association between blackness and crime 36 99:59:59,999 --> 99:59:59,999 made its way into the mind of my five-year old. 37 99:59:59,999 --> 99:59:59,999 It makes its way into all of our children, 38 99:59:59,999 --> 99:59:59,999 into all of us. 39 99:59:59,999 --> 99:59:59,999 Our minds are shaped by the racial disparities 40 99:59:59,999 --> 99:59:59,999 we see out in the world, 41 99:59:59,999 --> 99:59:59,999 and the narratives that help us to make sense of the disparities we see. 42 99:59:59,999 --> 99:59:59,999 Those people are criminals. 43 99:59:59,999 --> 99:59:59,999 Those people are violent. 44 99:59:59,999 --> 99:59:59,999 Those people are to be feared. 45 99:59:59,999 --> 99:59:59,999 When my research team brought people into our lab 46 99:59:59,999 --> 99:59:59,999 and exposed them to faces, 47 99:59:59,999 --> 99:59:59,999 we found that exposure to black faces 48 99:59:59,999 --> 99:59:59,999 led them to see blurry images of guns with greater clarity and speed. 49 99:59:59,999 --> 99:59:59,999 Bias cannot only control what we see, 50 99:59:59,999 --> 99:59:59,999 but where we look. 51 99:59:59,999 --> 99:59:59,999 We found that prompting people to think of violent crime 52 99:59:59,999 --> 99:59:59,999 can lead them to direct their eyes onto a black face 53 99:59:59,999 --> 99:59:59,999 and away from a white face. 54 99:59:59,999 --> 99:59:59,999 Prompting police officers to think of capturing and shooting 55 99:59:59,999 --> 99:59:59,999 and arresting 56 99:59:59,999 --> 99:59:59,999 leads their eyes to settle on black faces too. 57 99:59:59,999 --> 99:59:59,999 Bias can infect every aspect of our criminal justice system. 58 99:59:59,999 --> 99:59:59,999 In a large dataset of death-eligible defendants, 59 99:59:59,999 --> 99:59:59,999 we found that looking more black 60 99:59:59,999 --> 99:59:59,999 more than doubled their chances of receiving a death sentence, 61 99:59:59,999 --> 99:59:59,999 at least when their victims were white. 62 99:59:59,999 --> 99:59:59,999 This effect is significant even though we controlled 63 99:59:59,999 --> 99:59:59,999 for the severity of the crime 64 99:59:59,999 --> 99:59:59,999 and the defendant's attractiveness, 65 99:59:59,999 --> 99:59:59,999 and no matter what we controlled for, 66 99:59:59,999 --> 99:59:59,999 we found that black people 67 99:59:59,999 --> 99:59:59,999 were punished in proportion 68 99:59:59,999 --> 99:59:59,999 to the blackness of their physical features: 69 99:59:59,999 --> 99:59:59,999 the more black, 70 99:59:59,999 --> 99:59:59,999 the more death-worthy. 71 99:59:59,999 --> 99:59:59,999 Bias can also influence how teachers discipline students. 72 99:59:59,999 --> 99:59:59,999 My colleagues and I 73 99:59:59,999 --> 99:59:59,999 have found that teachers express a desire 74 99:59:59,999 --> 99:59:59,999 to discipline a black middle school student 75 99:59:59,999 --> 99:59:59,999 more harshly than a white student for the same repeated infractions. 76 99:59:59,999 --> 99:59:59,999 In a recent study, 77 99:59:59,999 --> 99:59:59,999 we're finding that teachers treat black students as a group 78 99:59:59,999 --> 99:59:59,999 but white students as individuals. 79 99:59:59,999 --> 99:59:59,999 If, for example, one black student misbehaves 80 99:59:59,999 --> 99:59:59,999 and then a different black student misbehaves a few days later, 81 99:59:59,999 --> 99:59:59,999 the teacher responds to that second black student 82 99:59:59,999 --> 99:59:59,999 as if he had misbehaved twice. 83 99:59:59,999 --> 99:59:59,999 It's as though the sins of one child 84 99:59:59,999 --> 99:59:59,999 get piled on to the other. 85 99:59:59,999 --> 99:59:59,999 We create categories to make sense of the world, 86 99:59:59,999 --> 99:59:59,999 to assert some control and coherence 87 99:59:59,999 --> 99:59:59,999 to the stimuli that we're constantly being bombarded with. 88 99:59:59,999 --> 99:59:59,999 Categorization and the bias that it seeds 89 99:59:59,999 --> 99:59:59,999 allow our brains to make judgments 90 99:59:59,999 --> 99:59:59,999 more quickly and efficiently, 91 99:59:59,999 --> 99:59:59,999 and we do this by instinctively relying on patterns 92 99:59:59,999 --> 99:59:59,999 that seem predictable. 93 99:59:59,999 --> 99:59:59,999 Yet just as the categories we create 94 99:59:59,999 --> 99:59:59,999 allow us to make quick decisions, 95 99:59:59,999 --> 99:59:59,999 they also reinforce bias. 96 99:59:59,999 --> 99:59:59,999 So the very things that help us to see the world 97 99:59:59,999 --> 99:59:59,999 also can blind us to it. 98 99:59:59,999 --> 99:59:59,999 They render our choices effortless, 99 99:59:59,999 --> 99:59:59,999 friction-free. 100 99:59:59,999 --> 99:59:59,999 Yet the exact a heavy toll. 101 99:59:59,999 --> 99:59:59,999 So what can we do? 102 99:59:59,999 --> 99:59:59,999 We are all vulnerable to bias, 103 99:59:59,999 --> 99:59:59,999 but we don't act on bias all the time. 104 99:59:59,999 --> 99:59:59,999 There are certain conditions that can bring bias alive 105 99:59:59,999 --> 99:59:59,999 and other conditions that can muffle it. 106 99:59:59,999 --> 99:59:59,999 Let me give you an example. 107 99:59:59,999 --> 99:59:59,999 Many people are familiar with the tech company Nextdoor. 108 99:59:59,999 --> 99:59:59,999 So their whole purpose is to create stronger, healthier, safer neighborhoods. 109 99:59:59,999 --> 99:59:59,999 And so they offer this online space 110 99:59:59,999 --> 99:59:59,999 where neighbors can gather and share information. 111 99:59:59,999 --> 99:59:59,999 Yet Nextdoor soon found that they had a problem 112 99:59:59,999 --> 99:59:59,999 with racial profiling. 113 99:59:59,999 --> 99:59:59,999 In the typical case, 114 99:59:59,999 --> 99:59:59,999 people would look outside their window 115 99:59:59,999 --> 99:59:59,999 and see a black man in their otherwise white neighborhood 116 99:59:59,999 --> 99:59:59,999 and make the snap judgment 117 99:59:59,999 --> 99:59:59,999 that he was up to no good, 118 99:59:59,999 --> 99:59:59,999 even when there was no evidence of criminal wrongdoing. 119 99:59:59,999 --> 99:59:59,999 In many ways, how we behave online 120 99:59:59,999 --> 99:59:59,999 is a reflection of how we behave in the world. 121 99:59:59,999 --> 99:59:59,999 But what we don't want to do is create an easy-to-use system 122 99:59:59,999 --> 99:59:59,999 that can amplify bias 123 99:59:59,999 --> 99:59:59,999 and deepen racial disparities, 124 99:59:59,999 --> 99:59:59,999 rather than dismantling them. 125 99:59:59,999 --> 99:59:59,999 So the co-founder of Nextdoor reached out to me and to others 126 99:59:59,999 --> 99:59:59,999 to try to figure out what to do, 127 99:59:59,999 --> 99:59:59,999 and they realized that to curb racial profiling on the platform, 128 99:59:59,999 --> 99:59:59,999 they were going to have to add friction. 129 99:59:59,999 --> 99:59:59,999 That is, they were going to have to slow people done. 130 99:59:59,999 --> 99:59:59,999 So Nextdoor had a choice to make, 131 99:59:59,999 --> 99:59:59,999 and against every impulse 132 99:59:59,999 --> 99:59:59,999 they decided to add friction. 133 99:59:59,999 --> 99:59:59,999 And they did this by adding a simple checklist. 134 99:59:59,999 --> 99:59:59,999 There were three items on it. 135 99:59:59,999 --> 99:59:59,999 First, they asked users to pause 136 99:59:59,999 --> 99:59:59,999 and think, what was this person doing 137 99:59:59,999 --> 99:59:59,999 that made him suspicious? 138 99:59:59,999 --> 99:59:59,999 The category "black man" is not ground for suspicion. 139 99:59:59,999 --> 99:59:59,999 Second, they asked users to describe the person's physical features, 140 99:59:59,999 --> 99:59:59,999 not simply their race and gender. 141 99:59:59,999 --> 99:59:59,999 Third, they realized that a lot of people 142 99:59:59,999 --> 99:59:59,999 didn't seem to know what racial profiling was, 143 99:59:59,999 --> 99:59:59,999 nor that they were engaging in it. 144 99:59:59,999 --> 99:59:59,999 So Nextdoor provided them with a definition 145 99:59:59,999 --> 99:59:59,999 and told them that it was strictly prohibited. 146 99:59:59,999 --> 99:59:59,999 Most of you have seen those signs in airports 147 99:59:59,999 --> 99:59:59,999 and Metro stations, "If you see something, say something." 148 99:59:59,999 --> 99:59:59,999 Nextdoor tried modifying this. 149 99:59:59,999 --> 99:59:59,999 "If you see something suspicious, say something specific." 150 99:59:59,999 --> 99:59:59,999 And using this strategy 151 99:59:59,999 --> 99:59:59,999 by simply slowing people down, 152 99:59:59,999 --> 99:59:59,999 Nextdoor was able to curb racial profiling by 75 percent. 153 99:59:59,999 --> 99:59:59,999 Now, people often will say to me, 154 99:59:59,999 --> 99:59:59,999 you can't add friction in every situation, in every context, 155 99:59:59,999 --> 99:59:59,999 and especially for people who make split second decisions all the time, 156 99:59:59,999 --> 99:59:59,999 but it turns out we can add frictions 157 99:59:59,999 --> 99:59:59,999 to more situations than we think. 158 99:59:59,999 --> 99:59:59,999 Working with the Oakland Police Department 159 99:59:59,999 --> 99:59:59,999 in California, 160 99:59:59,999 --> 99:59:59,999 I and a number of my colleagues were able to help the Department 161 99:59:59,999 --> 99:59:59,999 to reduce the number of stops they made 162 99:59:59,999 --> 99:59:59,999 of people who were not committing any serious crimes, 163 99:59:59,999 --> 99:59:59,999 and we did this by pushing officers 164 99:59:59,999 --> 99:59:59,999 to ask themselves a question 165 99:59:59,999 --> 99:59:59,999 before each and every stop they made: 166 99:59:59,999 --> 99:59:59,999 is this stop intelligence-led, 167 99:59:59,999 --> 99:59:59,999 yes or no? 168 99:59:59,999 --> 99:59:59,999 In other words, 169 99:59:59,999 --> 99:59:59,999 do I have prior information to tie this particular person 170 99:59:59,999 --> 99:59:59,999 to a specific crime? 171 99:59:59,999 --> 99:59:59,999 By adding that question 172 99:59:59,999 --> 99:59:59,999 to the form officer's complete doing the stop, 173 99:59:59,999 --> 99:59:59,999 they slow down, they pause, 174 99:59:59,999 --> 99:59:59,999 they think, why am I considering pulling this person over? 175 99:59:59,999 --> 99:59:59,999 In 2017, before we added that intelligence-led question to the form, 176 99:59:59,999 --> 99:59:59,999 officers made about 32,000 stops across the city. 177 99:59:59,999 --> 99:59:59,999 In that next year, 178 99:59:59,999 --> 99:59:59,999 with the addition of this question, 179 99:59:59,999 --> 99:59:59,999 that fell to 19,000 stops. 180 99:59:59,999 --> 99:59:59,999 African-American stops alone fell by 43 percent. 181 99:59:59,999 --> 99:59:59,999 And stopping fewer black people did not make the city any more dangerous. 182 99:59:59,999 --> 99:59:59,999 In fact, the crime rate continued to fall 183 99:59:59,999 --> 99:59:59,999 and the city became safer for everybody. 184 99:59:59,999 --> 99:59:59,999 So one solution can come from reducing the number of unnecessary stops. 185 99:59:59,999 --> 99:59:59,999 Another can come from improving the quality of the stops 186 99:59:59,999 --> 99:59:59,999 officers do make. 187 99:59:59,999 --> 99:59:59,999 And technology can help us here. 188 99:59:59,999 --> 99:59:59,999 We all know about George Floyd's death 189 99:59:59,999 --> 99:59:59,999 because those who tried to come to his aid 190 99:59:59,999 --> 99:59:59,999 held cell phone cameras to record that horrific, fatal encounter 191 99:59:59,999 --> 99:59:59,999 with the police. 192 99:59:59,999 --> 99:59:59,999 But we have all sorts of technology 193 99:59:59,999 --> 99:59:59,999 that we're not putting to good use. 194 99:59:59,999 --> 99:59:59,999 Police departments across the country 195 99:59:59,999 --> 99:59:59,999 are now required to wear body-worn cameras, 196 99:59:59,999 --> 99:59:59,999 so we have recordings of not only the most extreme and horrific encounters 197 99:59:59,999 --> 99:59:59,999 but of everyday interactions. 198 99:59:59,999 --> 99:59:59,999 With an interdisciplinary team at Stanford, 199 99:59:59,999 --> 99:59:59,999 we've begun to use machine learning techniques 200 99:59:59,999 --> 99:59:59,999 to analyze large numbers of encounters. 201 99:59:59,999 --> 99:59:59,999 This is to better understand what happens in routine traffic stops. 202 99:59:59,999 --> 99:59:59,999 What we found was that even when police officers 203 99:59:59,999 --> 99:59:59,999 are behaving professionally, 204 99:59:59,999 --> 99:59:59,999 they speak to black drivers less respectfully than white drivers. 205 99:59:59,999 --> 99:59:59,999 In fact, from the words officers use alone 206 99:59:59,999 --> 99:59:59,999 we could predict whether they were talking to a black driver or a white driver. 207 99:59:59,999 --> 99:59:59,999 The problem is that the vast majority of the footage from these cameras 208 99:59:59,999 --> 99:59:59,999 is not used by police departments 209 99:59:59,999 --> 99:59:59,999 to understand what's going on on the street 210 99:59:59,999 --> 99:59:59,999 or to train officers. 211 99:59:59,999 --> 99:59:59,999 And that's a shame. 212 99:59:59,999 --> 99:59:59,999 How does a routine stop 213 99:59:59,999 --> 99:59:59,999 turn into a deadly encounter? 214 99:59:59,999 --> 99:59:59,999 How did this happen in George Floyd's case? 215 99:59:59,999 --> 99:59:59,999 How did it happen in others? 216 99:59:59,999 --> 99:59:59,999 When my eldest son was 16 years old, 217 99:59:59,999 --> 99:59:59,999 he discovered that when white people look at him, 218 99:59:59,999 --> 99:59:59,999 they feel fear. 219 99:59:59,999 --> 99:59:59,999 Elevators are the worst, he said. 220 99:59:59,999 --> 99:59:59,999 When those doors close, 221 99:59:59,999 --> 99:59:59,999 people are trapped in this tiny space 222 99:59:59,999 --> 99:59:59,999 with someone they have been taught 223 99:59:59,999 --> 99:59:59,999 to associate with danger. 224 99:59:59,999 --> 99:59:59,999 My son senses their discomfort, 225 99:59:59,999 --> 99:59:59,999 and he smiles to put them at ease, 226 99:59:59,999 --> 99:59:59,999 to calm their fears. 227 99:59:59,999 --> 99:59:59,999 When he speaks, 228 99:59:59,999 --> 99:59:59,999 their bodies relax. 229 99:59:59,999 --> 99:59:59,999 They breathe easier. 230 99:59:59,999 --> 99:59:59,999 They take pleasure in his cadence, 231 99:59:59,999 --> 99:59:59,999 his diction, his word choice. 232 99:59:59,999 --> 99:59:59,999 He sounds like one of them. 233 99:59:59,999 --> 99:59:59,999 I used to think that my son was a natural extrovert like his father, 234 99:59:59,999 --> 99:59:59,999 but I realized at that moment in that conversation 235 99:59:59,999 --> 99:59:59,999 that his smile was not a sign 236 99:59:59,999 --> 99:59:59,999 that he wanted to connect with would-be strangers. 237 99:59:59,999 --> 99:59:59,999 It was a talisman he's used to protect himself, 238 99:59:59,999 --> 99:59:59,999 a survival skill he had honed over thousands of elevator rides. 239 99:59:59,999 --> 99:59:59,999 He was learning to accommodate the tension 240 99:59:59,999 --> 99:59:59,999 that his skin color generated 241 99:59:59,999 --> 99:59:59,999 and that put his own life at risk. 242 99:59:59,999 --> 99:59:59,999 We know that the brain is wired for bias, 243 99:59:59,999 --> 99:59:59,999 and one way to interrupt that bias 244 99:59:59,999 --> 99:59:59,999 is to pause and to reflect on the evidence of our assumptions, 245 99:59:59,999 --> 99:59:59,999 so we need to ask ourselves 246 99:59:59,999 --> 99:59:59,999 what assumptions do we bring 247 99:59:59,999 --> 99:59:59,999 when we step onto an elevator? 248 99:59:59,999 --> 99:59:59,999 Or an airplane? 249 99:59:59,999 --> 99:59:59,999 How do we make ourselves aware of our own unconscious bias? 250 99:59:59,999 --> 99:59:59,999 Who do those assumptions keep safe? 251 99:59:59,999 --> 99:59:59,999 Who do they put at risk? 252 99:59:59,999 --> 99:59:59,999 Until we ask these questions, 253 99:59:59,999 --> 99:59:59,999 and insist that our schools and our courts and our police departments 254 99:59:59,999 --> 99:59:59,999 and every institution do the same, 255 99:59:59,999 --> 99:59:59,999 we will continue to allow bias 256 99:59:59,999 --> 99:59:59,999 to blind us, 257 99:59:59,999 --> 99:59:59,999 and if we do, 258 99:59:59,999 --> 99:59:59,999 none of us are truly safe. 259 99:59:59,999 --> 99:59:59,999 Thank you.