9:59:59.000,9:59:59.000 Some years ago, 9:59:59.000,9:59:59.000 I was on an airplane with my son[br]who was just five years old at the time. 9:59:59.000,9:59:59.000 My son was so excited[br]about being on this airplane with mommy. 9:59:59.000,9:59:59.000 He's looking all around[br]and he's checking things out 9:59:59.000,9:59:59.000 and he's checking people out, 9:59:59.000,9:59:59.000 and he sees this man, and he says, "Hey, 9:59:59.000,9:59:59.000 that guy looks like Daddy." 9:59:59.000,9:59:59.000 And I look at the man, 9:59:59.000,9:59:59.000 and he didn't look anything[br]at all like my husband, 9:59:59.000,9:59:59.000 nothing at all. 9:59:59.000,9:59:59.000 And so then I start[br]looking around on the plane, 9:59:59.000,9:59:59.000 and I notice this man 9:59:59.000,9:59:59.000 was the only black guy on the plane. 9:59:59.000,9:59:59.000 And I thought, 9:59:59.000,9:59:59.000 all right. 9:59:59.000,9:59:59.000 I'm going to have to have[br]a little talk with my son 9:59:59.000,9:59:59.000 about how not all black people look alike. 9:59:59.000,9:59:59.000 My son, he lifts his head up,[br]and he says to me, 9:59:59.000,9:59:59.000 "I hope he doesn't rob the plane." 9:59:59.000,9:59:59.000 And I said, "What? What did you say?" 9:59:59.000,9:59:59.000 And he says, "Well, I hope that man[br]doesn't rob the plane." 9:59:59.000,9:59:59.000 And I said, "Well, why would you say that? 9:59:59.000,9:59:59.000 You know Daddy wouldn't rob a plane." 9:59:59.000,9:59:59.000 And he says, "Yeah, yeah,[br]yeah, well, I know." 9:59:59.000,9:59:59.000 And I said, "Well,[br]why would you say that?" 9:59:59.000,9:59:59.000 And he looked at me[br]with this really sad face, 9:59:59.000,9:59:59.000 and he says, 9:59:59.000,9:59:59.000 "I don't know why I said that. 9:59:59.000,9:59:59.000 I don't know why I was thinking that." 9:59:59.000,9:59:59.000 We are living with such severe[br]racial stratification 9:59:59.000,9:59:59.000 that even a five-year old 9:59:59.000,9:59:59.000 can tell us what's supposed[br]to happen next, 9:59:59.000,9:59:59.000 even with no evildoer, 9:59:59.000,9:59:59.000 even with no explicit hatred. 9:59:59.000,9:59:59.000 This association between[br]blackness and crime 9:59:59.000,9:59:59.000 made its way into the mind[br]of my five-year old. 9:59:59.000,9:59:59.000 It makes its way into all of our children, 9:59:59.000,9:59:59.000 into all of us. 9:59:59.000,9:59:59.000 Our minds are shaped[br]by the racial disparities 9:59:59.000,9:59:59.000 we see out in the world, 9:59:59.000,9:59:59.000 and the narratives that help us[br]to make sense of the disparities we see. 9:59:59.000,9:59:59.000 Those people are criminals. 9:59:59.000,9:59:59.000 Those people are violent. 9:59:59.000,9:59:59.000 Those people are to be feared. 9:59:59.000,9:59:59.000 When my research team[br]brought people into our lab 9:59:59.000,9:59:59.000 and exposed them to faces, 9:59:59.000,9:59:59.000 we found that exposure to black faces 9:59:59.000,9:59:59.000 led them to see blurry images[br]of guns with greater clarity and speed. 9:59:59.000,9:59:59.000 Bias cannot only control what we see, 9:59:59.000,9:59:59.000 but where we look. 9:59:59.000,9:59:59.000 We found that prompting people[br]to think of violent crime 9:59:59.000,9:59:59.000 can lead them to direct their eyes[br]onto a black face 9:59:59.000,9:59:59.000 and away from a white face. 9:59:59.000,9:59:59.000 Prompting police officers[br]to think of capturing and shooting 9:59:59.000,9:59:59.000 and arresting 9:59:59.000,9:59:59.000 leads their eyes to settle[br]on black faces too. 9:59:59.000,9:59:59.000 Bias can infect every aspect[br]of our criminal justice system. 9:59:59.000,9:59:59.000 In a large dataset[br]of death-eligible defendants, 9:59:59.000,9:59:59.000 we found that looking more black 9:59:59.000,9:59:59.000 more than doubled their chances[br]of receiving a death sentence, 9:59:59.000,9:59:59.000 at least when their victims were white. 9:59:59.000,9:59:59.000 This effect is significant[br]even though we controlled 9:59:59.000,9:59:59.000 for the severity of the crime 9:59:59.000,9:59:59.000 and the defendant's attractiveness, 9:59:59.000,9:59:59.000 and no matter what we controlled for, 9:59:59.000,9:59:59.000 we found that black people 9:59:59.000,9:59:59.000 were punished in proportion 9:59:59.000,9:59:59.000 to the blackness[br]of their physical features: 9:59:59.000,9:59:59.000 the more black, 9:59:59.000,9:59:59.000 the more death-worthy. 9:59:59.000,9:59:59.000 Bias can also influence[br]how teachers discipline students. 9:59:59.000,9:59:59.000 My colleagues and I 9:59:59.000,9:59:59.000 have found that teachers express a desire 9:59:59.000,9:59:59.000 to discipline a black[br]middle school student 9:59:59.000,9:59:59.000 more harshly than a white student[br]for the same repeated infractions. 9:59:59.000,9:59:59.000 In a recent study, 9:59:59.000,9:59:59.000 we're finding that teachers[br]treat black students as a group 9:59:59.000,9:59:59.000 but white students as individuals. 9:59:59.000,9:59:59.000 If, for example,[br]one black student misbehaves 9:59:59.000,9:59:59.000 and then a different black student[br]misbehaves a few days later, 9:59:59.000,9:59:59.000 the teacher responds[br]to that second black student 9:59:59.000,9:59:59.000 as if he had misbehaved twice. 9:59:59.000,9:59:59.000 It's as though the sins of one child 9:59:59.000,9:59:59.000 get piled on to the other. 9:59:59.000,9:59:59.000 We create categories[br]to make sense of the world, 9:59:59.000,9:59:59.000 to assert some control and coherence 9:59:59.000,9:59:59.000 to the stimuli that we're[br]constantly being bombarded with. 9:59:59.000,9:59:59.000 Categorization and the bias that it seeds 9:59:59.000,9:59:59.000 allow our brains to make judgments 9:59:59.000,9:59:59.000 more quickly and efficiently, 9:59:59.000,9:59:59.000 and we do this by instinctively[br]relying on patterns 9:59:59.000,9:59:59.000 that seem predictable. 9:59:59.000,9:59:59.000 Yet just as the categories we create 9:59:59.000,9:59:59.000 allow us to make quick decisions, 9:59:59.000,9:59:59.000 they also reinforce bias. 9:59:59.000,9:59:59.000 So the very things that help us[br]to see the world 9:59:59.000,9:59:59.000 also can blind us to it. 9:59:59.000,9:59:59.000 They render our choices effortless, 9:59:59.000,9:59:59.000 friction-free. 9:59:59.000,9:59:59.000 Yet the exact a heavy toll. 9:59:59.000,9:59:59.000 So what can we do? 9:59:59.000,9:59:59.000 We are all vulnerable to bias, 9:59:59.000,9:59:59.000 but we don't act on bias all the time. 9:59:59.000,9:59:59.000 There are certain conditions[br]that can bring bias alive 9:59:59.000,9:59:59.000 and other conditions that can muffle it. 9:59:59.000,9:59:59.000 Let me give you an example. 9:59:59.000,9:59:59.000 Many people are familiar[br]with the tech company Nextdoor. 9:59:59.000,9:59:59.000 So their whole purpose is to create[br]stronger, healthier, safer neighborhoods. 9:59:59.000,9:59:59.000 And so they offer this online space 9:59:59.000,9:59:59.000 where neighbors can gather[br]and share information. 9:59:59.000,9:59:59.000 Yet Nextdoor soon found[br]that they had a problem 9:59:59.000,9:59:59.000 with racial profiling. 9:59:59.000,9:59:59.000 In the typical case, 9:59:59.000,9:59:59.000 people would look outside their window 9:59:59.000,9:59:59.000 and see a black man[br]in their otherwise white neighborhood 9:59:59.000,9:59:59.000 and make the snap judgment 9:59:59.000,9:59:59.000 that he was up to no good, 9:59:59.000,9:59:59.000 even when there was no evidence[br]of criminal wrongdoing. 9:59:59.000,9:59:59.000 In many ways, how we behave online 9:59:59.000,9:59:59.000 is a reflection of how[br]we behave in the world. 9:59:59.000,9:59:59.000 But what we don't want to do[br]is create an easy-to-use system 9:59:59.000,9:59:59.000 that can amplify bias 9:59:59.000,9:59:59.000 and deepen racial disparities, 9:59:59.000,9:59:59.000 rather than dismantling them. 9:59:59.000,9:59:59.000 So the co-founder of Nextdoor[br]reached out to me and to others 9:59:59.000,9:59:59.000 to try to figure out what to do, 9:59:59.000,9:59:59.000 and they realized that[br]to curb racial profiling on the platform, 9:59:59.000,9:59:59.000 they were going to have to add friction. 9:59:59.000,9:59:59.000 That is, they were going[br]to have to slow people done. 9:59:59.000,9:59:59.000 So Nextdoor had a choice to make, 9:59:59.000,9:59:59.000 and against every impulse 9:59:59.000,9:59:59.000 they decided to add friction. 9:59:59.000,9:59:59.000 And they did this by adding[br]a simple checklist. 9:59:59.000,9:59:59.000 There were three items on it. 9:59:59.000,9:59:59.000 First, they asked users to pause 9:59:59.000,9:59:59.000 and think, what was this person doing 9:59:59.000,9:59:59.000 that made him suspicious? 9:59:59.000,9:59:59.000 The category "black man"[br]is not ground for suspicion. 9:59:59.000,9:59:59.000 Second, they asked users to describe[br]the person's physical features, 9:59:59.000,9:59:59.000 not simply their race and gender. 9:59:59.000,9:59:59.000 Third, they realized that a lot of people 9:59:59.000,9:59:59.000 didn't seem to know[br]what racial profiling was, 9:59:59.000,9:59:59.000 nor that they were engaging in it. 9:59:59.000,9:59:59.000 So Nextdoor provided them[br]with a definition 9:59:59.000,9:59:59.000 and told them that it[br]was strictly prohibited. 9:59:59.000,9:59:59.000 Most of you have seen[br]those signs in airports 9:59:59.000,9:59:59.000 and Metro stations,[br]"If you see something, say something." 9:59:59.000,9:59:59.000 Nextdoor tried modifying this. 9:59:59.000,9:59:59.000 "If you see something suspicious,[br]say something specific." 9:59:59.000,9:59:59.000 And using this strategy 9:59:59.000,9:59:59.000 by simply slowing people down, 9:59:59.000,9:59:59.000 Nextdoor was able to curb[br]racial profiling by 75 percent. 9:59:59.000,9:59:59.000 Now, people often will say to me, 9:59:59.000,9:59:59.000 you can't add friction[br]in every situation, in every context, 9:59:59.000,9:59:59.000 and especially for people who make[br]split second decisions all the time, 9:59:59.000,9:59:59.000 but it turns out we can add frictions 9:59:59.000,9:59:59.000 to more situations than we think. 9:59:59.000,9:59:59.000 Working with the Oakland Police Department 9:59:59.000,9:59:59.000 in California, 9:59:59.000,9:59:59.000 I and a number of my colleagues[br]were able to help the Department 9:59:59.000,9:59:59.000 to reduce the number of stops they made 9:59:59.000,9:59:59.000 of people who were not[br]committing any serious crimes, 9:59:59.000,9:59:59.000 and we did this by pushing officers 9:59:59.000,9:59:59.000 to ask themselves a question 9:59:59.000,9:59:59.000 before each and every stop they made: 9:59:59.000,9:59:59.000 is this stop intelligence-led, 9:59:59.000,9:59:59.000 yes or no? 9:59:59.000,9:59:59.000 In other words, 9:59:59.000,9:59:59.000 do I have prior information[br]to tie this particular person 9:59:59.000,9:59:59.000 to a specific crime? 9:59:59.000,9:59:59.000 By adding that question 9:59:59.000,9:59:59.000 to the form officer's[br]complete doing the stop, 9:59:59.000,9:59:59.000 they slow down, they pause, 9:59:59.000,9:59:59.000 they think, why am I considering[br]pulling this person over? 9:59:59.000,9:59:59.000 In 2017, before we added that[br]intelligence-led question to the form, 9:59:59.000,9:59:59.000 officers made about 32,000 stops[br]across the city. 9:59:59.000,9:59:59.000 In that next year, 9:59:59.000,9:59:59.000 with the addition of this question, 9:59:59.000,9:59:59.000 that fell to 19,000 stops. 9:59:59.000,9:59:59.000 African-American stops alone[br]fell by 43 percent. 9:59:59.000,9:59:59.000 And stopping fewer black people[br]did not make the city any more dangerous. 9:59:59.000,9:59:59.000 In fact, the crime rate continued to fall 9:59:59.000,9:59:59.000 and the city became safer for everybody. 9:59:59.000,9:59:59.000 So one solution can come from reducing[br]the number of unnecessary stops. 9:59:59.000,9:59:59.000 Another can come from improving[br]the quality of the stops 9:59:59.000,9:59:59.000 officers do make. 9:59:59.000,9:59:59.000 And technology can help us here. 9:59:59.000,9:59:59.000 We all know about George Floyd's death 9:59:59.000,9:59:59.000 because those who tried to come to his aid 9:59:59.000,9:59:59.000 held cell phone cameras[br]to record that horrific, fatal encounter 9:59:59.000,9:59:59.000 with the police. 9:59:59.000,9:59:59.000 But we have all sorts of technology 9:59:59.000,9:59:59.000 that we're not putting to good use. 9:59:59.000,9:59:59.000 Police departments across the country 9:59:59.000,9:59:59.000 are now required to wear[br]body-worn cameras, 9:59:59.000,9:59:59.000 so we have recordings of not only[br]the most extreme and horrific encounters 9:59:59.000,9:59:59.000 but of everyday interactions. 9:59:59.000,9:59:59.000 With an interdisciplinary[br]team at Stanford, 9:59:59.000,9:59:59.000 we've begun to use[br]machine learning techniques 9:59:59.000,9:59:59.000 to analyze large numbers of encounters. 9:59:59.000,9:59:59.000 This is to better understand what happens[br]in routine traffic stops. 9:59:59.000,9:59:59.000 What we found was that[br]even when police officers 9:59:59.000,9:59:59.000 are behaving professionally, 9:59:59.000,9:59:59.000 they speak to black drivers[br]less respectfully than white drivers. 9:59:59.000,9:59:59.000 In fact, from the words officers use alone 9:59:59.000,9:59:59.000 we could predict whether they were talking[br]to a black driver or a white driver. 9:59:59.000,9:59:59.000 The problem is that the vast majority[br]of the footage from these cameras 9:59:59.000,9:59:59.000 is not used by police departments 9:59:59.000,9:59:59.000 to understand what's[br]going on on the street 9:59:59.000,9:59:59.000 or to train officers. 9:59:59.000,9:59:59.000 And that's a shame. 9:59:59.000,9:59:59.000 How does a routine stop 9:59:59.000,9:59:59.000 turn into a deadly encounter? 9:59:59.000,9:59:59.000 How did this happen[br]in George Floyd's case? 9:59:59.000,9:59:59.000 How did it happen in others? 9:59:59.000,9:59:59.000 When my eldest son was 16 years old, 9:59:59.000,9:59:59.000 he discovered that[br]when white people look at him, 9:59:59.000,9:59:59.000 they feel fear. 9:59:59.000,9:59:59.000 Elevators are the worst, he said. 9:59:59.000,9:59:59.000 When those doors close, 9:59:59.000,9:59:59.000 people are trapped in this tiny space 9:59:59.000,9:59:59.000 with someone they have been taught 9:59:59.000,9:59:59.000 to associate with danger. 9:59:59.000,9:59:59.000 My son senses their discomfort, 9:59:59.000,9:59:59.000 and he smiles to put them at ease, 9:59:59.000,9:59:59.000 to calm their fears. 9:59:59.000,9:59:59.000 When he speaks, 9:59:59.000,9:59:59.000 their bodies relax. 9:59:59.000,9:59:59.000 They breathe easier. 9:59:59.000,9:59:59.000 They take pleasure in his cadence, 9:59:59.000,9:59:59.000 his diction, his word choice. 9:59:59.000,9:59:59.000 He sounds like one of them. 9:59:59.000,9:59:59.000 I used to think that my son[br]was a natural extrovert like his father, 9:59:59.000,9:59:59.000 but I realized at that moment[br]in that conversation 9:59:59.000,9:59:59.000 that his smile was not a sign 9:59:59.000,9:59:59.000 that he wanted to connect[br]with would-be strangers. 9:59:59.000,9:59:59.000 It was a talisman he's used[br]to protect himself, 9:59:59.000,9:59:59.000 a survival skill he had honed[br]over thousands of elevator rides. 9:59:59.000,9:59:59.000 He was learning to accommodate the tension 9:59:59.000,9:59:59.000 that his skin color generated 9:59:59.000,9:59:59.000 and that put his own life at risk. 9:59:59.000,9:59:59.000 We know that the brain is wired for bias, 9:59:59.000,9:59:59.000 and one way to interrupt that bias 9:59:59.000,9:59:59.000 is to pause and to reflect[br]on the evidence of our assumptions, 9:59:59.000,9:59:59.000 so we need to ask ourselves 9:59:59.000,9:59:59.000 what assumptions do we bring 9:59:59.000,9:59:59.000 when we step onto an elevator? 9:59:59.000,9:59:59.000 Or an airplane? 9:59:59.000,9:59:59.000 How do we make ourselves aware[br]of our own unconscious bias? 9:59:59.000,9:59:59.000 Who do those assumptions keep safe? 9:59:59.000,9:59:59.000 Who do they put at risk? 9:59:59.000,9:59:59.000 Until we ask these questions, 9:59:59.000,9:59:59.000 and insist that our schools[br]and our courts and our police departments 9:59:59.000,9:59:59.000 and every institution do the same, 9:59:59.000,9:59:59.000 we will continue to allow bias 9:59:59.000,9:59:59.000 to blind us, 9:59:59.000,9:59:59.000 and if we do, 9:59:59.000,9:59:59.000 none of us are truly safe. 9:59:59.000,9:59:59.000 Thank you.