1 00:00:00,000 --> 00:00:09,329 *preroll music* 2 00:00:09,329 --> 00:00:14,179 Herald: Welcome Jeff with a warm applause on stage. He works for Tactical Tech 3 00:00:14,179 --> 00:00:19,130 *applause* 4 00:00:19,130 --> 00:00:22,860 and will talk about a bias in data and racial profiling 5 00:00:22,860 --> 00:00:25,870 in Germany compared with the UK. It’s your stage! 6 00:00:25,870 --> 00:00:30,090 Jeff: Right. Thank you! Yeah, okay! 7 00:00:30,090 --> 00:00:33,320 My presentation is called “Profiling (In)justice – 8 00:00:33,320 --> 00:00:36,430 – Disaggregating Data by Race and Ethnicity to Monitor 9 00:00:36,430 --> 00:00:41,630 and Evaluate Discriminatory Policing”. In terms of my background: 10 00:00:41,630 --> 00:00:46,780 I’ve done research, doing mostly quantitative research 11 00:00:46,780 --> 00:00:50,730 around the issues of racial discrimination for a long time. 12 00:00:50,730 --> 00:00:55,960 In New York, at the Center for Constitutional Rights I was working on 13 00:00:55,960 --> 00:00:59,800 looking at trends and levels of 14 00:00:59,800 --> 00:01:04,210 use-of-force by police against civilians, and also on stop-and-search 15 00:01:04,210 --> 00:01:08,601 against civilians. And then more recently for the last 18 months or so 16 00:01:08,601 --> 00:01:12,330 I’ve been working as a research consultant at Tactical Tech, 17 00:01:12,330 --> 00:01:16,360 looking at issues of data politics and privacy. So this is kind of like a merger 18 00:01:16,360 --> 00:01:21,960 of these 2 areas. In terms of what this presentation is gonna be about: 19 00:01:21,960 --> 00:01:26,900 there’s gonna be 3 takeaways. First, that 20 00:01:26,900 --> 00:01:29,590 we’re dealing with the issues of privacy and also [freedom from] discrimination. 21 00:01:29,590 --> 00:01:34,869 And both are fundamental human rights. But there’s tension between the two. 22 00:01:34,869 --> 00:01:40,879 And important questions to think about are: “When do privacy concerns exceed 23 00:01:40,879 --> 00:01:46,490 or take precedence over those of discrimination, or vice versa?” 24 00:01:46,490 --> 00:01:53,400 Two: That data is political, both in the collection and aggregation of data; 25 00:01:53,400 --> 00:01:56,930 but also in terms of having the categories of being created. 26 00:01:56,930 --> 00:02:00,549 And then, three: That data ethics are a complex thing, that things aren’t 27 00:02:00,549 --> 00:02:05,090 so black-and-white all of the time. So what is racial profiling? 28 00:02:05,090 --> 00:02:08,910 The term originates from the US. 29 00:02:08,910 --> 00:02:14,509 And it refers to when a police officer suspects, stops, questions, arrests or… 30 00:02:14,509 --> 00:02:17,079 you know, or… at other stages (?) of the communal justice system 31 00:02:17,079 --> 00:02:21,039 because of their perceived race or ethnicity. After 9/11 32 00:02:21,039 --> 00:02:26,609 it also refers to the profiling of Muslims or people perceived to be Middle Eastern. 33 00:02:26,609 --> 00:02:31,519 And in German there is no direct translation, so the term ‘Racial Profiling’ (quotes) 34 00:02:31,519 --> 00:02:36,859 is used a lot in parliamentary hearings and also in court documents. 35 00:02:36,859 --> 00:02:41,790 So the problem that we’re gonna talk about is that because of the lack of data 36 00:02:41,790 --> 00:02:46,309 in Germany there’s no empirical evidence to monitor and evaluate 37 00:02:46,309 --> 00:02:50,729 trends in discrimination. This is creating problems 38 00:02:50,729 --> 00:02:55,290 for both civil society in terms of looking at these levels and trends over time, 39 00:02:55,290 --> 00:02:58,199 but also from an individual perspective it becomes difficult for people 40 00:02:58,199 --> 00:03:02,259 to file complaints. In Germany the only way to file a complaint officially 41 00:03:02,259 --> 00:03:07,999 is to go to the police department, which introduces power dynamics, 42 00:03:07,999 --> 00:03:11,349 you know, challenges and additional barriers. But also if you’re an individual 43 00:03:11,349 --> 00:03:16,329 you have to show that there’s a trend, right? That you are part of another, 44 00:03:16,329 --> 00:03:19,759 a long standing story. And without this data it becomes difficult to prove 45 00:03:19,759 --> 00:03:24,049 that that’s happening. So what we’re needing, 46 00:03:24,049 --> 00:03:27,159 or what some people are calling for, is having this data 47 00:03:27,159 --> 00:03:32,850 at a state and a sort of national level. And this ratio that I’m putting here, 48 00:03:32,850 --> 00:03:36,019 referring to policing, is looking at the rate at which people are stopped 49 00:03:36,019 --> 00:03:41,629 over the census figure of the demographic share of the population. 50 00:03:41,629 --> 00:03:44,900 And you really need both; the first being on the police side and 51 00:03:44,900 --> 00:03:49,589 the second being on the census. So that, you know, if you only have one, 52 00:03:49,589 --> 00:03:52,170 if you only have the rate at which police were stopping people then you actually 53 00:03:52,170 --> 00:03:55,169 can’t see if this is discriminatory or not. And if you only have the census 54 00:03:55,169 --> 00:03:59,720 then you can’t see that, either. So you really need both. 55 00:03:59,720 --> 00:04:03,790 The European Commission, the International Labour Organisation and academics are all 56 00:04:03,790 --> 00:04:10,549 calling for these… the creation of standardized and comparable data sets. 57 00:04:10,549 --> 00:04:13,939 And I’m not gonna read these out, but I can go back to them later 58 00:04:13,939 --> 00:04:18,760 if you’re interested. But what I’m gonna talk about is comparing the UK 59 00:04:18,760 --> 00:04:23,290 to that of Germany. So in Germany, 60 00:04:23,290 --> 00:04:28,130 in 1983 there was a census; or there was an attempt to making a census. 61 00:04:28,130 --> 00:04:31,970 But due to wide-spread resentment and disenfranchisement, 62 00:04:31,970 --> 00:04:37,190 fears of surveillance and lack of trust in state data collection 63 00:04:37,190 --> 00:04:42,490 there was a big boycott. Or people deliberately filled in forms wrong. 64 00:04:42,490 --> 00:04:45,280 In some cases there were even bombings of statistical offices. 65 00:04:45,280 --> 00:04:51,220 Or people spilled coffee over census forms to try to deliberately ruin them. 66 00:04:51,220 --> 00:04:55,530 As a couple of other presentations at the conference have already said 67 00:04:55,530 --> 00:04:59,250 this was found to be an unconstitutional census. 68 00:04:59,250 --> 00:05:01,990 Because of the way that they were framing it. 69 00:05:01,990 --> 00:05:08,520 Comparing the census to household registrations. 70 00:05:08,520 --> 00:05:14,900 And so the census was delayed until 1987, 71 00:05:14,900 --> 00:05:19,930 which was the most recent census until the most recent European one in 2011. 72 00:05:19,930 --> 00:05:23,260 This Supreme Court decision was really important 73 00:05:23,260 --> 00:05:28,810 because it established this right for informational self-determination. 74 00:05:28,810 --> 00:05:33,040 Very important for privacy in terms of Germany. 75 00:05:33,040 --> 00:05:37,710 You know, until today. So what kinds of information is being collected? 76 00:05:37,710 --> 00:05:40,690 In Germany we have pretty standard kind of demographic information things 77 00:05:40,690 --> 00:05:45,200 like gender, age, income, religion. But what I want to talk about in particular 78 00:05:45,200 --> 00:05:49,200 is country origin and country citizenship. 79 00:05:49,200 --> 00:05:53,660 Which are used to determine a person of migration background. And 80 00:05:53,660 --> 00:05:56,860 this term ‘person of migration background’ generally refers to whether you, 81 00:05:56,860 --> 00:06:00,220 your parents or your grandparents – the first, second or third generation – 82 00:06:00,220 --> 00:06:03,960 come from a migrant background. Right, and 83 00:06:03,960 --> 00:06:10,000 this term is used oftentimes as a proxy for ethnic or for racial diversity in Germany. 84 00:06:10,000 --> 00:06:15,050 And this is problematic because you’re using citizenship as a proxy 85 00:06:15,050 --> 00:06:20,080 for looking at racial and ethnic identity. And it also ignores the experiences 86 00:06:20,080 --> 00:06:23,450 and identities, the self identities of people who don’t fall into 87 00:06:23,450 --> 00:06:26,870 this ‘first, second or third generation’, right? People who may identify 88 00:06:26,870 --> 00:06:30,690 as Black German, let’s say. But of fourth, fifth or sixth generation. 89 00:06:30,690 --> 00:06:34,710 They’re just ignored in this data set. So they fall out. 90 00:06:34,710 --> 00:06:38,160 Also, it’s difficult to measure these at a national level because each state 91 00:06:38,160 --> 00:06:41,950 has different definitions of what constitutes a migrant background. 92 00:06:41,950 --> 00:06:44,790 So we don’t have this at a national level but also within states there’s no way 93 00:06:44,790 --> 00:06:49,370 to compare them. Of course, not having that data doesn’t mean 94 00:06:49,370 --> 00:06:53,840 that there’s no racism, right? And so in 2005 e.g. we see 95 00:06:53,840 --> 00:06:57,180 that neo-Nazi incidents have increased 25% 96 00:06:57,180 --> 00:07:03,320 – the NSU case coming out but still going on in court proceedings. 97 00:07:03,320 --> 00:07:08,020 The xenophobic attacks but also the way in which these crimes were investigated 98 00:07:08,020 --> 00:07:13,670 – at a state and at a federal level – and the way that it was botched, 99 00:07:13,670 --> 00:07:17,900 in addition to showing that racism now in general 100 00:07:17,900 --> 00:07:22,230 is at a higher rate than it has been for the last 30 years. And much more recently 101 00:07:22,230 --> 00:07:26,710 seeing the rise in arson attacks on refugee centers. There’s been 102 00:07:26,710 --> 00:07:30,360 over 200 attacks this year so far. You know, all of these showed 103 00:07:30,360 --> 00:07:34,220 that not collecting this data doesn’t mean that we don’t have a problem. 104 00:07:34,220 --> 00:07:40,830 So, the UK by comparison: In 1981, there was the Brixton riots, 105 00:07:40,830 --> 00:07:45,670 in an area of London. And these arose largely 106 00:07:45,670 --> 00:07:50,320 because of resentment towards the way that police were 107 00:07:50,320 --> 00:07:53,550 carrying out what they called ‘Sus Laws’. Or people being able to be stopped 108 00:07:53,550 --> 00:07:58,080 on suspicion of committing a crime, carrying drugs, 109 00:07:58,080 --> 00:08:03,650 having a weapon etc. and so forth. And so in the aftermath of the riot 110 00:08:03,650 --> 00:08:07,550 they came up with this report called the ‘Scarman report’. And this found 111 00:08:07,550 --> 00:08:11,150 that there is much disproportionality in the way that Police were carrying out 112 00:08:11,150 --> 00:08:16,280 their stop-and-search procedures. So for the first time this required… 113 00:08:16,280 --> 00:08:20,130 or one of the reforms that was instituted was that UK Police started 114 00:08:20,130 --> 00:08:26,750 to have to collect data on race or ethnicity during the stops. 115 00:08:26,750 --> 00:08:29,600 When they stop a person they have to start collecting this data. And then you have 116 00:08:29,600 --> 00:08:34,629 a baseline that’s being established. Around the same time in the UK 117 00:08:34,629 --> 00:08:38,729 we have the 1981 census. 118 00:08:38,729 --> 00:08:41,809 And in society they were having a lot of debates around 119 00:08:41,809 --> 00:08:45,899 whether or not they wanted to have this… 120 00:08:45,899 --> 00:08:49,971 collecting this baseline national level (?) figure, because we need these 2 things 121 00:08:49,971 --> 00:08:56,260 for this ratio in order to monitor and evaluate levels of discrimination. 122 00:08:56,260 --> 00:09:00,240 But, you know, there was a lot of opposition to this. 123 00:09:00,240 --> 00:09:04,829 And many found it to be (quote) “morally and politically objectionable”. 124 00:09:04,829 --> 00:09:08,570 But not for the reason you’d think. People found objections to it 125 00:09:08,570 --> 00:09:13,230 not because of asking these question, but because of the way that the question 126 00:09:13,230 --> 00:09:17,190 was phrased, with the categories that were being used. And they did surveys 127 00:09:17,190 --> 00:09:21,399 between ’75 and about ’95, and found that 128 00:09:21,399 --> 00:09:26,529 among marginalized communities and in minority ethnicity groups 129 00:09:26,529 --> 00:09:31,329 there was actually a lot of support for collecting this kind of data. 130 00:09:31,329 --> 00:09:35,250 They just wanted to have it phrased to be different. And so ’91 they started 131 00:09:35,250 --> 00:09:40,359 to collect the data. They put this ‘race question’ in. And here I have, 132 00:09:40,359 --> 00:09:45,600 in 2011 – the most recent census – some of the kinds of categories 133 00:09:45,600 --> 00:09:50,049 that they wanted to also include. And they’ve changed over time. 134 00:09:50,049 --> 00:09:54,329 So e.g. like ‘White Irish people’ felt that they also were discriminated against. 135 00:09:54,329 --> 00:09:58,930 And they experienced things differently than white British people, e.g. 136 00:09:58,930 --> 00:10:03,231 So having things broken down further would be helpful for them 137 00:10:03,231 --> 00:10:09,720 in terms of highlighting discrimination that each specific demographic faces. 138 00:10:09,720 --> 00:10:14,379 So around that time ’91, ’93 we have the murder of Stephen Lawrence 139 00:10:14,379 --> 00:10:19,130 in an unprovoked racist attack. Nobody was ever convicted of that. But 140 00:10:19,130 --> 00:10:22,529 what’s important is that we have this ‘Macpherson report’ that came out. 141 00:10:22,529 --> 00:10:27,290 And it developed a lot of recommendations, 70, and most of them were adopted. 142 00:10:27,290 --> 00:10:31,529 One: to be collecting this at a national level, and to be comparing these. 143 00:10:31,529 --> 00:10:35,199 In 2011 they stopped mandating that you had to collect this data, 144 00:10:35,199 --> 00:10:38,709 at a national level. So none of the data from then going forward 145 00:10:38,709 --> 00:10:42,659 can actually be trusted. Some forces continued to do it, 146 00:10:42,659 --> 00:10:46,270 but not all of them. So you can’t actually compare them between forces. 147 00:10:46,270 --> 00:10:50,249 In the same year we have these London riots. The Guardian and LSE put out 148 00:10:50,249 --> 00:10:54,190 a report called “Reading the Riots”. Where they did a lot of interviews with people 149 00:10:54,190 --> 00:10:58,429 who participated. And they found that most of the people who participated 150 00:10:58,429 --> 00:11:03,569 had feelings of… that they were mistreated by Police. 151 00:11:03,569 --> 00:11:07,820 Or that there is racial discrimination in terms of the policing practices. 152 00:11:07,820 --> 00:11:11,760 That they weren’t being treated with respect. 153 00:11:11,760 --> 00:11:16,710 So to put some data to that: Before this was removed 154 00:11:16,710 --> 00:11:22,219 there… it was 2 different types of stops in the UK. Those PACE stops, 155 00:11:22,219 --> 00:11:25,769 where you stops with reasonable suspicion. 156 00:11:25,769 --> 00:11:30,379 And among that you have e.g. black people stopped at 7 times the rate of white people. 157 00:11:30,379 --> 00:11:34,690 Asian people – Asian referring to (?)(?)(?)(?) Southeast Asian in the UK – 158 00:11:34,690 --> 00:11:39,430 at twice the rate. And ‘Section 60 stops’: where you don’t have to actually have 159 00:11:39,430 --> 00:11:43,399 reasonable suspicion. And when you don’t need to have that you have much, much 160 00:11:43,399 --> 00:11:51,840 higher rates. 26.6 times the rate of white people black people are being stopped at. 161 00:11:51,840 --> 00:11:54,069 But the State Department even coming out and they’re saying: “There’s 162 00:11:54,069 --> 00:11:59,730 no relationship between criminality and race… criminality and ethnicity”. 163 00:11:59,730 --> 00:12:02,450 In fact it’s like: If people are being stopped at these rates it’s… 164 00:12:02,450 --> 00:12:06,670 it’s in the wrong direction. You have white males in particular who are 165 00:12:06,670 --> 00:12:10,020 fending at higher rates. Who are using drugs at a higher rate. Who are 166 00:12:10,020 --> 00:12:15,060 possessing weapons at a higher rate. But that’s not who’s being stopped. 167 00:12:15,060 --> 00:12:19,579 There is a connection though between race and ethnicity and poverty. 168 00:12:19,579 --> 00:12:23,040 So you can see here, they call it like BAME groups, or ‘Black, Asian and 169 00:12:23,040 --> 00:12:27,220 Minority Ethnicity’. And you can see that among like wealth and assets: 170 00:12:27,220 --> 00:12:30,710 it’s much, much lower for non-white households. Unemployment rates 171 00:12:30,710 --> 00:12:36,149 are much higher as well. Income is much lower. 172 00:12:36,149 --> 00:12:39,809 So I like making maps. And I think maps are really cool. ’Cause you can 173 00:12:39,809 --> 00:12:44,269 tell stories when you overlay a lot of data with it. So on the left 174 00:12:44,269 --> 00:12:50,699 I put by different borough in London where people are actually being stopped. 175 00:12:50,699 --> 00:12:54,529 Per 1,000 people in 2012. And on the right I put 176 00:12:54,529 --> 00:12:58,789 where the crime is actually occurring. And this is coming from UK Police. 177 00:12:58,789 --> 00:13:02,009 And so you can see that where people are being stopped isn’t exactly 178 00:13:02,009 --> 00:13:05,799 where the crime is actually happening. And if you’re seeing this stop-and-search 179 00:13:05,799 --> 00:13:11,069 as a crime preventing tactic then we have to question why this isn’t lining up. 180 00:13:11,069 --> 00:13:15,449 Going back to this ratio: 181 00:13:15,449 --> 00:13:19,459 earlier I mentioned like – having the rate at which one group is being stopped 182 00:13:19,459 --> 00:13:22,990 over that share of the total population. 183 00:13:22,990 --> 00:13:26,000 And we can take it a step further and we can compare that to… 184 00:13:26,000 --> 00:13:29,029 between different demographic groups. 185 00:13:29,029 --> 00:13:33,610 And when using census figures combined with police figures, 186 00:13:33,610 --> 00:13:38,500 we can do things like looking on the left. I mean this disproportionality ratio, 187 00:13:38,500 --> 00:13:41,260 so the rate at which black groups as a share are stopped 188 00:13:41,260 --> 00:13:45,839 versus the total population, compared to white groups are stopped. 189 00:13:45,839 --> 00:13:49,920 And you can see the darker areas is where you have a higher rate. 190 00:13:49,920 --> 00:13:56,230 So when we’re talking about those ‘7 times, or 26 times more likely’ 191 00:13:56,230 --> 00:13:59,959 these are those areas that we’re talking about. And so the darker areas: 192 00:13:59,959 --> 00:14:05,909 you can see that when compared to poverty, 193 00:14:05,909 --> 00:14:09,309 people are stopped… there’s greater disproportionality ratios 194 00:14:09,309 --> 00:14:13,030 in wealthier areas than there are in poorer areas. And this is kind of 195 00:14:13,030 --> 00:14:16,989 a way, you could say, almost of perceiving people of colour 196 00:14:16,989 --> 00:14:24,510 as others who shouldn’t belong in these areas. It’s also… you can… 197 00:14:24,510 --> 00:14:27,819 when combined with other census information you can see that you have 198 00:14:27,819 --> 00:14:32,069 more discrimination, you have more disparities in areas that are more white 199 00:14:32,069 --> 00:14:36,240 and also less racially diverse. 200 00:14:36,240 --> 00:14:40,069 So this is kind of all on the same kind of a message. 201 00:14:40,069 --> 00:14:44,229 But if it works fine? – It doesn’t. UK Police is saying that 202 00:14:44,229 --> 00:14:49,499 at most they have a 6% arrest rate of all stops. 203 00:14:49,499 --> 00:14:52,970 And arrests are not conviction rates. 204 00:14:52,970 --> 00:14:59,319 Looking for weapons we have like less than 1% of a positive search rate. 205 00:14:59,319 --> 00:15:03,350 And the European Human Rights Commission e.g. has called for reform 206 00:15:03,350 --> 00:15:06,999 of these practices. The UN has called for reform of these practices. 207 00:15:06,999 --> 00:15:12,559 And they instituted like a reform that called for 208 00:15:12,559 --> 00:15:19,039 having a 20% arrest quota. And so that could either go positively or negatively. 209 00:15:19,039 --> 00:15:21,649 Making a higher quota means that you could be just arresting more people 210 00:15:21,649 --> 00:15:26,439 that you’re stopping. More likely, or hopefully it means that you have 211 00:15:26,439 --> 00:15:31,550 a higher justification or grounds for stopping a person. 212 00:15:31,550 --> 00:15:35,430 So these are the kinds of things you can do in the UK, with these kinds of data. 213 00:15:35,430 --> 00:15:40,079 In Germany, you can’t. But I wanna highlight there’s this one case 214 00:15:40,079 --> 00:15:45,150 in Koblenz in 2010. There was a student of… 215 00:15:45,150 --> 00:15:50,050 unnamed, black student who is stopped travelling on train, 216 00:15:50,050 --> 00:15:53,310 and who was asked to show his ID. And he refused. And he said: “No, 217 00:15:53,310 --> 00:16:01,190 I’m not gonna do that. This is reminiscent of Nazi era tactics”. 218 00:16:01,190 --> 00:16:07,509 And so he was charged with slander. And he was brought into court. 219 00:16:07,509 --> 00:16:11,439 And the police officer, when it was in court, said, (quote): 220 00:16:11,439 --> 00:16:16,149 “I approach people that look like foreigners, this is based on skin colour.” 221 00:16:16,149 --> 00:16:20,209 And so this is for the first time the police have admitted that 222 00:16:20,209 --> 00:16:23,470 their grounds for doing immigration related stops are based on 223 00:16:23,470 --> 00:16:28,520 perceived race or ethnicity. The judge sided with the police. 224 00:16:28,520 --> 00:16:32,029 That this was good justification, like it was good grounds. 225 00:16:32,029 --> 00:16:36,779 But a higher court ruled that that wasn’t the case. 226 00:16:36,779 --> 00:16:38,540 They said: “Yeah, this is unconstitutional, 227 00:16:38,540 --> 00:16:42,339 you can’t actually do it, it violates the constitution.” 228 00:16:42,339 --> 00:16:46,249 No person shall be favoured or disfavoured because of sex, parentage, race, 229 00:16:46,249 --> 00:16:50,739 language, homeland, origin, faith, religious… etc. 230 00:16:50,739 --> 00:16:54,360 Just as a side note there’s been a large movement to remove this term ‘race’ 231 00:16:54,360 --> 00:16:58,410 from that part of the constitution since it’s been put in. 232 00:16:58,410 --> 00:17:02,189 And also the court dismissed the slander charge. They said: “No, this student…” 233 00:17:02,189 --> 00:17:07,160 like he’s actually able to critique the police, you know, in this way. 234 00:17:07,160 --> 00:17:10,660 But after we have the response by the police union, 235 00:17:10,660 --> 00:17:14,440 the head of the police union at the time, who said (quote): 236 00:17:14,440 --> 00:17:18,010 “The courts deal with the law in an aesthetical pleasing way, but 237 00:17:18,010 --> 00:17:21,760 they don’t make sure their judgments match practical requirements”. 238 00:17:21,760 --> 00:17:25,400 And so what this means is: we see that according to the police union 239 00:17:25,400 --> 00:17:28,870 – this isn’t official response, but this is from the Police Union itself – 240 00:17:28,870 --> 00:17:32,920 they say that we need to profile. We need to do this. 241 00:17:32,920 --> 00:17:38,750 Or else we aren’t able to do immigration related stops. 242 00:17:38,750 --> 00:17:43,470 That’s crazy. They also… I mean, at the same time 243 00:17:43,470 --> 00:17:46,840 when they were doing these parliamentary hearings they institute these mandatory 244 00:17:46,840 --> 00:17:50,660 inter cultural trainings for police officers. And these are kind of 245 00:17:50,660 --> 00:17:55,210 like a one-day training where you go and learn all about 246 00:17:55,210 --> 00:17:58,650 how to deal with people from different cultures. But in some of the interviews 247 00:17:58,650 --> 00:18:01,910 that I was doing they said: “Okay, well, this isn’t an inter cultural issue. 248 00:18:01,910 --> 00:18:05,730 This is a racism issue”. 249 00:18:05,730 --> 00:18:08,250 People aren’t just coming from other places. These are Germans, 250 00:18:08,250 --> 00:18:11,000 these are people who grew up here. These are people who live here. Who know 251 00:18:11,000 --> 00:18:15,970 how to speak the language. Who were born and raised… 252 00:18:15,970 --> 00:18:19,260 And we need to be dealing with this in a different way. 253 00:18:19,260 --> 00:18:23,250 However, in this time, we see that racial profiling has become part of 254 00:18:23,250 --> 00:18:29,560 the national conversation. And so this is the sticker that somebody put up 255 00:18:29,560 --> 00:18:33,040 in Berlin, in a U-Bahn. It says: “Attention…, 256 00:18:33,040 --> 00:18:36,140 we practice RACIAL PROFILING while checking the validity of your ticket”. 257 00:18:36,140 --> 00:18:42,200 It’s not real, but it looks… I think it’s kind of cool. 258 00:18:42,200 --> 00:18:45,790 When they were doing this in these Bundestag hearings… 259 00:18:45,790 --> 00:18:50,260 they released data for Federal Police for 2013. This is the first time 260 00:18:50,260 --> 00:18:54,270 that we have any data that’s released. No data has ever been released 261 00:18:54,270 --> 00:18:57,430 based on State Police stops. They say that they’re not actually 262 00:18:57,430 --> 00:19:01,010 collecting the information, so they don’t have anything to show. Of course 263 00:19:01,010 --> 00:19:03,960 the figures that are released from the Federal Police are not disaggregated 264 00:19:03,960 --> 00:19:08,000 by race and ethnicity. But what does this actually show? 265 00:19:08,000 --> 00:19:17,270 So, most of the stops, over 85% are border stops. 266 00:19:17,270 --> 00:19:20,910 Border being within ca. 30 km of the German border. 267 00:19:20,910 --> 00:19:25,540 So this is actually taking into account most of the German population. 268 00:19:25,540 --> 00:19:29,470 But if we’re doing these immigration related stops then… if we break it down 269 00:19:29,470 --> 00:19:34,430 by offense – in the last two, these are the immigration related offenses 270 00:19:34,430 --> 00:19:38,910 that people are committing – and we have less than, at most, 271 00:19:38,910 --> 00:19:44,080 maybe 1% of people who are found to be a positive, 272 00:19:44,080 --> 00:19:48,100 meaning that they’re found to be violating some kind of offense. It’s – again, 273 00:19:48,100 --> 00:19:53,930 it’s not a conviction, right? And people can challenge this. 274 00:19:53,930 --> 00:19:56,550 E.g. like you don’t have to have your ID on you in all times. You can 275 00:19:56,550 --> 00:20:00,470 present it later, and the charge can go away. 276 00:20:00,470 --> 00:20:05,080 But if we have such low rates of positive searches 277 00:20:05,080 --> 00:20:10,980 then like why is this happening? Why do we feel that with such good data, 278 00:20:10,980 --> 00:20:18,950 and knowing, as good researchers, why are we continuing this as a practice? 279 00:20:18,950 --> 00:20:22,000 On one of the other interviews that I was doing they found that okay well: 280 00:20:22,000 --> 00:20:26,470 You know, we know this is ineffective. But this has the effect of criminalizing 281 00:20:26,470 --> 00:20:31,550 our communities. And whether or not this is true 282 00:20:31,550 --> 00:20:35,130 is an argument for why we should maybe have this kind of data to show that 283 00:20:35,130 --> 00:20:41,220 this is or is not actually occurring. Of course, European Commission 284 00:20:41,220 --> 00:20:46,490 against racism and intolerance and the UN have said: “Well, even among this at most 285 00:20:46,490 --> 00:20:50,021 1% positive rates these are not being distributed evenly, and 286 00:20:50,021 --> 00:20:53,700 you have people of certain groups that are being stopped at rates higher than others, 287 00:20:53,700 --> 00:20:58,870 particularly black and other minority ethnicity groups.” 288 00:20:58,870 --> 00:21:05,670 Okay, so, going back, why… into the initial question… 289 00:21:05,670 --> 00:21:10,670 If we have both freedom from discrimination and the right to privacy 290 00:21:10,670 --> 00:21:15,930 as these human rights how do we address this tension? 291 00:21:15,930 --> 00:21:18,270 And how do we make sure that we’re making the right decision in terms of 292 00:21:18,270 --> 00:21:23,440 which takes precedence? And so I came… or I’ve thought of 3 different reasons 293 00:21:23,440 --> 00:21:27,690 why this isn’t happening. The first being a series of legal challenges. 294 00:21:27,690 --> 00:21:31,740 Things that are preventing us from implementing this 295 00:21:31,740 --> 00:21:36,400 from a legal basis. And the first… you know there’s 3 exceptions 296 00:21:36,400 --> 00:21:39,240 that would allow for this data to be collected. 297 00:21:39,240 --> 00:21:43,350 (1) The first being if there’s a provision in EU directive that calls for collecting 298 00:21:43,350 --> 00:21:49,700 this kind of a data. And within that (2) if you have the consent of the person 299 00:21:49,700 --> 00:21:53,770 the data is subject, let’s say. Consent is kind of a difficult thing 300 00:21:53,770 --> 00:21:57,970 and we could have a whole conversation just about that on its own. 301 00:21:57,970 --> 00:22:00,950 If you’re being stopped by police officer to what extent can you actually consent 302 00:22:00,950 --> 00:22:06,660 to the data that’s being collected? But this is put in place 303 00:22:06,660 --> 00:22:10,510 as one of the mandatory legal requirements. 304 00:22:10,510 --> 00:22:16,050 Or (3) if there’s an exception in the hopefully soon to be finalized 305 00:22:16,050 --> 00:22:19,460 EU Data Protection law that allows for collecting data 306 00:22:19,460 --> 00:22:23,020 if it’s in the public interest. So you could argue that we need to be collecting 307 00:22:23,020 --> 00:22:28,920 this data because monitoring and evaluating discrimination 308 00:22:28,920 --> 00:22:34,480 is a problem that we need to solve as a society, right? 309 00:22:34,480 --> 00:22:38,810 Two: As a lot of people here at the conference are talking about: 310 00:22:38,810 --> 00:22:42,950 there’s a lot of distrust in terms of collecting data by the state. 311 00:22:42,950 --> 00:22:47,960 Particularly sensitive data. But I mean as many of us are already aware 312 00:22:47,960 --> 00:22:53,520 this data is already being collected. And this doesn’t mean that we should maybe 313 00:22:53,520 --> 00:22:57,680 collect more just for the sake of collecting data. 314 00:22:57,680 --> 00:23:01,460 But in terms of sensitive data – 315 00:23:01,460 --> 00:23:04,990 we’re collecting things also like medical data. And medical data sometimes 316 00:23:04,990 --> 00:23:08,720 is interesting for looking at trends in terms of the illnesses, 317 00:23:08,720 --> 00:23:14,850 and where illnesses spread. And you can look at this as also possibly a way of 318 00:23:14,850 --> 00:23:21,130 using sensitive data for highlighting and monitoring public problems. 319 00:23:21,130 --> 00:23:25,150 And, (3), we have these challenges in determining 320 00:23:25,150 --> 00:23:29,060 which kind of categories we should put in place. 321 00:23:29,060 --> 00:23:32,890 But, like the UK, if something were implemented in Germany 322 00:23:32,890 --> 00:23:37,090 I feel as though this would change over time as other groups also want their data 323 00:23:37,090 --> 00:23:43,490 to be collected… or not! 324 00:23:43,490 --> 00:23:48,400 So that’s kind of where I’m at. I think that 325 00:23:48,400 --> 00:23:51,480 there are no easy answers in terms of whether we should or should not do this. 326 00:23:51,480 --> 00:23:53,670 But I think that at the very least we should be starting to have 327 00:23:53,670 --> 00:23:56,500 these conversations. And I think that it’s important to start having these 328 00:23:56,500 --> 00:23:59,440 conversations with communities themselves who are being targeted, 329 00:23:59,440 --> 00:24:05,060 or feel they’re being profiled. So, thank you! 330 00:24:05,060 --> 00:24:16,320 *applause* 331 00:24:16,320 --> 00:24:20,420 Herald: It was an awesome talk. I think there might be 5 minutes for questions. 332 00:24:20,420 --> 00:24:24,620 There are mics over there and over there. And whoever has a question, 333 00:24:24,620 --> 00:24:28,140 like in the front rows, I can come walk to you. 334 00:24:28,140 --> 00:24:30,980 Question: Thank you very much. I’m just wondering in terms of… 335 00:24:30,980 --> 00:24:33,370 are you sort of creating this… 336 00:24:33,370 --> 00:24:34,690 Jeff: I’m sorry, I can’t hear you… 337 00:24:34,690 --> 00:24:37,260 Question: Sorry, of course… I’m sort of curious in terms of how you’re 338 00:24:37,260 --> 00:24:40,990 creating the disproportionate demographics where there will be birth, including 339 00:24:40,990 --> 00:24:44,520 other kinds of information, such as sex, age, time of day they’re stopped. 340 00:24:44,520 --> 00:24:46,300 Because there’s possibly unemployment bias as well… 341 00:24:46,300 --> 00:24:47,830 Jeff: I’m sorry, I still can’t actually hear you. 342 00:24:47,830 --> 00:24:52,510 Question: Sorry… whether it’d be worth including, say, other details 343 00:24:52,510 --> 00:24:56,350 about people, such as their sex, their age, maybe the time of day that 344 00:24:56,350 --> 00:25:01,880 these stops are happening. As there may be a bias towards the unemployed. 345 00:25:01,880 --> 00:25:06,760 If that’s possible, do you think, with the UK census data? 346 00:25:06,760 --> 00:25:10,350 Jeff: So you’re asking: Do I feel as though we should also be including 347 00:25:10,350 --> 00:25:15,090 other kinds of demographic data? Yeah. I mean I do, but I think that 348 00:25:15,090 --> 00:25:18,600 I shouldn’t be the one who’s deciding how to implement these programs. And I think 349 00:25:18,600 --> 00:25:23,190 that we should be speaking with the communities themselves 350 00:25:23,190 --> 00:25:26,530 and having them give their opinion. So if this is something that those communities 351 00:25:26,530 --> 00:25:30,260 who feel that they’re being targeted or being discriminated against 352 00:25:30,260 --> 00:25:33,800 want to include then I think that they should be taken into account. But 353 00:25:33,800 --> 00:25:37,470 I don’t know that I should be the one who’s deciding that. 354 00:25:37,470 --> 00:25:40,980 Herald: Okay, next question over there, please. 355 00:25:40,980 --> 00:25:45,230 Question: To this ratio you’ve been talking about: So you compare 356 00:25:45,230 --> 00:25:49,530 census data to – as you said in the definition 357 00:25:49,530 --> 00:25:53,510 in the first slide – perceived ethnicity or race. 358 00:25:53,510 --> 00:25:57,810 So it is an attribution of the persons themselves in a census 359 00:25:57,810 --> 00:26:01,730 compared to attribution per police officers. And those 360 00:26:01,730 --> 00:26:05,490 won’t necessarily match, I’m not sure. So I was just wondering 361 00:26:05,490 --> 00:26:08,980 whether you could comment on that a bit. And this is related 362 00:26:08,980 --> 00:26:13,130 to the second question when it comes about: We don’t get this data 363 00:26:13,130 --> 00:26:17,600 maybe from the police, because it’s difficult for the state to collect it. 364 00:26:17,600 --> 00:26:21,560 But maybe we could get the data from those which suffer from discrimination 365 00:26:21,560 --> 00:26:25,830 in the first place. So do you see any possibility for public platforms… 366 00:26:25,830 --> 00:26:29,930 So I was reminded of this idea from Egypt, HarassMap (?) 367 00:26:29,930 --> 00:26:34,140 which is about sexual harassment of women. That just made visible, 368 00:26:34,140 --> 00:26:37,710 with maps, similar to what you do, actually where this happened, 369 00:26:37,710 --> 00:26:42,860 when this happened, and how this happened. But it’s been the people themselves 370 00:26:42,860 --> 00:26:46,700 speaking out and making this heard. And I was wondering 371 00:26:46,700 --> 00:26:51,600 whether that may be another source of the data you would be needing for your work. 372 00:26:51,600 --> 00:26:55,750 Jeff: So the first question was talking about whether we should be using 373 00:26:55,750 --> 00:26:58,640 ‘self-identified’ vs. ‘perceived’, right? 374 00:26:58,640 --> 00:27:02,280 Yeah, I mean they may not line up, right? 375 00:27:02,280 --> 00:27:06,470 People can be perceived in a way different than they identify. 376 00:27:06,470 --> 00:27:10,450 Some groups in Germany are calling for both. 377 00:27:10,450 --> 00:27:14,500 They’re calling for kind of like a two-ticket mechanism 378 00:27:14,500 --> 00:27:19,750 where you have people who put how they self-identify 379 00:27:19,750 --> 00:27:24,040 and also how the Police are identifying them. If we’re looking for patterns 380 00:27:24,040 --> 00:27:27,580 of discrimination then it may actually be more interesting if we’re looking at 381 00:27:27,580 --> 00:27:31,580 how people are perceived. Then, how people self-identify. 382 00:27:31,580 --> 00:27:35,520 But I think it’s important to take both into account. And for the second question, 383 00:27:35,520 --> 00:27:38,170 I’m sorry, I kind of forgot what that was. 384 00:27:38,170 --> 00:27:42,010 Question: Like asking the people themselves for data 385 00:27:42,010 --> 00:27:45,770 when they suffer from discrimination or [are] being stopped more. 386 00:27:45,770 --> 00:27:49,790 Jeff: Yeah, no, I mean I think that’s a great idea. And there was a survey 387 00:27:49,790 --> 00:27:53,890 that was actually just done, that was doing just that! 388 00:27:53,890 --> 00:27:57,200 The findings haven’t been released, but it just finishes up. And it’s looking 389 00:27:57,200 --> 00:28:01,370 at different types of experiences of discrimination that people are having. 390 00:28:01,370 --> 00:28:05,600 There’s also organisations like social worker organisations 391 00:28:05,600 --> 00:28:08,730 that have been collecting this data for a long time. 392 00:28:08,730 --> 00:28:14,420 Having hundreds and hundreds of cases. Yeah, thanks! 393 00:28:14,420 --> 00:28:19,640 *postroll music* 394 00:28:19,640 --> 00:28:25,421 *Subtitles created by c3subtitles.de in the year 2016. Join, and help us!*