0:00:00.000,0:00:09.329 preroll music 0:00:09.329,0:00:14.179 Herald: Welcome Jeff with a warm applause[br]on stage. He works for Tactical Tech 0:00:14.179,0:00:19.130 applause 0:00:19.130,0:00:22.860 and will talk about a bias in[br]data and racial profiling 0:00:22.860,0:00:25.870 in Germany compared with[br]the UK. It’s your stage! 0:00:25.870,0:00:30.090 Jeff: Right. Thank you! Yeah, okay! 0:00:30.090,0:00:33.320 My presentation is called[br]“Profiling (In)justice – 0:00:33.320,0:00:36.430 – Disaggregating Data by Race[br]and Ethnicity to Monitor 0:00:36.430,0:00:41.630 and Evaluate Discriminatory Policing”.[br]In terms of my background: 0:00:41.630,0:00:46.780 I’ve done research, doing[br]mostly quantitative research 0:00:46.780,0:00:50.730 around the issues of racial[br]discrimination for a long time. 0:00:50.730,0:00:55.960 In New York, at the Center for[br]Constitutional Rights I was working on 0:00:55.960,0:00:59.800 looking at trends and levels of 0:00:59.800,0:01:04.210 use-of-force by police against civilians,[br]and also on stop-and-search 0:01:04.210,0:01:08.601 against civilians. And then more[br]recently for the last 18 months or so 0:01:08.601,0:01:12.330 I’ve been working as a research[br]consultant at Tactical Tech, 0:01:12.330,0:01:16.360 looking at issues of data politics and[br]privacy. So this is kind of like a merger 0:01:16.360,0:01:21.960 of these 2 areas. In terms of what this[br]presentation is gonna be about: 0:01:21.960,0:01:26.900 there’s gonna be 3 takeaways. First, that 0:01:26.900,0:01:29.590 we’re dealing with the issues of privacy[br]and also [freedom from] discrimination. 0:01:29.590,0:01:34.869 And both are fundamental human rights.[br]But there’s tension between the two. 0:01:34.869,0:01:40.879 And important questions to think about[br]are: “When do privacy concerns exceed 0:01:40.879,0:01:46.490 or take precedence over those of[br]discrimination, or vice versa?” 0:01:46.490,0:01:53.400 Two: That data is political, both in the[br]collection and aggregation of data; 0:01:53.400,0:01:56.930 but also in terms of having the[br]categories of being created. 0:01:56.930,0:02:00.549 And then, three: That data ethics are[br]a complex thing, that things aren’t 0:02:00.549,0:02:05.090 so black-and-white all of the time.[br]So what is racial profiling? 0:02:05.090,0:02:08.910 The term originates from the US. 0:02:08.910,0:02:14.509 And it refers to when a police officer[br]suspects, stops, questions, arrests or… 0:02:14.509,0:02:17.079 you know, or… at other stages (?)[br]of the communal justice system 0:02:17.079,0:02:21.039 because of their perceived[br]race or ethnicity. After 9/11 0:02:21.039,0:02:26.609 it also refers to the profiling of Muslims[br]or people perceived to be Middle Eastern. 0:02:26.609,0:02:31.519 And in German there is no direct translation,[br]so the term ‘Racial Profiling’ (quotes) 0:02:31.519,0:02:36.859 is used a lot in parliamentary hearings[br]and also in court documents. 0:02:36.859,0:02:41.790 So the problem that we’re gonna talk[br]about is that because of the lack of data 0:02:41.790,0:02:46.309 in Germany there’s no empirical[br]evidence to monitor and evaluate 0:02:46.309,0:02:50.729 trends in discrimination.[br]This is creating problems 0:02:50.729,0:02:55.290 for both civil society in terms of looking[br]at these levels and trends over time, 0:02:55.290,0:02:58.199 but also from an individual perspective[br]it becomes difficult for people 0:02:58.199,0:03:02.259 to file complaints. In Germany the only[br]way to file a complaint officially 0:03:02.259,0:03:07.999 is to go to the police department,[br]which introduces power dynamics, 0:03:07.999,0:03:11.349 you know, challenges and additional[br]barriers. But also if you’re an individual 0:03:11.349,0:03:16.329 you have to show that there’s a trend,[br]right? That you are part of another, 0:03:16.329,0:03:19.759 a long standing story. And without this[br]data it becomes difficult to prove 0:03:19.759,0:03:24.049 that that’s happening.[br]So what we’re needing, 0:03:24.049,0:03:27.159 or what some people are calling[br]for, is having this data 0:03:27.159,0:03:32.850 at a state and a sort of national level.[br]And this ratio that I’m putting here, 0:03:32.850,0:03:36.019 referring to policing, is looking at the[br]rate at which people are stopped 0:03:36.019,0:03:41.629 over the census figure of the[br]demographic share of the population. 0:03:41.629,0:03:44.900 And you really need both; the first[br]being on the police side and 0:03:44.900,0:03:49.589 the second being on the census. So[br]that, you know, if you only have one, 0:03:49.589,0:03:52.170 if you only have the rate at which police[br]were stopping people then you actually 0:03:52.170,0:03:55.169 can’t see if this is discriminatory or[br]not. And if you only have the census 0:03:55.169,0:03:59.720 then you can’t see that, either.[br]So you really need both. 0:03:59.720,0:04:03.790 The European Commission, the International[br]Labour Organisation and academics are all 0:04:03.790,0:04:10.549 calling for these… the creation of[br]standardized and comparable data sets. 0:04:10.549,0:04:13.939 And I’m not gonna read these out,[br]but I can go back to them later 0:04:13.939,0:04:18.760 if you’re interested. But what I’m gonna[br]talk about is comparing the UK 0:04:18.760,0:04:23.290 to that of Germany. So in Germany, 0:04:23.290,0:04:28.130 in 1983 there was a census; or there[br]was an attempt to making a census. 0:04:28.130,0:04:31.970 But due to wide-spread resentment[br]and disenfranchisement, 0:04:31.970,0:04:37.190 fears of surveillance and lack of[br]trust in state data collection 0:04:37.190,0:04:42.490 there was a big boycott. Or people[br]deliberately filled in forms wrong. 0:04:42.490,0:04:45.280 In some cases there were even[br]bombings of statistical offices. 0:04:45.280,0:04:51.220 Or people spilled coffee over census[br]forms to try to deliberately ruin them. 0:04:51.220,0:04:55.530 As a couple of other presentations at the[br]conference have already said 0:04:55.530,0:04:59.250 this was found to be an[br]unconstitutional census. 0:04:59.250,0:05:01.990 Because of the way that[br]they were framing it. 0:05:01.990,0:05:08.520 Comparing the census to[br]household registrations. 0:05:08.520,0:05:14.900 And so the census was delayed until 1987, 0:05:14.900,0:05:19.930 which was the most recent census until[br]the most recent European one in 2011. 0:05:19.930,0:05:23.260 This Supreme Court decision[br]was really important 0:05:23.260,0:05:28.810 because it established this right[br]for informational self-determination. 0:05:28.810,0:05:33.040 Very important for privacy[br]in terms of Germany. 0:05:33.040,0:05:37.710 You know, until today. So what kinds[br]of information is being collected? 0:05:37.710,0:05:40.690 In Germany we have pretty standard kind[br]of demographic information things 0:05:40.690,0:05:45.200 like gender, age, income, religion. But[br]what I want to talk about in particular 0:05:45.200,0:05:49.200 is country origin and country citizenship. 0:05:49.200,0:05:53.660 Which are used to determine a person[br]of migration background. And 0:05:53.660,0:05:56.860 this term ‘person of migration background’[br]generally refers to whether you, 0:05:56.860,0:06:00.220 your parents or your grandparents[br]– the first, second or third generation – 0:06:00.220,0:06:03.960 come from a migrant background. Right, and 0:06:03.960,0:06:10.000 this term is used oftentimes as a proxy[br]for ethnic or for racial diversity in Germany. 0:06:10.000,0:06:15.050 And this is problematic because[br]you’re using citizenship as a proxy 0:06:15.050,0:06:20.080 for looking at racial and ethnic identity.[br]And it also ignores the experiences 0:06:20.080,0:06:23.450 and identities, the self identities[br]of people who don’t fall into 0:06:23.450,0:06:26.870 this ‘first, second or third generation’,[br]right? People who may identify 0:06:26.870,0:06:30.690 as Black German, let’s say. But[br]of fourth, fifth or sixth generation. 0:06:30.690,0:06:34.710 They’re just ignored in this[br]data set. So they fall out. 0:06:34.710,0:06:38.160 Also, it’s difficult to measure these at[br]a national level because each state 0:06:38.160,0:06:41.950 has different definitions of what[br]constitutes a migrant background. 0:06:41.950,0:06:44.790 So we don’t have this at a national level[br]but also within states there’s no way 0:06:44.790,0:06:49.370 to compare them. Of course, not[br]having that data doesn’t mean 0:06:49.370,0:06:53.840 that there’s no racism, right?[br]And so in 2005 e.g. we see 0:06:53.840,0:06:57.180 that neo-Nazi incidents have increased 25% 0:06:57.180,0:07:03.320 – the NSU case coming out but still[br]going on in court proceedings. 0:07:03.320,0:07:08.020 The xenophobic attacks but also the way[br]in which these crimes were investigated 0:07:08.020,0:07:13.670 – at a state and at a federal level –[br]and the way that it was botched, 0:07:13.670,0:07:17.900 in addition to showing that[br]racism now in general 0:07:17.900,0:07:22.230 is at a higher rate than it has been for[br]the last 30 years. And much more recently 0:07:22.230,0:07:26.710 seeing the rise in arson attacks on[br]refugee centers. There’s been 0:07:26.710,0:07:30.360 over 200 attacks this year so far.[br]You know, all of these showed 0:07:30.360,0:07:34.220 that not collecting this data doesn’t[br]mean that we don’t have a problem. 0:07:34.220,0:07:40.830 So, the UK by comparison: In 1981,[br]there was the Brixton riots, 0:07:40.830,0:07:45.670 in an area of London.[br]And these arose largely 0:07:45.670,0:07:50.320 because of resentment towards[br]the way that police were 0:07:50.320,0:07:53.550 carrying out what they called ‘Sus Laws’.[br]Or people being able to be stopped 0:07:53.550,0:07:58.080 on suspicion of committing[br]a crime, carrying drugs, 0:07:58.080,0:08:03.650 having a weapon etc. and so forth.[br]And so in the aftermath of the riot 0:08:03.650,0:08:07.550 they came up with this report called the[br]‘Scarman report’. And this found 0:08:07.550,0:08:11.150 that there is much disproportionality in[br]the way that Police were carrying out 0:08:11.150,0:08:16.280 their stop-and-search procedures.[br]So for the first time this required… 0:08:16.280,0:08:20.130 or one of the reforms that was[br]instituted was that UK Police started 0:08:20.130,0:08:26.750 to have to collect data on race[br]or ethnicity during the stops. 0:08:26.750,0:08:29.600 When they stop a person they have to start[br]collecting this data. And then you have 0:08:29.600,0:08:34.629 a baseline that’s being established.[br]Around the same time in the UK 0:08:34.629,0:08:38.729 we have the 1981 census. 0:08:38.729,0:08:41.809 And in society they were having[br]a lot of debates around 0:08:41.809,0:08:45.899 whether or not they wanted to have this… 0:08:45.899,0:08:49.971 collecting this baseline national level[br](?) figure, because we need these 2 things 0:08:49.971,0:08:56.260 for this ratio in order to monitor and[br]evaluate levels of discrimination. 0:08:56.260,0:09:00.240 But, you know, there was[br]a lot of opposition to this. 0:09:00.240,0:09:04.829 And many found it to be (quote)[br]“morally and politically objectionable”. 0:09:04.829,0:09:08.570 But not for the reason you’d think.[br]People found objections to it 0:09:08.570,0:09:13.230 not because of asking these question,[br]but because of the way that the question 0:09:13.230,0:09:17.190 was phrased, with the categories that[br]were being used. And they did surveys 0:09:17.190,0:09:21.399 between ’75 and about ’95, and found that 0:09:21.399,0:09:26.529 among marginalized communities[br]and in minority ethnicity groups 0:09:26.529,0:09:31.329 there was actually a lot of support[br]for collecting this kind of data. 0:09:31.329,0:09:35.250 They just wanted to have it phrased to[br]be different. And so ’91 they started 0:09:35.250,0:09:40.359 to collect the data. They put this[br]‘race question’ in. And here I have, 0:09:40.359,0:09:45.600 in 2011 – the most recent census –[br]some of the kinds of categories 0:09:45.600,0:09:50.049 that they wanted to also include.[br]And they’ve changed over time. 0:09:50.049,0:09:54.329 So e.g. like ‘White Irish people’ felt[br]that they also were discriminated against. 0:09:54.329,0:09:58.930 And they experienced things differently[br]than white British people, e.g. 0:09:58.930,0:10:03.231 So having things broken down[br]further would be helpful for them 0:10:03.231,0:10:09.720 in terms of highlighting discrimination[br]that each specific demographic faces. 0:10:09.720,0:10:14.379 So around that time ’91, ’93 we[br]have the murder of Stephen Lawrence 0:10:14.379,0:10:19.130 in an unprovoked racist attack. Nobody[br]was ever convicted of that. But 0:10:19.130,0:10:22.529 what’s important is that we have this[br]‘Macpherson report’ that came out. 0:10:22.529,0:10:27.290 And it developed a lot of recommendations,[br]70, and most of them were adopted. 0:10:27.290,0:10:31.529 One: to be collecting this at a national[br]level, and to be comparing these. 0:10:31.529,0:10:35.199 In 2011 they stopped mandating[br]that you had to collect this data, 0:10:35.199,0:10:38.709 at a national level. So none of the[br]data from then going forward 0:10:38.709,0:10:42.659 can actually be trusted. Some[br]forces continued to do it, 0:10:42.659,0:10:46.270 but not all of them. So you can’t actually[br]compare them between forces. 0:10:46.270,0:10:50.249 In the same year we have these London[br]riots. The Guardian and LSE put out 0:10:50.249,0:10:54.190 a report called “Reading the Riots”. Where[br]they did a lot of interviews with people 0:10:54.190,0:10:58.429 who participated. And they found that[br]most of the people who participated 0:10:58.429,0:11:03.569 had feelings of… that they[br]were mistreated by Police. 0:11:03.569,0:11:07.820 Or that there is racial discrimination[br]in terms of the policing practices. 0:11:07.820,0:11:11.760 That they weren’t being[br]treated with respect. 0:11:11.760,0:11:16.710 So to put some data to that:[br]Before this was removed 0:11:16.710,0:11:22.219 there… it was 2 different types of[br]stops in the UK. Those PACE stops, 0:11:22.219,0:11:25.769 where you stops with reasonable suspicion. 0:11:25.769,0:11:30.379 And among that you have e.g. black people[br]stopped at 7 times the rate of white people. 0:11:30.379,0:11:34.690 Asian people – Asian referring to (?)(?)(?)(?)[br]Southeast Asian in the UK – 0:11:34.690,0:11:39.430 at twice the rate. And ‘Section 60 stops’:[br]where you don’t have to actually have 0:11:39.430,0:11:43.399 reasonable suspicion. And when you don’t[br]need to have that you have much, much 0:11:43.399,0:11:51.840 higher rates. 26.6 times the rate of white[br]people black people are being stopped at. 0:11:51.840,0:11:54.069 But the State Department even coming[br]out and they’re saying: “There’s 0:11:54.069,0:11:59.730 no relationship between criminality[br]and race… criminality and ethnicity”. 0:11:59.730,0:12:02.450 In fact it’s like: If people are being[br]stopped at these rates it’s… 0:12:02.450,0:12:06.670 it’s in the wrong direction. You have[br]white males in particular who are 0:12:06.670,0:12:10.020 fending at higher rates. Who are using[br]drugs at a higher rate. Who are 0:12:10.020,0:12:15.060 possessing weapons at a higher rate.[br]But that’s not who’s being stopped. 0:12:15.060,0:12:19.579 There is a connection though between[br]race and ethnicity and poverty. 0:12:19.579,0:12:23.040 So you can see here, they call it like[br]BAME groups, or ‘Black, Asian and 0:12:23.040,0:12:27.220 Minority Ethnicity’. And you can see[br]that among like wealth and assets: 0:12:27.220,0:12:30.710 it’s much, much lower for non-white[br]households. Unemployment rates 0:12:30.710,0:12:36.149 are much higher as well.[br]Income is much lower. 0:12:36.149,0:12:39.809 So I like making maps. And I think[br]maps are really cool. ’Cause you can 0:12:39.809,0:12:44.269 tell stories when you overlay a lot[br]of data with it. So on the left 0:12:44.269,0:12:50.699 I put by different borough in London[br]where people are actually being stopped. 0:12:50.699,0:12:54.529 Per 1,000 people in 2012.[br]And on the right I put 0:12:54.529,0:12:58.789 where the crime is actually occurring.[br]And this is coming from UK Police. 0:12:58.789,0:13:02.009 And so you can see that where people[br]are being stopped isn’t exactly 0:13:02.009,0:13:05.799 where the crime is actually happening.[br]And if you’re seeing this stop-and-search 0:13:05.799,0:13:11.069 as a crime preventing tactic then we[br]have to question why this isn’t lining up. 0:13:11.069,0:13:15.449 Going back to this ratio: 0:13:15.449,0:13:19.459 earlier I mentioned like – having the rate[br]at which one group is being stopped 0:13:19.459,0:13:22.990 over that share of the total population. 0:13:22.990,0:13:26.000 And we can take it a step further[br]and we can compare that to… 0:13:26.000,0:13:29.029 between different demographic groups. 0:13:29.029,0:13:33.610 And when using census figures[br]combined with police figures, 0:13:33.610,0:13:38.500 we can do things like looking on the left.[br]I mean this disproportionality ratio, 0:13:38.500,0:13:41.260 so the rate at which black groups[br]as a share are stopped 0:13:41.260,0:13:45.839 versus the total population, compared[br]to white groups are stopped. 0:13:45.839,0:13:49.920 And you can see the darker areas[br]is where you have a higher rate. 0:13:49.920,0:13:56.230 So when we’re talking about those[br]‘7 times, or 26 times more likely’ 0:13:56.230,0:13:59.959 these are those areas that we’re[br]talking about. And so the darker areas: 0:13:59.959,0:14:05.909 you can see that when compared to poverty, 0:14:05.909,0:14:09.309 people are stopped… there’s[br]greater disproportionality ratios 0:14:09.309,0:14:13.030 in wealthier areas than there are in[br]poorer areas. And this is kind of 0:14:13.030,0:14:16.989 a way, you could say, almost[br]of perceiving people of colour 0:14:16.989,0:14:24.510 as others who shouldn’t belong in[br]these areas. It’s also… you can… 0:14:24.510,0:14:27.819 when combined with other census[br]information you can see that you have 0:14:27.819,0:14:32.069 more discrimination, you have more[br]disparities in areas that are more white 0:14:32.069,0:14:36.240 and also less racially diverse. 0:14:36.240,0:14:40.069 So this is kind of all on the[br]same kind of a message. 0:14:40.069,0:14:44.229 But if it works fine? – It doesn’t.[br]UK Police is saying that 0:14:44.229,0:14:49.499 at most they have a 6%[br]arrest rate of all stops. 0:14:49.499,0:14:52.970 And arrests are not conviction rates. 0:14:52.970,0:14:59.319 Looking for weapons we have like less[br]than 1% of a positive search rate. 0:14:59.319,0:15:03.350 And the European Human Rights[br]Commission e.g. has called for reform 0:15:03.350,0:15:06.999 of these practices. The UN has called[br]for reform of these practices. 0:15:06.999,0:15:12.559 And they instituted like[br]a reform that called for 0:15:12.559,0:15:19.039 having a 20% arrest quota. And so that[br]could either go positively or negatively. 0:15:19.039,0:15:21.649 Making a higher quota means that you[br]could be just arresting more people 0:15:21.649,0:15:26.439 that you’re stopping. More likely, or[br]hopefully it means that you have 0:15:26.439,0:15:31.550 a higher justification or grounds[br]for stopping a person. 0:15:31.550,0:15:35.430 So these are the kinds of things you can[br]do in the UK, with these kinds of data. 0:15:35.430,0:15:40.079 In Germany, you can’t. But I wanna[br]highlight there’s this one case 0:15:40.079,0:15:45.150 in Koblenz in 2010.[br]There was a student of… 0:15:45.150,0:15:50.050 unnamed, black student who[br]is stopped travelling on train, 0:15:50.050,0:15:53.310 and who was asked to show his ID.[br]And he refused. And he said: “No, 0:15:53.310,0:16:01.190 I’m not gonna do that. This is[br]reminiscent of Nazi era tactics”. 0:16:01.190,0:16:07.509 And so he was charged with slander.[br]And he was brought into court. 0:16:07.509,0:16:11.439 And the police officer, when it[br]was in court, said, (quote): 0:16:11.439,0:16:16.149 “I approach people that look like[br]foreigners, this is based on skin colour.” 0:16:16.149,0:16:20.209 And so this is for the first time[br]the police have admitted that 0:16:20.209,0:16:23.470 their grounds for doing immigration[br]related stops are based on 0:16:23.470,0:16:28.520 perceived race or ethnicity.[br]The judge sided with the police. 0:16:28.520,0:16:32.029 That this was good justification,[br]like it was good grounds. 0:16:32.029,0:16:36.779 But a higher court ruled[br]that that wasn’t the case. 0:16:36.779,0:16:38.540 They said: “Yeah,[br]this is unconstitutional, 0:16:38.540,0:16:42.339 you can’t actually do it,[br]it violates the constitution.” 0:16:42.339,0:16:46.249 No person shall be favoured or disfavoured[br]because of sex, parentage, race, 0:16:46.249,0:16:50.739 language, homeland, origin,[br]faith, religious… etc. 0:16:50.739,0:16:54.360 Just as a side note there’s been a large[br]movement to remove this term ‘race’ 0:16:54.360,0:16:58.410 from that part of the constitution[br]since it’s been put in. 0:16:58.410,0:17:02.189 And also the court dismissed the slander[br]charge. They said: “No, this student…” 0:17:02.189,0:17:07.160 like he’s actually able to critique[br]the police, you know, in this way. 0:17:07.160,0:17:10.660 But after we have the response[br]by the police union, 0:17:10.660,0:17:14.440 the head of the police union[br]at the time, who said (quote): 0:17:14.440,0:17:18.010 “The courts deal with the law in[br]an aesthetical pleasing way, but 0:17:18.010,0:17:21.760 they don’t make sure their judgments[br]match practical requirements”. 0:17:21.760,0:17:25.400 And so what this means is: we see[br]that according to the police union 0:17:25.400,0:17:28.870 – this isn’t official response, but this[br]is from the Police Union itself – 0:17:28.870,0:17:32.920 they say that we need to[br]profile. We need to do this. 0:17:32.920,0:17:38.750 Or else we aren’t able to do[br]immigration related stops. 0:17:38.750,0:17:43.470 That’s crazy. They also…[br]I mean, at the same time 0:17:43.470,0:17:46.840 when they were doing these parliamentary[br]hearings they institute these mandatory 0:17:46.840,0:17:50.660 inter cultural trainings for police[br]officers. And these are kind of 0:17:50.660,0:17:55.210 like a one-day training where[br]you go and learn all about 0:17:55.210,0:17:58.650 how to deal with people from different[br]cultures. But in some of the interviews 0:17:58.650,0:18:01.910 that I was doing they said: “Okay, well,[br]this isn’t an inter cultural issue. 0:18:01.910,0:18:05.730 This is a racism issue”. 0:18:05.730,0:18:08.250 People aren’t just coming from other[br]places. These are Germans, 0:18:08.250,0:18:11.000 these are people who grew up here. These[br]are people who live here. Who know 0:18:11.000,0:18:15.970 how to speak the language.[br]Who were born and raised… 0:18:15.970,0:18:19.260 And we need to be dealing[br]with this in a different way. 0:18:19.260,0:18:23.250 However, in this time, we see that[br]racial profiling has become part of 0:18:23.250,0:18:29.560 the national conversation. And so this[br]is the sticker that somebody put up 0:18:29.560,0:18:33.040 in Berlin, in a U-Bahn.[br]It says: “Attention…, 0:18:33.040,0:18:36.140 we practice RACIAL PROFILING while[br]checking the validity of your ticket”. 0:18:36.140,0:18:42.200 It’s not real, but it looks…[br]I think it’s kind of cool. 0:18:42.200,0:18:45.790 When they were doing this in[br]these Bundestag hearings… 0:18:45.790,0:18:50.260 they released data for Federal Police[br]for 2013. This is the first time 0:18:50.260,0:18:54.270 that we have any data that’s released.[br]No data has ever been released 0:18:54.270,0:18:57.430 based on State Police stops.[br]They say that they’re not actually 0:18:57.430,0:19:01.010 collecting the information, so they[br]don’t have anything to show. Of course 0:19:01.010,0:19:03.960 the figures that are released from the[br]Federal Police are not disaggregated 0:19:03.960,0:19:08.000 by race and ethnicity.[br]But what does this actually show? 0:19:08.000,0:19:17.270 So, most of the stops,[br]over 85% are border stops. 0:19:17.270,0:19:20.910 Border being within ca. 30 km[br]of the German border. 0:19:20.910,0:19:25.540 So this is actually taking into account[br]most of the German population. 0:19:25.540,0:19:29.470 But if we’re doing these immigration[br]related stops then… if we break it down 0:19:29.470,0:19:34.430 by offense – in the last two, these are[br]the immigration related offenses 0:19:34.430,0:19:38.910 that people are committing – and[br]we have less than, at most, 0:19:38.910,0:19:44.080 maybe 1% of people who[br]are found to be a positive, 0:19:44.080,0:19:48.100 meaning that they’re found to be violating[br]some kind of offense. It’s – again, 0:19:48.100,0:19:53.930 it’s not a conviction, right?[br]And people can challenge this. 0:19:53.930,0:19:56.550 E.g. like you don’t have to have your[br]ID on you in all times. You can 0:19:56.550,0:20:00.470 present it later, and the[br]charge can go away. 0:20:00.470,0:20:05.080 But if we have such low[br]rates of positive searches 0:20:05.080,0:20:10.980 then like why is this happening? Why[br]do we feel that with such good data, 0:20:10.980,0:20:18.950 and knowing, as good researchers,[br]why are we continuing this as a practice? 0:20:18.950,0:20:22.000 On one of the other interviews that I was[br]doing they found that okay well: 0:20:22.000,0:20:26.470 You know, we know this is ineffective.[br]But this has the effect of criminalizing 0:20:26.470,0:20:31.550 our communities. And[br]whether or not this is true 0:20:31.550,0:20:35.130 is an argument for why we should maybe[br]have this kind of data to show that 0:20:35.130,0:20:41.220 this is or is not actually occurring.[br]Of course, European Commission 0:20:41.220,0:20:46.490 against racism and intolerance and the UN[br]have said: “Well, even among this at most 0:20:46.490,0:20:50.021 1% positive rates these are[br]not being distributed evenly, and 0:20:50.021,0:20:53.700 you have people of certain groups that are[br]being stopped at rates higher than others, 0:20:53.700,0:20:58.870 particularly black and other[br]minority ethnicity groups.” 0:20:58.870,0:21:05.670 Okay, so, going back, why…[br]into the initial question… 0:21:05.670,0:21:10.670 If we have both freedom from[br]discrimination and the right to privacy 0:21:10.670,0:21:15.930 as these human rights how[br]do we address this tension? 0:21:15.930,0:21:18.270 And how do we make sure that we’re[br]making the right decision in terms of 0:21:18.270,0:21:23.440 which takes precedence? And so I came…[br]or I’ve thought of 3 different reasons 0:21:23.440,0:21:27.690 why this isn’t happening. The first[br]being a series of legal challenges. 0:21:27.690,0:21:31.740 Things that are preventing[br]us from implementing this 0:21:31.740,0:21:36.400 from a legal basis. And the first…[br]you know there’s 3 exceptions 0:21:36.400,0:21:39.240 that would allow for this[br]data to be collected. 0:21:39.240,0:21:43.350 (1) The first being if there’s a provision[br]in EU directive that calls for collecting 0:21:43.350,0:21:49.700 this kind of a data. And within that[br](2) if you have the consent of the person 0:21:49.700,0:21:53.770 the data is subject, let’s say.[br]Consent is kind of a difficult thing 0:21:53.770,0:21:57.970 and we could have a whole conversation[br]just about that on its own. 0:21:57.970,0:22:00.950 If you’re being stopped by police officer[br]to what extent can you actually consent 0:22:00.950,0:22:06.660 to the data that’s being collected?[br]But this is put in place 0:22:06.660,0:22:10.510 as one of the mandatory[br]legal requirements. 0:22:10.510,0:22:16.050 Or (3) if there’s an exception in[br]the hopefully soon to be finalized 0:22:16.050,0:22:19.460 EU Data Protection law that[br]allows for collecting data 0:22:19.460,0:22:23.020 if it’s in the public interest. So you[br]could argue that we need to be collecting 0:22:23.020,0:22:28.920 this data because monitoring[br]and evaluating discrimination 0:22:28.920,0:22:34.480 is a problem that we need[br]to solve as a society, right? 0:22:34.480,0:22:38.810 Two: As a lot of people here at[br]the conference are talking about: 0:22:38.810,0:22:42.950 there’s a lot of distrust in terms[br]of collecting data by the state. 0:22:42.950,0:22:47.960 Particularly sensitive data. But I mean[br]as many of us are already aware 0:22:47.960,0:22:53.520 this data is already being collected. And[br]this doesn’t mean that we should maybe 0:22:53.520,0:22:57.680 collect more just for the[br]sake of collecting data. 0:22:57.680,0:23:01.460 But in terms of sensitive data – 0:23:01.460,0:23:04.990 we’re collecting things also like medical[br]data. And medical data sometimes 0:23:04.990,0:23:08.720 is interesting for looking at trends[br]in terms of the illnesses, 0:23:08.720,0:23:14.850 and where illnesses spread. And you can[br]look at this as also possibly a way of 0:23:14.850,0:23:21.130 using sensitive data for highlighting[br]and monitoring public problems. 0:23:21.130,0:23:25.150 And, (3), we have these[br]challenges in determining 0:23:25.150,0:23:29.060 which kind of categories[br]we should put in place. 0:23:29.060,0:23:32.890 But, like the UK, if something[br]were implemented in Germany 0:23:32.890,0:23:37.090 I feel as though this would change over[br]time as other groups also want their data 0:23:37.090,0:23:43.490 to be collected… or not! 0:23:43.490,0:23:48.400 So that’s kind of where[br]I’m at. I think that 0:23:48.400,0:23:51.480 there are no easy answers in terms of[br]whether we should or should not do this. 0:23:51.480,0:23:53.670 But I think that at the very least[br]we should be starting to have 0:23:53.670,0:23:56.500 these conversations. And I think that[br]it’s important to start having these 0:23:56.500,0:23:59.440 conversations with communities[br]themselves who are being targeted, 0:23:59.440,0:24:05.060 or feel they’re being profiled.[br]So, thank you! 0:24:05.060,0:24:16.320 applause 0:24:16.320,0:24:20.420 Herald: It was an awesome talk. I think[br]there might be 5 minutes for questions. 0:24:20.420,0:24:24.620 There are mics over there and over[br]there. And whoever has a question, 0:24:24.620,0:24:28.140 like in the front rows,[br]I can come walk to you. 0:24:28.140,0:24:30.980 Question: Thank you very much.[br]I’m just wondering in terms of… 0:24:30.980,0:24:33.370 are you sort of creating this… 0:24:33.370,0:24:34.690 Jeff: I’m sorry, I can’t hear you… 0:24:34.690,0:24:37.260 Question: Sorry, of course… I’m sort[br]of curious in terms of how you’re 0:24:37.260,0:24:40.990 creating the disproportionate demographics[br]where there will be birth, including 0:24:40.990,0:24:44.520 other kinds of information, such as sex,[br]age, time of day they’re stopped. 0:24:44.520,0:24:46.300 Because there’s possibly[br]unemployment bias as well… 0:24:46.300,0:24:47.830 Jeff: I’m sorry, I still can’t[br]actually hear you. 0:24:47.830,0:24:52.510 Question: Sorry… whether it’d be[br]worth including, say, other details 0:24:52.510,0:24:56.350 about people, such as their sex, their[br]age, maybe the time of day that 0:24:56.350,0:25:01.880 these stops are happening. As there may[br]be a bias towards the unemployed. 0:25:01.880,0:25:06.760 If that’s possible, do you think,[br]with the UK census data? 0:25:06.760,0:25:10.350 Jeff: So you’re asking: Do I feel as[br]though we should also be including 0:25:10.350,0:25:15.090 other kinds of demographic data?[br]Yeah. I mean I do, but I think that 0:25:15.090,0:25:18.600 I shouldn’t be the one who’s deciding how[br]to implement these programs. And I think 0:25:18.600,0:25:23.190 that we should be speaking with[br]the communities themselves 0:25:23.190,0:25:26.530 and having them give their opinion. So if[br]this is something that those communities 0:25:26.530,0:25:30.260 who feel that they’re being targeted[br]or being discriminated against 0:25:30.260,0:25:33.800 want to include then I think that they[br]should be taken into account. But 0:25:33.800,0:25:37.470 I don’t know that I should be[br]the one who’s deciding that. 0:25:37.470,0:25:40.980 Herald: Okay, next question[br]over there, please. 0:25:40.980,0:25:45.230 Question: To this ratio you’ve been[br]talking about: So you compare 0:25:45.230,0:25:49.530 census data to – as you[br]said in the definition 0:25:49.530,0:25:53.510 in the first slide –[br]perceived ethnicity or race. 0:25:53.510,0:25:57.810 So it is an attribution of the[br]persons themselves in a census 0:25:57.810,0:26:01.730 compared to attribution per[br]police officers. And those 0:26:01.730,0:26:05.490 won’t necessarily match, I’m not[br]sure. So I was just wondering 0:26:05.490,0:26:08.980 whether you could comment on[br]that a bit. And this is related 0:26:08.980,0:26:13.130 to the second question when it comes[br]about: We don’t get this data 0:26:13.130,0:26:17.600 maybe from the police, because it’s[br]difficult for the state to collect it. 0:26:17.600,0:26:21.560 But maybe we could get the data from[br]those which suffer from discrimination 0:26:21.560,0:26:25.830 in the first place. So do you see any[br]possibility for public platforms… 0:26:25.830,0:26:29.930 So I was reminded of this[br]idea from Egypt, HarassMap (?) 0:26:29.930,0:26:34.140 which is about sexual harassment[br]of women. That just made visible, 0:26:34.140,0:26:37.710 with maps, similar to what you do,[br]actually where this happened, 0:26:37.710,0:26:42.860 when this happened, and how this happened.[br]But it’s been the people themselves 0:26:42.860,0:26:46.700 speaking out and making this[br]heard. And I was wondering 0:26:46.700,0:26:51.600 whether that may be another source of the[br]data you would be needing for your work. 0:26:51.600,0:26:55.750 Jeff: So the first question was talking[br]about whether we should be using 0:26:55.750,0:26:58.640 ‘self-identified’ vs. ‘perceived’,[br]right? 0:26:58.640,0:27:02.280 Yeah, I mean they may not line up, right? 0:27:02.280,0:27:06.470 People can be perceived in a way[br]different than they identify. 0:27:06.470,0:27:10.450 Some groups in Germany[br]are calling for both. 0:27:10.450,0:27:14.500 They’re calling for kind of like[br]a two-ticket mechanism 0:27:14.500,0:27:19.750 where you have people who[br]put how they self-identify 0:27:19.750,0:27:24.040 and also how the Police are identifying[br]them. If we’re looking for patterns 0:27:24.040,0:27:27.580 of discrimination then it may actually[br]be more interesting if we’re looking at 0:27:27.580,0:27:31.580 how people are perceived.[br]Then, how people self-identify. 0:27:31.580,0:27:35.520 But I think it’s important to take both[br]into account. And for the second question, 0:27:35.520,0:27:38.170 I’m sorry, I kind of forgot what that was. 0:27:38.170,0:27:42.010 Question: Like asking the[br]people themselves for data 0:27:42.010,0:27:45.770 when they suffer from discrimination[br]or [are] being stopped more. 0:27:45.770,0:27:49.790 Jeff: Yeah, no, I mean I think that’s a[br]great idea. And there was a survey 0:27:49.790,0:27:53.890 that was actually just done,[br]that was doing just that! 0:27:53.890,0:27:57.200 The findings haven’t been released,[br]but it just finishes up. And it’s looking 0:27:57.200,0:28:01.370 at different types of experiences of[br]discrimination that people are having. 0:28:01.370,0:28:05.600 There’s also organisations like[br]social worker organisations 0:28:05.600,0:28:08.730 that have been collecting[br]this data for a long time. 0:28:08.730,0:28:14.420 Having hundreds and hundreds[br]of cases. Yeah, thanks! 0:28:14.420,0:28:19.640 postroll music 0:28:19.640,0:28:25.421 Subtitles created by c3subtitles.de[br]in the year 2016. Join, and help us!