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← Jeff Deutch: Profiling (In)justice

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Showing Revision 32 created 10/06/2016 by Bar Sch.

  1. preroll music

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