Home / Teaching / Decolonising Education: From Theory to Practice / Interview with Shail Patel on decolonisation of, and anti-racism in, the mathematical sciences
Interview with Shail Patel on decolonisation of, and anti-racism in, the mathematical sciences
This video is an interview with Shail Patel who talks about decolonisation of, and anti-racism in, the mathematical sciences.
i<00:00:08.800> want<00:00:08.960> to<00:00:09.040> say<00:00:09.200> a<00:00:09.280> big<00:00:09.519> thank<00:00:09.840> you i want to say a big thank you
i want to say a big thank you out<00:00:10.400> to<00:00:10.559> the<00:00:10.639> black<00:00:10.960> lives<00:00:11.360> matter<00:00:12.080> people<00:00:12.719> i out to the black lives matter people i
out to the black lives matter people i think<00:00:13.120> really<00:00:13.440> we<00:00:13.599> wouldn't<00:00:13.920> be<00:00:14.160> here think really we wouldn’t be here
think really we wouldn’t be here having<00:00:14.880> this<00:00:15.040> conversation<00:00:15.759> now<00:00:16.080> if<00:00:16.240> it having this conversation now if it
having this conversation now if it wasn’t<00:00:16.560> for<00:00:16.720> all<00:00:16.880> the<00:00:16.960> great<00:00:17.199> work<00:00:17.520> they've wasn’t for all the great work they’ve wasn’t for all the great work they’ve done done
done for<00:00:19.359> me<00:00:20.400> i<00:00:20.640> think<00:00:20.800> the<00:00:20.960> whole<00:00:21.279> issue<00:00:21.520> of for me i think the whole issue of
for me i think the whole issue of decolonization<00:00:22.880> has<00:00:23.199> more decolonization has more
decolonization has more urgency<00:00:24.960> in<00:00:25.039> the<00:00:25.199> social<00:00:25.680> sciences urgency in the social sciences
urgency in the social sciences subjects<00:00:27.760> like<00:00:28.000> history<00:00:28.720> art<00:00:29.119> literature subjects like history art literature
subjects like history art literature whatever<00:00:30.080> where<00:00:30.320> you<00:00:30.400> can<00:00:30.560> really<00:00:30.880> see<00:00:31.119> strong whatever where you can really see strong
whatever where you can really see strong in<00:00:31.760> colonial<00:00:32.480> influences in colonial influences
in colonial influences in<00:00:34.559> the<00:00:34.640> mathematical<00:00:35.360> sciences<00:00:36.079> and<00:00:36.320> in in the mathematical sciences and in in the mathematical sciences and in stems stems
stems subjects<00:00:39.600> for<00:00:40.000> in<00:00:40.160> general<00:00:41.440> my<00:00:41.680> feeling<00:00:42.079> is subjects for in general my feeling is subjects for in general my feeling is that that
that anti-racism<00:00:44.640> is<00:00:44.960> more<00:00:45.360> of<00:00:45.520> a<00:00:45.680> burning anti-racism is more of a burning
anti-racism is more of a burning important<00:00:47.360> issue so<00:00:53.440> i'm<00:00:53.600> going<00:00:53.760> to<00:00:53.840> substitute<00:00:54.559> that so i’m going to substitute that
so i’m going to substitute that anti-racism<00:00:55.920> into<00:00:56.079> that<00:00:56.320> question anti-racism into that question
anti-racism into that question and<00:00:57.120> say<00:00:57.440> okay<00:00:57.680> well<00:00:57.840> there's<00:00:58.160> two<00:00:58.480> levels<00:00:59.359> at and say okay well there’s two levels at and say okay well there’s two levels at which which
which anti-racism<00:01:01.520> racism<00:01:02.239> features anti-racism racism features
anti-racism racism features one<00:01:03.600> is<00:01:03.760> at<00:01:03.840> the<00:01:04.000> level<00:01:04.320> of<00:01:04.479> structural<00:01:05.199> racism one is at the level of structural racism
one is at the level of structural racism you<00:01:06.400> have you have
you have you<00:01:06.880> look<00:01:07.040> at<00:01:07.200> sort<00:01:07.439> of<00:01:07.600> staffing<00:01:08.400> and<00:01:08.560> the<00:01:09.040> you you look at sort of staffing and the you
you look at sort of staffing and the you know<00:01:09.360> the<00:01:09.520> number<00:01:09.760> of<00:01:09.920> people<00:01:10.159> of<00:01:10.400> color know the number of people of color
know the number of people of color at<00:01:11.600> particularly<00:01:12.159> at<00:01:12.400> senior<00:01:12.960> staff<00:01:13.280> levels at particularly at senior staff levels
at particularly at senior staff levels within<00:01:14.000> university within university
within university you<00:01:15.439> look<00:01:15.600> at<00:01:15.759> university<00:01:16.479> entrance<00:01:17.119> and<00:01:17.200> the you look at university entrance and the
you look at university entrance and the difficulty<00:01:17.920> it<00:01:18.080> is<00:01:18.159> for<00:01:18.320> people<00:01:18.640> of difficulty it is for people of
difficulty it is for people of color<00:01:19.280> to<00:01:19.439> get<00:01:19.680> into<00:01:20.000> university<00:01:21.439> you<00:01:21.600> look<00:01:21.759> at color to get into university you look at
color to get into university you look at racism<00:01:22.479> now<00:01:22.799> been<00:01:22.960> reported<00:01:23.520> in<00:01:23.680> lecture racism now been reported in lecture racism now been reported in lecture halls halls
halls actually<00:01:25.520> in<00:01:26.080> you<00:01:26.240> know<00:01:26.640> in<00:01:27.200> student<00:01:27.680> halls actually in you know in student halls
actually in you know in student halls and<00:01:28.320> things<00:01:28.560> like<00:01:28.799> that<00:01:29.920> and<00:01:30.320> these<00:01:30.560> are and things like that and these are
and things like that and these are serious<00:01:31.040> structural<00:01:31.520> issues<00:01:31.840> which<00:01:32.159> do<00:01:32.320> not serious structural issues which do not
serious structural issues which do not affect<00:01:32.799> the<00:01:32.880> mathematical<00:01:33.600> sciences affect the mathematical sciences
affect the mathematical sciences alone<00:01:36.000> but<00:01:36.240> affect<00:01:36.720> them<00:01:37.200> as<00:01:37.680> well alone but affect them as well
alone but affect them as well as<00:01:38.560> everybody<00:01:39.040> else<00:01:39.920> so<00:01:40.079> i'm<00:01:40.159> not<00:01:40.320> going<00:01:40.479> to<00:01:40.560> go as everybody else so i’m not going to go
as everybody else so i’m not going to go into<00:01:41.119> that<00:01:41.360> in<00:01:41.520> more<00:01:41.759> detail<00:01:42.159> but<00:01:42.320> i<00:01:42.399> wanted<00:01:42.640> to into that in more detail but i wanted to
into that in more detail but i wanted to flag<00:01:43.200> up<00:01:43.439> that<00:01:43.920> level flag up that level
flag up that level i<00:01:44.720> think<00:01:45.040> is<00:01:45.280> probably<00:01:46.320> the<00:01:46.479> most<00:01:46.880> important i think is probably the most important i think is probably the most important level level
level to<00:01:48.720> engage<00:01:49.040> with<00:01:49.280> anti-racism<00:01:50.399> so<00:01:50.640> the<00:01:50.880> second to engage with anti-racism so the second to engage with anti-racism so the second level level
level is<00:01:52.640> the<00:01:52.960> level<00:01:53.280> of<00:01:53.439> curriculum<00:01:55.360> and<00:01:55.520> again<00:01:55.759> i'm is the level of curriculum and again i’m
is the level of curriculum and again i’m going<00:01:55.920> to<00:01:56.000> take<00:01:56.240> mathematical<00:01:56.960> sciences<00:01:57.759> as<00:01:57.920> a going to take mathematical sciences as a
going to take mathematical sciences as a sort<00:01:58.159> of<00:01:58.320> a<00:01:58.399> broad<00:01:58.799> area<00:01:59.119> it's<00:01:59.200> not<00:01:59.360> just sort of a broad area it’s not just
sort of a broad area it’s not just sort<00:02:00.159> of<00:02:00.320> pure<00:02:00.640> mathematics<00:02:01.360> or<00:02:01.439> even<00:02:01.759> applied sort of pure mathematics or even applied
sort of pure mathematics or even applied mathematics<00:02:02.719> but mathematics but
mathematics but computer<00:02:04.079> science<00:02:04.640> statistics<00:02:05.680> data computer science statistics data computer science statistics data modeling modeling
modeling things<00:02:07.040> like<00:02:07.280> this<00:02:08.560> artificial<00:02:09.119> intelligence things like this artificial intelligence
things like this artificial intelligence which<00:02:10.000> is<00:02:10.080> the<00:02:10.160> big<00:02:10.399> one which is the big one
which is the big one so<00:02:13.760> you<00:02:13.920> know<00:02:14.000> when<00:02:14.160> i<00:02:14.319> first<00:02:14.560> started so you know when i first started
so you know when i first started thinking<00:02:15.200> about<00:02:15.360> this<00:02:15.520> i<00:02:15.599> thought thinking about this i thought
thinking about this i thought how<00:02:16.000> can<00:02:16.480> an<00:02:16.640> equation<00:02:17.200> be<00:02:17.360> biased<00:02:18.480> you<00:02:18.560> know how can an equation be biased you know how can an equation be biased you know an an
an equation<00:02:19.520> is<00:02:19.920> an<00:02:20.080> equation<00:02:20.640> how<00:02:20.800> can<00:02:21.040> it<00:02:21.120> have equation is an equation how can it have
equation is an equation how can it have racism<00:02:22.480> in<00:02:22.640> it<00:02:22.800> and if<00:02:25.200> you<00:02:25.360> look<00:02:26.080> deeper<00:02:27.680> there if you look deeper there
if you look deeper there these<00:02:28.959> algorithms<00:02:30.080> are<00:02:30.239> not<00:02:30.400> just<00:02:30.640> biased<00:02:31.120> and these algorithms are not just biased and
these algorithms are not just biased and even<00:02:31.599> racist<00:02:32.160> but<00:02:32.319> have<00:02:32.640> serious even racist but have serious
even racist but have serious consequences<00:02:33.920> on<00:02:34.080> people's<00:02:34.560> lives consequences on people’s lives
consequences on people’s lives so<00:02:35.519> i<00:02:35.599> want<00:02:35.680> to<00:02:35.760> give<00:02:36.000> you<00:02:36.319> one<00:02:36.640> example<00:02:38.080> in so i want to give you one example in
so i want to give you one example in january<00:02:38.879> 2020 january 2020
january 2020 robert<00:02:40.640> julian<00:02:41.920> when<00:02:42.160> i<00:02:42.319> read<00:02:42.480> the<00:02:42.640> name robert julian when i read the name
robert julian when i read the name robert<00:02:43.440> julian<00:02:43.920> bought<00:02:44.160> jack<00:02:44.480> williams robert julian bought jack williams
robert julian bought jack williams was<00:02:45.840> handcuffed<00:02:46.560> on<00:02:46.720> his<00:02:46.879> driveway<00:02:47.599> in<00:02:47.840> front was handcuffed on his driveway in front
was handcuffed on his driveway in front of<00:02:48.239> his<00:02:48.400> wife<00:02:48.640> and<00:02:48.840> children of his wife and children
of his wife and children taken<00:02:50.480> off<00:02:50.640> to<00:02:50.879> prison<00:02:52.720> and taken off to prison and
taken off to prison and on<00:02:53.680> suspicion<00:02:54.480> of<00:02:54.720> stealing<00:02:55.360> five<00:02:55.840> watches on suspicion of stealing five watches
on suspicion of stealing five watches from<00:02:56.800> a<00:02:56.879> store<00:02:57.200> in<00:02:57.360> detroit from a store in detroit
from a store in detroit now<00:03:00.400> this<00:03:00.640> suspicion<00:03:01.599> and<00:03:01.680> he<00:03:01.840> was<00:03:01.920> taken<00:03:02.159> to<00:03:02.319> a now this suspicion and he was taken to a
now this suspicion and he was taken to a detention<00:03:02.959> center<00:03:03.360> kept<00:03:03.599> there<00:03:03.760> overnight detention center kept there overnight
detention center kept there overnight and<00:03:04.400> kept<00:03:04.640> for<00:03:04.800> 30<00:03:05.280> hours and kept for 30 hours
and kept for 30 hours and<00:03:06.560> this<00:03:06.800> was<00:03:07.200> all<00:03:07.599> done<00:03:08.480> on<00:03:08.640> the<00:03:08.879> basis and this was all done on the basis
and this was all done on the basis of<00:03:09.760> a<00:03:09.920> face<00:03:10.400> recognition<00:03:11.360> algorithm<00:03:12.239> alone of a face recognition algorithm alone
of a face recognition algorithm alone and<00:03:14.239> it's<00:03:14.400> the<00:03:14.720> first<00:03:15.200> time<00:03:15.599> that<00:03:16.159> we<00:03:16.319> know<00:03:16.560> of and it’s the first time that we know of
and it’s the first time that we know of that<00:03:16.879> at<00:03:16.959> least<00:03:17.200> that's<00:03:17.360> been<00:03:17.599> reported that at least that’s been reported
that at least that’s been reported that<00:03:19.040> somebody<00:03:19.599> has<00:03:19.840> been<00:03:20.400> arrested<00:03:21.680> in<00:03:21.840> plain that somebody has been arrested in plain that somebody has been arrested in plain daylight daylight
daylight and<00:03:23.599> put<00:03:23.920> into<00:03:24.159> detention<00:03:24.879> for<00:03:25.120> 30<00:03:25.440> hours<00:03:26.239> on and put into detention for 30 hours on
and put into detention for 30 hours on the<00:03:26.480> basis<00:03:26.879> of<00:03:26.959> the<00:03:27.040> prediction<00:03:27.519> of<00:03:27.680> an the basis of the prediction of an
the basis of the prediction of an algorithm<00:03:28.480> alone algorithm alone
algorithm alone now<00:03:31.040> in<00:03:31.200> the<00:03:31.440> he<00:03:31.599> had<00:03:31.760> his<00:03:31.920> dna<00:03:32.400> taken<00:03:32.879> he<00:03:33.040> was now in the he had his dna taken he was
now in the he had his dna taken he was fingerprinted<00:03:34.159> he<00:03:34.319> was<00:03:34.560> questioned fingerprinted he was questioned
fingerprinted he was questioned he<00:03:35.760> on<00:03:36.080> all<00:03:36.319> of<00:03:36.400> this<00:03:36.720> it's<00:03:37.120> and two<00:03:39.599> weeks<00:03:39.840> later<00:03:40.239> he<00:03:40.480> had<00:03:40.560> to<00:03:40.640> go<00:03:40.799> and<00:03:40.959> appear two weeks later he had to go and appear
two weeks later he had to go and appear in<00:03:41.440> court<00:03:41.760> and<00:03:41.920> of<00:03:42.000> course<00:03:42.239> the<00:03:42.400> case<00:03:42.640> was in court and of course the case was
in court and of course the case was dropped<00:03:43.040> because<00:03:43.280> there<00:03:43.519> wasn't dropped because there wasn’t
dropped because there wasn’t any<00:03:45.120> evidence<00:03:47.200> um any evidence um
any evidence um and<00:03:49.120> you<00:03:49.280> probably<00:03:49.680> already<00:03:50.000> guessed<00:03:51.120> that<00:03:51.360> of and you probably already guessed that of and you probably already guessed that of course course
course mr<00:03:52.640> williams<00:03:53.360> was<00:03:53.599> black<00:03:55.280> right<00:03:55.680> and<00:03:55.920> sadly mr williams was black right and sadly
mr williams was black right and sadly that’s<00:03:57.519> not<00:03:57.760> surprising<00:03:59.439> so<00:03:59.599> how<00:03:59.840> did<00:04:00.000> this that’s not surprising so how did this
that’s not surprising so how did this happen<00:04:00.560> how<00:04:00.720> did<00:04:00.879> this<00:04:01.120> come<00:04:01.360> about happen how did this come about
happen how did this come about and<00:04:03.200> there's<00:04:03.599> lots<00:04:03.920> of<00:04:04.080> press<00:04:04.560> lots<00:04:04.799> in<00:04:04.959> the and there’s lots of press lots in the and there’s lots of press lots in the press press
press about<00:04:06.400> racism<00:04:06.879> and<00:04:07.040> algorithms<00:04:07.599> and<00:04:07.680> if<00:04:07.760> you about racism and algorithms and if you
about racism and algorithms and if you just<00:04:08.000> do<00:04:08.159> a<00:04:08.239> google<00:04:08.560> search<00:04:08.879> on<00:04:09.040> racist just do a google search on racist
just do a google search on racist algorithms<00:04:10.239> or<00:04:10.319> racist<00:04:10.799> artificial algorithms or racist artificial algorithms or racist artificial intelligence intelligence
intelligence you’ll<00:04:12.640> find<00:04:13.200> oodles<00:04:13.599> of<00:04:13.680> stuff<00:04:13.920> so<00:04:14.159> just<00:04:14.319> this you’ll find oodles of stuff so just this
you’ll find oodles of stuff so just this one<00:04:14.879> example<00:04:15.280> how<00:04:15.439> did<00:04:15.599> it<00:04:15.680> come<00:04:15.920> about one example how did it come about
one example how did it come about well<00:04:18.000> modern<00:04:19.600> algorithms<00:04:20.320> used well modern algorithms used
well modern algorithms used in<00:04:21.440> anger<00:04:21.759> and<00:04:21.919> industry<00:04:22.479> and<00:04:22.560> by<00:04:22.720> the<00:04:22.880> police in anger and industry and by the police
in anger and industry and by the police where<00:04:23.600> else where else
where else ev<00:04:24.560> everywhere<00:04:25.040> else<00:04:25.360> a<00:04:25.520> lot<00:04:25.919> many<00:04:26.160> of<00:04:26.240> them ev everywhere else a lot many of them
ev everywhere else a lot many of them are<00:04:26.560> based<00:04:26.880> on<00:04:27.040> artificial<00:04:27.520> intelligence are based on artificial intelligence
are based on artificial intelligence neural<00:04:29.919> nets<00:04:30.240> you<00:04:30.320> may<00:04:30.479> have<00:04:30.560> heard<00:04:30.880> of<00:04:31.040> these neural nets you may have heard of these
neural nets you may have heard of these things<00:04:31.600> are<00:04:31.759> trained<00:04:32.479> on things are trained on
things are trained on data<00:04:34.320> and<00:04:34.479> the<00:04:34.639> algorithm<00:04:35.199> is<00:04:35.440> only<00:04:35.759> as<00:04:36.000> good data and the algorithm is only as good
data and the algorithm is only as good as<00:04:36.400> the<00:04:36.639> data as the data
as the data and<00:04:38.240> it<00:04:38.400> turns<00:04:38.720> out<00:04:38.960> in<00:04:39.040> these<00:04:39.360> kind<00:04:39.520> of<00:04:39.680> so and it turns out in these kind of so
and it turns out in these kind of so what<00:04:40.000> happened<00:04:40.400> in<00:04:40.479> the<00:04:40.639> store what happened in the store
what happened in the store was<00:04:41.919> that<00:04:42.080> they<00:04:42.240> had<00:04:42.479> a<00:04:42.960> grainy<00:04:43.600> cctv was that they had a grainy cctv
was that they had a grainy cctv image<00:04:45.440> of<00:04:45.520> the<00:04:45.600> person<00:04:45.919> who<00:04:46.080> did<00:04:46.240> the<00:04:46.320> robbery image of the person who did the robbery
image of the person who did the robbery and<00:04:47.440> that<00:04:47.680> came<00:04:48.000> up<00:04:48.080> with<00:04:48.400> a<00:04:48.479> positive<00:04:49.120> match and that came up with a positive match
and that came up with a positive match in<00:04:49.680> inverted<00:04:50.160> commerce in inverted commerce
in inverted commerce with<00:04:51.440> mr<00:04:51.759> williams<00:04:53.280> now with mr williams now
with mr williams now there<00:04:54.560> are<00:04:54.720> more<00:04:55.520> white<00:04:55.919> faces<00:04:56.720> that<00:04:56.880> these there are more white faces that these
there are more white faces that these algorithms<00:04:57.759> are<00:04:57.840> trained<00:04:58.160> on algorithms are trained on
algorithms are trained on black<00:04:59.120> faces<00:05:00.639> so<00:05:01.120> they<00:05:01.600> do<00:05:01.840> a<00:05:02.080> better black faces so they do a better
black faces so they do a better job<00:05:03.840> of<00:05:04.080> recognizing<00:05:05.039> white<00:05:05.360> faces job of recognizing white faces
job of recognizing white faces than<00:05:06.160> they<00:05:06.320> do<00:05:06.560> with<00:05:06.639> black<00:05:07.039> faces<00:05:07.440> this<00:05:07.680> is than they do with black faces this is
than they do with black faces this is well<00:05:08.960> um well um
well um well<00:05:09.600> reported<00:05:10.320> in<00:05:10.639> in<00:05:10.720> the<00:05:10.880> press<00:05:11.360> and<00:05:11.600> in well reported in in the press and in
well reported in in the press and in academic<00:05:13.039> journals<00:05:14.720> so academic journals so
academic journals so and<00:05:16.400> up<00:05:16.560> to<00:05:16.800> a<00:05:16.880> hundred<00:05:17.360> times<00:05:17.919> even<00:05:18.160> it's<00:05:18.320> been and up to a hundred times even it’s been and up to a hundred times even it’s been reported reported
reported in<00:05:19.520> one<00:05:19.759> article<00:05:20.080> in<00:05:20.240> the<00:05:20.400> mit<00:05:20.880> review<00:05:22.080> then in one article in the mit review then
in one article in the mit review then not<00:05:23.120> just<00:05:23.280> black<00:05:23.520> people<00:05:23.759> but<00:05:24.000> people<00:05:24.320> of not just black people but people of
not just black people but people of color<00:05:25.520> so color so
color so this<00:05:27.360> stuff<00:05:28.000> you<00:05:28.160> know<00:05:28.320> and<00:05:28.400> what<00:05:28.639> is<00:05:28.800> more this stuff you know and what is more this stuff you know and what is more worrying worrying
worrying is<00:05:31.039> that<00:05:31.280> similar<00:05:31.680> algorithms<00:05:32.400> are<00:05:32.479> being is that similar algorithms are being
is that similar algorithms are being used<00:05:32.960> for<00:05:33.120> predictive<00:05:33.520> policing used for predictive policing
used for predictive policing there’s<00:05:35.039> a<00:05:35.199> major<00:05:35.600> study<00:05:36.080> in<00:05:36.240> the<00:05:36.320> nyu<00:05:37.280> law there’s a major study in the nyu law there’s a major study in the nyu law review review
review called<00:05:39.759> um<00:05:40.160> let's<00:05:40.320> see<00:05:40.479> i'm<00:05:40.560> going<00:05:40.720> to<00:05:40.800> hear called um let’s see i’m going to hear
called um let’s see i’m going to hear dirty<00:05:41.440> data<00:05:42.000> bad dirty data bad
dirty data bad predictions<00:05:43.840> saying<00:05:44.160> that<00:05:44.639> um<00:05:45.039> similar predictions saying that um similar
predictions saying that um similar algorithms<00:05:46.000> that algorithms that
algorithms that predict<00:05:47.199> the<00:05:47.440> likelihood<00:05:48.320> of<00:05:48.479> somebody<00:05:48.880> to<00:05:49.039> go predict the likelihood of somebody to go predict the likelihood of somebody to go off off
off and<00:05:49.919> offend<00:05:50.320> again<00:05:51.440> are<00:05:51.600> based<00:05:52.160> on<00:05:52.560> police and offend again are based on police and offend again are based on police records records
records and<00:05:55.680> in<00:05:55.919> many<00:05:56.240> cases<00:05:56.800> they<00:05:56.960> have<00:05:57.120> found and in many cases they have found
and in many cases they have found evidence<00:05:57.919> that<00:05:58.160> these evidence that these
evidence that these records<00:05:59.600> themselves<00:06:00.080> have<00:06:00.400> been records themselves have been records themselves have been systematically systematically
systematically um<00:06:03.360> tampered<00:06:03.840> with<00:06:04.000> they're<00:06:04.240> corrupt<00:06:04.800> they're um tampered with they’re corrupt they’re
um tampered with they’re corrupt they’re unlawful<00:06:05.919> they're<00:06:06.240> dirty<00:06:06.800> if<00:06:06.960> you<00:06:07.120> like unlawful they’re dirty if you like
unlawful they’re dirty if you like and<00:06:09.199> so<00:06:09.759> what<00:06:10.080> then<00:06:10.479> the<00:06:10.639> predictive and so what then the predictive
and so what then the predictive algorithm<00:06:11.680> will<00:06:11.840> do<00:06:12.000> of<00:06:12.080> course<00:06:12.400> is algorithm will do of course is
algorithm will do of course is faithfully<00:06:15.199> mimic faithfully mimic
faithfully mimic imitate<00:06:16.319> copy<00:06:17.360> the<00:06:17.600> behavior<00:06:18.479> of imitate copy the behavior of
imitate copy the behavior of corrupt<00:06:19.840> police<00:06:20.319> officers<00:06:21.520> and<00:06:21.759> for<00:06:22.000> example corrupt police officers and for example
corrupt police officers and for example you<00:06:22.560> imagine<00:06:23.039> a<00:06:23.199> judge<00:06:23.520> deciding<00:06:24.080> whether<00:06:24.319> to you imagine a judge deciding whether to
you imagine a judge deciding whether to give<00:06:24.720> bail<00:06:25.039> or<00:06:25.280> not give bail or not
give bail or not they’ll<00:06:26.240> use<00:06:26.479> a<00:06:26.560> predictive<00:06:27.039> algorithm<00:06:27.600> like they’ll use a predictive algorithm like
they’ll use a predictive algorithm like that<00:06:28.639> and<00:06:28.880> it<00:06:29.039> will<00:06:29.280> be that and it will be
that and it will be racist<00:06:31.360> in<00:06:31.520> its<00:06:31.759> effect<00:06:32.240> and<00:06:32.319> this<00:06:32.560> will racist in its effect and this will
racist in its effect and this will affect<00:06:33.199> people's<00:06:33.600> lives affect people’s lives
affect people’s lives um<00:06:34.800> they<00:06:34.960> found<00:06:35.360> examples<00:06:35.759> of<00:06:35.840> this<00:06:36.080> i<00:06:36.240> think um they found examples of this i think
um they found examples of this i think in<00:06:37.039> chicago in chicago
in chicago uh<00:06:38.400> new<00:06:38.639> orleans<00:06:39.199> and<00:06:39.360> maricopa<00:06:40.000> county<00:06:40.960> in uh new orleans and maricopa county in uh new orleans and maricopa county in arizona arizona
arizona so<00:06:43.360> um<00:06:44.240> and<00:06:44.400> there's<00:06:44.720> as<00:06:44.880> i<00:06:44.960> said<00:06:45.120> there's<00:06:45.440> many so um and there’s as i said there’s many
so um and there’s as i said there’s many many<00:06:45.919> more<00:06:46.240> examples<00:06:46.960> um<00:06:47.840> and many more examples um and
many more examples um and well<00:06:48.960> what<00:06:49.199> can<00:06:49.360> be<00:06:49.599> done<00:06:50.080> that's<00:06:50.319> the<00:06:50.400> key well what can be done that’s the key
well what can be done that’s the key question<00:06:51.120> that's<00:06:51.280> where<00:06:51.520> you<00:06:51.759> started<00:06:52.240> yeah question that’s where you started yeah
question that’s where you started yeah what<00:06:52.960> can<00:06:53.120> be<00:06:53.280> done<00:06:53.599> about<00:06:53.919> this<00:06:54.880> a<00:06:55.039> lot<00:06:55.199> more what can be done about this a lot more
what can be done about this a lot more research<00:06:55.840> needs<00:06:56.000> to<00:06:56.080> be<00:06:56.240> done research needs to be done
research needs to be done on<00:06:57.120> the<00:06:57.360> mathematical<00:06:58.000> science<00:06:58.400> end<00:06:58.560> of on the mathematical science end of
on the mathematical science end of things<00:06:58.960> what<00:06:59.120> can<00:06:59.280> we<00:06:59.440> do things what can we do
things what can we do in<00:06:59.919> order<00:07:00.240> to<00:07:00.400> make<00:07:00.639> sure<00:07:00.960> that<00:07:01.520> algorithms in order to make sure that algorithms
in order to make sure that algorithms aren’t<00:07:02.560> biased um<00:07:05.520> but<00:07:05.759> ultimately<00:07:06.319> it's<00:07:06.560> not<00:07:06.960> just<00:07:07.360> about um but ultimately it’s not just about um but ultimately it’s not just about people people
people and<00:07:08.560> not<00:07:08.720> just<00:07:08.880> about<00:07:09.199> techies<00:07:09.680> i'm<00:07:09.759> sorry<00:07:10.000> i and not just about techies i’m sorry i
and not just about techies i’m sorry i meant<00:07:10.240> to<00:07:10.400> say<00:07:10.720> it's<00:07:10.880> not<00:07:11.120> just<00:07:11.360> about<00:07:11.599> the meant to say it’s not just about the
meant to say it’s not just about the techies<00:07:12.240> who<00:07:12.479> make<00:07:12.720> the<00:07:12.880> algorithms<00:07:13.520> about techies who make the algorithms about techies who make the algorithms about people people
people it’s<00:07:15.199> about<00:07:15.360> the<00:07:15.520> people<00:07:15.919> who<00:07:16.240> gather<00:07:16.560> the it’s about the people who gather the it’s about the people who gather the data data
data who<00:07:19.280> you<00:07:19.440> know<00:07:19.919> um<00:07:20.479> vet<00:07:20.800> the<00:07:20.960> data<00:07:21.440> for<00:07:21.759> how who you know um vet the data for how
who you know um vet the data for how you<00:07:22.960> know<00:07:23.280> good<00:07:23.520> it<00:07:23.680> is<00:07:24.319> it's<00:07:24.479> about<00:07:24.800> people you know good it is it’s about people
you know good it is it’s about people who<00:07:25.280> then<00:07:25.680> use<00:07:26.080> the<00:07:26.319> algorithms who then use the algorithms
who then use the algorithms when<00:07:27.599> they're<00:07:28.000> deployed<00:07:28.880> and<00:07:29.120> in<00:07:29.360> situ when they’re deployed and in situ
when they’re deployed and in situ and<00:07:31.120> so<00:07:32.080> what<00:07:32.319> needs<00:07:32.720> to<00:07:32.880> be<00:07:32.960> done<00:07:33.280> is<00:07:33.840> is and so what needs to be done is is
and so what needs to be done is is partly<00:07:34.720> at<00:07:34.800> the<00:07:34.960> mathematical<00:07:35.599> sciences<00:07:36.160> end partly at the mathematical sciences end
partly at the mathematical sciences end but<00:07:36.960> partly<00:07:37.360> you're<00:07:37.599> talking<00:07:37.919> about<00:07:38.319> a<00:07:38.560> global but partly you’re talking about a global
but partly you’re talking about a global kind<00:07:39.840> of<00:07:40.080> effort<00:07:41.199> in<00:07:41.599> standards kind of effort in standards
kind of effort in standards maybe<00:07:43.120> ethical<00:07:43.680> reviews<00:07:44.960> and<00:07:45.440> coupled<00:07:45.919> with maybe ethical reviews and coupled with maybe ethical reviews and coupled with that that
that a<00:07:47.360> massive<00:07:47.919> mindset<00:07:48.639> shift<00:07:49.440> in<00:07:49.599> terms<00:07:49.840> of<00:07:50.000> how a massive mindset shift in terms of how
a massive mindset shift in terms of how we<00:07:50.319> approach<00:07:50.720> this<00:07:50.960> area we approach this area
we approach this area again<00:07:52.240> i<00:07:52.319> made<00:07:52.560> a<00:07:52.639> list<00:07:52.879> of<00:07:53.039> loan<00:07:53.360> mortgage again i made a list of loan mortgage
again i made a list of loan mortgage applications<00:07:54.639> credit<00:07:54.960> risk<00:07:55.280> rating<00:07:55.759> fraud applications credit risk rating fraud applications credit risk rating fraud detection detection
detection granting<00:07:58.080> a<00:07:58.160> bear<00:07:58.400> we<00:07:58.560> touched<00:07:58.800> on<00:07:59.039> exam<00:07:59.440> break granting a bear we touched on exam break
granting a bear we touched on exam break prediction<00:08:00.479> you prediction you
prediction you may<00:08:01.199> remember<00:08:01.599> last<00:08:01.919> summer<00:08:02.639> it<00:08:02.720> was<00:08:02.879> that<00:08:03.039> big may remember last summer it was that big
may remember last summer it was that big fewer<00:08:03.599> and<00:08:03.680> that<00:08:03.840> was<00:08:03.919> just<00:08:04.160> a<00:08:04.240> simple fewer and that was just a simple
fewer and that was just a simple linear<00:08:05.680> mathematical<00:08:06.319> equation<00:08:06.800> no<00:08:06.960> clever linear mathematical equation no clever
linear mathematical equation no clever neural<00:08:07.680> nets<00:08:07.919> or<00:08:08.000> anything neural nets or anything
neural nets or anything insurance<00:08:09.360> premiums<00:08:09.919> job<00:08:10.240> application insurance premiums job application
insurance premiums job application streaming<00:08:11.680> screening streaming screening
streaming screening housing<00:08:13.199> benefits<00:08:13.840> medical<00:08:14.319> diagnosis housing benefits medical diagnosis
housing benefits medical diagnosis algorithms<00:08:15.520> border<00:08:15.919> security algorithms border security
algorithms border security very<00:08:17.440> interesting<00:08:17.919> example<00:08:18.400> a<00:08:18.479> campaigning very interesting example a campaigning
very interesting example a campaigning group<00:08:19.360> fox<00:08:19.680> club<00:08:20.000> working<00:08:20.240> with<00:08:20.479> the<00:08:20.560> joint group fox club working with the joint group fox club working with the joint council council
council for<00:08:21.680> the<00:08:21.759> welfare<00:08:22.160> of<00:08:22.319> immigrants<00:08:23.199> took<00:08:23.440> the for the welfare of immigrants took the
for the welfare of immigrants took the home<00:08:24.080> office home office
home office to<00:08:25.840> judicial<00:08:26.479> review<00:08:27.680> on<00:08:27.919> an<00:08:28.160> algorithm<00:08:28.720> that to judicial review on an algorithm that
to judicial review on an algorithm that they<00:08:28.960> were<00:08:29.199> using<00:08:29.599> that<00:08:29.759> they<00:08:30.000> claimed<00:08:30.400> was they were using that they claimed was
they were using that they claimed was racist<00:08:31.280> and<00:08:31.520> they racist and they
racist and they won<00:08:32.560> a<00:08:32.719> judicial<00:08:33.279> review<00:08:33.760> against<00:08:34.159> the<00:08:34.719> home won a judicial review against the home
won a judicial review against the home office<00:08:35.360> that's<00:08:35.599> a<00:08:35.680> major<00:08:36.080> thing office that’s a major thing
office that’s a major thing border<00:08:37.120> security<00:08:37.680> i<00:08:37.839> was<00:08:38.159> intruder<00:08:38.719> alarm border security i was intruder alarm
border security i was intruder alarm automated<00:08:39.839> news<00:08:40.159> feeds<00:08:40.479> in<00:08:40.560> summaries<00:08:40.959> i<00:08:41.039> mean automated news feeds in summaries i mean
automated news feeds in summaries i mean it’s<00:08:43.279> it's<00:08:43.519> a<00:08:43.680> long<00:08:43.919> long<00:08:44.240> long<00:08:44.480> list there’s<00:08:47.920> two<00:08:48.080> things<00:08:48.320> i<00:08:48.399> want<00:08:48.640> to<00:08:48.720> sell<00:08:48.959> to there’s two things i want to sell to
there’s two things i want to sell to give<00:08:49.200> an<00:08:49.360> example<00:08:49.760> but<00:08:49.920> i<00:08:50.000> also<00:08:50.240> want<00:08:50.399> to<00:08:50.480> give give an example but i also want to give
give an example but i also want to give a<00:08:50.880> ray<00:08:51.120> of<00:08:51.279> hope a ray of hope
a ray of hope that<00:08:52.640> actually<00:08:53.360> in<00:08:53.519> some<00:08:53.760> strange<00:08:54.480> way that actually in some strange way
that actually in some strange way if<00:08:55.760> the<00:08:55.920> racism<00:08:56.640> is<00:08:56.800> externalized<00:08:57.920> in<00:08:58.080> an if the racism is externalized in an if the racism is externalized in an algorithm algorithm
algorithm it<00:09:00.399> might<00:09:00.800> be<00:09:01.040> easier<00:09:01.519> for<00:09:01.760> humans<00:09:02.240> to<00:09:02.560> deal it might be easier for humans to deal it might be easier for humans to deal with with
with than<00:09:04.080> if<00:09:04.240> it's<00:09:04.640> internal<00:09:05.360> within<00:09:05.760> a<00:09:05.839> human than if it’s internal within a human
than if it’s internal within a human being<00:09:07.839> but being but
being but you<00:09:08.320> can<00:09:08.480> say<00:09:08.720> it's<00:09:08.880> not<00:09:09.120> me<00:09:09.360> gov<00:09:10.080> look<00:09:10.399> it's you can say it’s not me gov look it’s
you can say it’s not me gov look it’s this<00:09:10.880> algorithm<00:09:11.519> is<00:09:11.680> racist this algorithm is racist
this algorithm is racist okay<00:09:13.040> let's<00:09:13.360> fix<00:09:13.680> the<00:09:13.839> algorithm<00:09:14.480> then<00:09:15.279> right okay let’s fix the algorithm then right
okay let’s fix the algorithm then right the<00:09:16.640> second<00:09:16.959> thing<00:09:17.120> i<00:09:17.200> wanted<00:09:17.519> to<00:09:17.680> say<00:09:17.839> is<00:09:18.000> that the second thing i wanted to say is that
the second thing i wanted to say is that the<00:09:18.320> racism<00:09:18.959> doesn't<00:09:19.279> just<00:09:19.600> happen<00:09:20.000> at<00:09:20.160> a the racism doesn’t just happen at a
the racism doesn’t just happen at a conscious<00:09:20.959> level conscious level
conscious level it<00:09:21.760> can<00:09:22.000> happen<00:09:22.600> subconsciously<00:09:24.160> so<00:09:24.399> there's it can happen subconsciously so there’s
it can happen subconsciously so there’s an<00:09:24.800> example<00:09:25.279> with<00:09:25.519> twitter an example with twitter
an example with twitter that<00:09:27.040> it<00:09:27.440> um<00:09:28.160> uh<00:09:28.880> it<00:09:29.040> deployed that it um uh it deployed
that it um uh it deployed a<00:09:30.720> an<00:09:30.880> algorithm<00:09:32.080> that<00:09:32.399> in<00:09:32.640> a<00:09:32.880> in<00:09:33.040> an<00:09:33.200> image a an algorithm that in a in an image a an algorithm that in a in an image with with
with a<00:09:34.080> bunch<00:09:34.399> of<00:09:34.560> faces<00:09:35.040> in<00:09:35.200> it<00:09:35.440> it<00:09:35.519> would a bunch of faces in it it would
a bunch of faces in it it would automatically<00:09:36.640> zoom<00:09:36.959> in automatically zoom in
automatically zoom in on<00:09:37.600> the<00:09:38.160> most<00:09:38.480> important<00:09:39.120> phase<00:09:40.240> and on the most important phase and
on the most important phase and use<00:09:40.800> that<00:09:41.040> that's<00:09:41.360> for<00:09:41.519> example<00:09:41.920> if<00:09:42.080> you use that that’s for example if you
use that that’s for example if you wanted<00:09:42.560> a<00:09:43.040> an<00:09:43.200> image<00:09:43.519> for<00:09:43.760> your wanted a an image for your
wanted a an image for your own<00:09:45.040> home<00:09:45.440> screen<00:09:46.000> this<00:09:46.240> is<00:09:46.399> the<00:09:46.480> image<00:09:46.800> you own home screen this is the image you own home screen this is the image you wanted wanted
wanted and<00:09:48.800> what<00:09:49.120> one<00:09:49.760> uh<00:09:50.160> twitter<00:09:50.560> user<00:09:50.959> actually<00:09:51.200> a and what one uh twitter user actually a
and what one uh twitter user actually a complete<00:09:51.680> random<00:09:52.080> person<00:09:52.560> found complete random person found
complete random person found was<00:09:53.600> that<00:09:53.839> it<00:09:53.920> was<00:09:54.240> systematically<00:09:55.120> biased was that it was systematically biased
was that it was systematically biased towards<00:09:55.920> white<00:09:56.240> people<00:09:56.480> it<00:09:56.560> would<00:09:56.800> pick<00:09:57.120> out towards white people it would pick out
towards white people it would pick out white<00:09:58.000> people<00:09:59.360> and<00:09:59.440> they<00:09:59.600> did<00:09:59.839> an<00:10:00.000> excellent white people and they did an excellent white people and they did an excellent example example
example of<00:10:01.440> um<00:10:02.240> put<00:10:02.399> together<00:10:02.800> a<00:10:03.120> joint<00:10:03.519> image<00:10:03.760> of of um put together a joint image of of um put together a joint image of obama obama
obama and<00:10:05.200> mitch<00:10:05.440> mcconnell<00:10:07.040> and<00:10:07.440> mitch<00:10:07.680> mcconnell and mitch mcconnell and mitch mcconnell
and mitch mcconnell and mitch mcconnell who’s<00:10:08.240> the<00:10:08.399> leader<00:10:08.640> of<00:10:08.720> the who’s the leader of the
who’s the leader of the the<00:10:09.200> senate<00:10:09.760> the<00:10:09.920> republican<00:10:10.560> leader<00:10:10.880> in<00:10:10.959> the the senate the republican leader in the
the senate the republican leader in the senate<00:10:11.440> and senate and
senate and the<00:10:12.240> algorithm<00:10:13.040> invariably<00:10:14.000> picked<00:10:14.320> mitch the algorithm invariably picked mitch
the algorithm invariably picked mitch mcconnell<00:10:15.120> who's<00:10:15.360> white<00:10:15.760> over mcconnell who’s white over
mcconnell who’s white over president<00:10:17.040> obama<00:10:18.720> um president obama um
president obama um and<00:10:20.320> twitter<00:10:21.519> twitter<00:10:21.920> said<00:10:22.320> that<00:10:22.480> they and twitter twitter said that they
and twitter twitter said that they had<00:10:23.519> vetted<00:10:23.920> this<00:10:24.240> algorithm<00:10:25.200> for<00:10:25.680> racial had vetted this algorithm for racial had vetted this algorithm for racial buyers buyers
buyers and<00:10:27.760> they<00:10:27.920> hadn't<00:10:28.320> found<00:10:28.640> this<00:10:28.959> effect<00:10:29.839> and and they hadn’t found this effect and
and they hadn’t found this effect and they’ve<00:10:30.160> been<00:10:30.320> very<00:10:30.640> open<00:10:30.959> about<00:10:31.279> it<00:10:31.440> they've they’ve been very open about it they’ve they’ve been very open about it they’ve taken taken
taken it<00:10:32.160> away<00:10:32.480> they're<00:10:32.640> going<00:10:32.800> to<00:10:32.959> look<00:10:33.200> at<00:10:33.360> it it away they’re going to look at it
it away they’re going to look at it carefully<00:10:33.920> they're<00:10:34.079> going<00:10:34.160> to<00:10:34.320> put<00:10:34.560> all<00:10:34.720> this carefully they’re going to put all this
carefully they’re going to put all this out<00:10:35.760> on<00:10:36.320> the<00:10:36.720> um<00:10:37.120> on<00:10:37.200> the<00:10:37.360> internet<00:10:37.839> all<00:10:38.000> the out on the um on the internet all the
out on the um on the internet all the results<00:10:38.399> it's<00:10:38.560> just<00:10:38.800> a<00:10:38.880> couple<00:10:39.120> months<00:10:39.440> ago results it’s just a couple months ago
results it’s just a couple months ago this<00:10:39.839> happened this happened
this happened um<00:10:42.320> but<00:10:42.480> the<00:10:42.800> second<00:10:43.120> thing<00:10:43.360> that's um but the second thing that’s
um but the second thing that’s interesting<00:10:44.079> about<00:10:44.320> this<00:10:44.640> is<00:10:44.880> the<00:10:45.200> reason<00:10:45.680> it interesting about this is the reason it
interesting about this is the reason it came<00:10:46.240> about came about
came about is<00:10:47.360> because<00:10:47.680> they<00:10:48.079> based<00:10:48.880> their<00:10:49.519> um is because they based their um
is because they based their um their<00:10:50.880> algorithm<00:10:52.000> on<00:10:52.240> the<00:10:52.480> findings<00:10:53.200> of<00:10:53.360> the their algorithm on the findings of the
their algorithm on the findings of the psychological<00:10:54.320> process psychological process
psychological process um<00:10:55.360> called<00:10:55.680> eyeball<00:10:56.160> tracking<00:10:57.200> where<00:10:57.360> you um called eyeball tracking where you
um called eyeball tracking where you present<00:10:57.920> two<00:10:58.079> images<00:10:58.560> to<00:10:58.839> a present two images to a
present two images to a somebody<00:11:00.640> in<00:11:00.720> your<00:11:00.880> laboratory<00:11:02.079> and<00:11:02.160> you<00:11:02.399> see somebody in your laboratory and you see
somebody in your laboratory and you see where<00:11:03.040> their<00:11:03.279> eyeballs<00:11:03.920> focus<00:11:04.320> and where their eyeballs focus and
where their eyeballs focus and there’s<00:11:04.880> you<00:11:05.040> know<00:11:05.120> you<00:11:05.279> can<00:11:05.440> with<00:11:05.680> these there’s you know you can with these
there’s you know you can with these cameras<00:11:06.240> you<00:11:06.399> can<00:11:06.480> check<00:11:06.720> that<00:11:06.959> on<00:11:07.040> the<00:11:07.200> screen cameras you can check that on the screen
cameras you can check that on the screen and<00:11:09.320> subconsciously<00:11:10.480> and<00:11:10.560> this<00:11:10.800> is<00:11:10.880> the<00:11:11.040> issue and subconsciously and this is the issue
and subconsciously and this is the issue this<00:11:11.519> is<00:11:11.680> subconscious<00:11:12.839> processes this is subconscious processes
this is subconscious processes people<00:11:15.200> will<00:11:15.680> tend<00:11:16.560> to<00:11:16.720> gravitate people will tend to gravitate
people will tend to gravitate towards<00:11:18.160> white<00:11:18.560> faces towards white faces
towards white faces and<00:11:20.880> then<00:11:21.040> we<00:11:21.200> have<00:11:21.360> to<00:11:21.519> ask<00:11:21.920> why<00:11:22.160> is<00:11:22.240> that and then we have to ask why is that
and then we have to ask why is that happening<00:11:22.880> and<00:11:22.959> you're<00:11:23.120> talking<00:11:23.440> about happening and you’re talking about
happening and you’re talking about unpicking<00:11:24.240> a<00:11:24.320> whole<00:11:24.640> legacy unpicking a whole legacy
unpicking a whole legacy of<00:11:25.920> decades<00:11:26.640> of<00:11:26.839> television<00:11:28.160> film of decades of television film
of decades of television film advertising<00:11:30.240> images random<00:11:33.760> a<00:11:33.839> random<00:11:34.240> aside<00:11:34.640> for<00:11:34.880> example<00:11:35.200> did random a random aside for example did
random a random aside for example did you<00:11:35.519> know<00:11:35.680> that<00:11:35.920> a<00:11:36.000> quarter<00:11:36.399> of<00:11:36.560> cowboys<00:11:37.120> were black i<00:11:45.040> don't<00:11:45.200> want<00:11:45.360> to<00:11:45.440> get<00:11:45.680> into<00:11:45.920> hair<00:11:46.160> splitting i don’t want to get into hair splitting
i don’t want to get into hair splitting i<00:11:46.800> mean<00:11:47.120> i<00:11:47.200> think<00:11:47.440> what<00:11:47.680> people<00:11:47.920> are<00:11:48.079> doing<00:11:48.399> in i mean i think what people are doing in
i mean i think what people are doing in decolonization<00:11:49.760> is<00:11:49.920> great<00:11:50.320> and<00:11:50.480> i<00:11:50.639> support decolonization is great and i support
decolonization is great and i support all<00:11:51.200> their<00:11:51.519> efforts<00:11:51.920> that<00:11:52.000> they're<00:11:52.240> doing<00:11:53.120> and all their efforts that they’re doing and
all their efforts that they’re doing and what<00:11:53.600> people<00:11:53.839> are<00:11:53.920> doing what people are doing
what people are doing anti-racism<00:11:55.200> is<00:11:55.360> great<00:11:55.760> and<00:11:55.920> i<00:11:56.000> support<00:11:56.480> all anti-racism is great and i support all
anti-racism is great and i support all the<00:11:56.720> efforts<00:11:57.120> they're<00:11:57.440> doing the efforts they’re doing
the efforts they’re doing and<00:11:59.279> you<00:11:59.440> know<00:11:59.600> i<00:11:59.680> think<00:11:59.839> we<00:12:00.000> can<00:12:00.399> sometimes and you know i think we can sometimes
and you know i think we can sometimes you<00:12:01.680> know<00:12:01.839> we<00:12:01.920> can<00:12:02.079> get<00:12:02.240> go<00:12:02.480> fall<00:12:02.720> down<00:12:02.959> rabbit you know we can get go fall down rabbit
you know we can get go fall down rabbit holes<00:12:03.760> by<00:12:03.920> worrying<00:12:04.240> about<00:12:04.399> precisely<00:12:04.959> what holes by worrying about precisely what
holes by worrying about precisely what one<00:12:05.360> term<00:12:05.600> means<00:12:05.920> or<00:12:06.000> another<00:12:06.320> term<00:12:06.560> means one term means or another term means
one term means or another term means and<00:12:07.839> you<00:12:07.920> know<00:12:08.079> there<00:12:08.320> are<00:12:08.639> issues<00:12:09.200> involved and you know there are issues involved
and you know there are issues involved but<00:12:10.000> it's but it’s
but it’s really<00:12:11.519> what's<00:12:11.760> it<00:12:12.079> at<00:12:12.160> the<00:12:12.320> heart<00:12:12.639> of<00:12:12.720> the really what’s it at the heart of the
really what’s it at the heart of the matter<00:12:13.279> is<00:12:13.440> what<00:12:13.600> are<00:12:13.600> you<00:12:13.680> going<00:12:13.839> to<00:12:13.920> do<00:12:14.160> about matter is what are you going to do about matter is what are you going to do about it i’ll<00:12:17.839> second<00:12:18.320> start<00:12:18.639> with<00:12:18.800> the<00:12:18.880> second<00:12:19.279> part i’ll second start with the second part
i’ll second start with the second part because<00:12:19.839> there's<00:12:20.160> lots<00:12:20.399> of<00:12:20.560> people<00:12:20.880> working because there’s lots of people working
because there’s lots of people working in<00:12:21.360> the<00:12:21.519> area<00:12:21.920> and<00:12:22.000> that's<00:12:22.320> great<00:12:22.720> to<00:12:22.880> see in the area and that’s great to see
in the area and that’s great to see there’s<00:12:24.320> i<00:12:24.480> mean<00:12:24.639> again<00:12:24.880> i've<00:12:24.959> got<00:12:25.120> a<00:12:25.200> list there’s i mean again i’ve got a list
there’s i mean again i’ve got a list here<00:12:25.760> of<00:12:26.000> groups<00:12:26.480> involved<00:12:26.959> for<00:12:27.200> example here of groups involved for example
here of groups involved for example data<00:12:28.560> for<00:12:28.800> black<00:12:29.200> lives<00:12:30.160> ai<00:12:30.720> now<00:12:31.120> institute data for black lives ai now institute
data for black lives ai now institute black<00:12:32.160> in<00:12:32.399> computing<00:12:33.040> black<00:12:33.440> and<00:12:33.680> a<00:12:34.000> i black in computing black and a i
black in computing black and a i robo<00:12:35.360> hub<00:12:35.760> people<00:12:36.560> the<00:12:36.800> radical<00:12:37.360> ai<00:12:37.920> group robo hub people the radical ai group
robo hub people the radical ai group um<00:12:39.600> and<00:12:39.680> these<00:12:39.920> all<00:12:40.079> come<00:12:40.320> from<00:12:40.480> the<00:12:40.639> technical um and these all come from the technical
um and these all come from the technical side<00:12:41.600> and<00:12:41.839> there's<00:12:42.079> also<00:12:42.399> a<00:12:42.399> lot<00:12:42.639> of side and there’s also a lot of
side and there’s also a lot of newspaper<00:12:43.760> reporters<00:12:44.480> and<00:12:44.639> if<00:12:44.800> you<00:12:44.880> just<00:12:45.040> go newspaper reporters and if you just go
newspaper reporters and if you just go online<00:12:45.760> and online and
online and again<00:12:46.560> do<00:12:46.720> a<00:12:46.880> search<00:12:47.120> you'll<00:12:47.279> see<00:12:47.440> a<00:12:47.440> lot<00:12:47.600> of again do a search you’ll see a lot of
again do a search you’ll see a lot of newspaper<00:12:48.800> karen<00:12:49.200> how<00:12:49.440> it newspaper karen how it
newspaper karen how it um<00:12:50.240> mit<00:12:51.200> review<00:12:51.600> is<00:12:51.760> doing<00:12:52.079> great<00:12:52.320> stuff<00:12:52.720> there um mit review is doing great stuff there
um mit review is doing great stuff there and<00:12:53.760> the<00:12:53.839> new<00:12:54.000> york<00:12:54.240> times<00:12:54.639> brought<00:12:54.959> up<00:12:55.040> this and the new york times brought up this
and the new york times brought up this article<00:12:55.920> about<00:12:56.320> mr<00:12:56.639> williams article about mr williams
article about mr williams so<00:12:57.839> reporters<00:12:58.639> are<00:12:58.800> engaged<00:13:00.000> there's<00:13:00.399> also so reporters are engaged there’s also
so reporters are engaged there’s also campaign<00:13:01.600> groups<00:13:02.320> engaged<00:13:02.880> i<00:13:03.040> mentioned campaign groups engaged i mentioned campaign groups engaged i mentioned foxglove foxglove
foxglove and<00:13:04.800> joint<00:13:05.120> council<00:13:05.440> for<00:13:05.519> the<00:13:05.600> welfare<00:13:06.000> of<00:13:06.079> the and joint council for the welfare of the
and joint council for the welfare of the immigrants<00:13:06.959> but<00:13:07.120> there's immigrants but there’s
immigrants but there’s many<00:13:07.839> many<00:13:08.160> others<00:13:08.720> involved<00:13:09.279> in<00:13:09.360> this<00:13:09.680> in many many others involved in this in
many many others involved in this in terms<00:13:10.160> of terms of
terms of um<00:13:11.440> so<00:13:11.680> that's<00:13:12.000> good<00:13:12.639> um<00:13:13.760> probably<00:13:14.240> at<00:13:14.399> a um so that’s good um probably at a
um so that’s good um probably at a technical<00:13:15.040> level technical level
technical level i<00:13:15.680> think<00:13:15.839> within<00:13:16.160> the<00:13:16.320> mathematical<00:13:17.040> sciences i think within the mathematical sciences
i think within the mathematical sciences community<00:13:18.079> there's<00:13:18.480> more<00:13:18.720> that<00:13:18.959> needs<00:13:19.200> to<00:13:19.360> be community there’s more that needs to be
community there’s more that needs to be done<00:13:19.760> a<00:13:19.839> lot<00:13:20.160> more done a lot more
done a lot more and<00:13:21.279> i<00:13:21.360> know<00:13:21.600> that<00:13:21.839> your<00:13:22.079> colleague<00:13:22.480> eddie and i know that your colleague eddie
and i know that your colleague eddie wilson<00:13:23.360> in<00:13:23.519> engineering<00:13:24.000> maths wilson in engineering maths
wilson in engineering maths is<00:13:24.880> talking<00:13:25.200> about<00:13:26.560> doing<00:13:26.880> a<00:13:26.959> workshop is talking about doing a workshop
is talking about doing a workshop to<00:13:28.480> look<00:13:28.720> at<00:13:29.040> how<00:13:29.279> to<00:13:29.760> you<00:13:29.920> know<00:13:30.079> develop to look at how to you know develop
to look at how to you know develop mathematical<00:13:31.120> science<00:13:31.519> methods mathematical science methods
mathematical science methods to<00:13:32.720> rectify<00:13:33.600> and<00:13:34.000> well<00:13:34.160> first<00:13:34.480> to<00:13:34.720> spot to rectify and well first to spot
to rectify and well first to spot and<00:13:35.760> then<00:13:36.000> to<00:13:36.160> rectify<00:13:36.800> these<00:13:37.040> kinds<00:13:37.279> of<00:13:37.440> bias and then to rectify these kinds of bias
and then to rectify these kinds of bias in<00:13:38.320> uh in uh
in uh algorithms<00:13:40.320> um<00:13:41.199> so<00:13:41.360> yeah<00:13:41.600> as<00:13:41.760> for<00:13:41.920> myself algorithms um so yeah as for myself
algorithms um so yeah as for myself i<00:13:42.720> mean<00:13:42.880> i'm<00:13:43.040> retired<00:13:43.440> i've<00:13:43.600> been<00:13:43.680> retired<00:13:44.160> for i mean i’m retired i’ve been retired for
i mean i’m retired i’ve been retired for 10<00:13:44.560> years<00:13:45.440> so 10 years so 10 years so um<00:13:46.880> i'm<00:13:47.920> sort<00:13:48.160> of<00:13:48.639> i<00:13:48.800> talk<00:13:49.040> to<00:13:49.199> people um i’m sort of i talk to people
um i’m sort of i talk to people and<00:13:50.000> i'm<00:13:50.399> trying<00:13:50.639> to<00:13:50.880> influence<00:13:51.440> people<00:13:51.839> it's and i’m trying to influence people it’s
and i’m trying to influence people it’s it’s<00:13:52.399> what<00:13:52.639> our<00:13:52.880> hr<00:13:53.279> department<00:13:53.839> at<00:13:53.920> unilever it’s what our hr department at unilever
it’s what our hr department at unilever used<00:13:54.639> to<00:13:54.720> call<00:13:54.959> influencing<00:13:55.600> through<00:13:55.839> others used to call influencing through others
used to call influencing through others and<00:13:57.120> so<00:13:57.279> i'm<00:13:57.440> not<00:13:57.680> actively<00:13:58.320> engaged<00:13:58.959> in and so i’m not actively engaged in
and so i’m not actively engaged in research<00:14:00.839> myself things<00:14:05.680> have<00:14:06.000> morphed<00:14:08.320> i<00:14:08.560> think<00:14:08.800> it's<00:14:09.040> strange things have morphed i think it’s strange
things have morphed i think it’s strange i<00:14:09.519> mean i mean
i mean there’s<00:14:11.199> more<00:14:11.600> racist<00:14:12.399> incidences<00:14:13.519> than there’s more racist incidences than
there’s more racist incidences than there<00:14:13.839> were<00:14:14.079> back<00:14:14.399> then there were back then
there were back then but<00:14:15.680> there<00:14:15.839> were<00:14:16.160> less<00:14:16.800> challenges<00:14:17.839> to<00:14:18.240> the but there were less challenges to the
but there were less challenges to the structures<00:14:19.360> of<00:14:19.519> racism<00:14:20.160> back<00:14:20.480> then<00:14:20.639> than structures of racism back then than
structures of racism back then than there<00:14:21.040> are<00:14:21.279> now there are now
there are now so<00:14:22.880> if<00:14:23.040> you<00:14:23.120> like<00:14:23.360> it's<00:14:23.519> all<00:14:23.680> being<00:14:24.079> talked so if you like it’s all being talked
so if you like it’s all being talked about<00:14:24.800> more<00:14:25.360> and about more and
about more and people<00:14:26.320> are<00:14:26.480> also<00:14:27.920> to<00:14:28.079> a<00:14:28.079> certain<00:14:28.480> extent people are also to a certain extent
people are also to a certain extent moving<00:14:29.360> towards<00:14:29.839> action<00:14:30.320> more moving towards action more
moving towards action more um<00:14:32.320> but<00:14:32.560> also<00:14:32.800> there's<00:14:33.360> a<00:14:33.519> greater um but also there’s a greater
um but also there’s a greater instance<00:14:35.360> i<00:14:35.519> went<00:14:35.680> to<00:14:35.839> bristol<00:14:36.240> university<00:14:36.720> i instance i went to bristol university i
instance i went to bristol university i never<00:14:37.279> encountered never encountered
never encountered any<00:14:38.720> overt<00:14:40.320> act<00:14:40.639> of<00:14:40.800> racism any overt act of racism
any overt act of racism but<00:14:42.320> there<00:14:43.360> there<00:14:43.600> are<00:14:43.920> now<00:14:44.639> there<00:14:44.959> are but there there are now there are
but there there are now there are over<00:14:46.000> acts<00:14:46.320> of<00:14:46.480> racism<00:14:47.680> on<00:14:47.920> british over acts of racism on british
over acts of racism on british university<00:14:48.880> campuses university campuses
university campuses so<00:14:51.040> some<00:14:51.279> things<00:14:51.519> have<00:14:51.680> changed<00:14:52.160> some<00:14:52.320> things so some things have changed some things
so some things have changed some things have<00:14:52.639> got<00:14:52.800> better<00:14:53.120> some<00:14:53.279> things<00:14:53.440> have<00:14:53.519> got have got better some things have got
have got better some things have got worse<00:14:54.079> i<00:14:54.160> think<00:14:54.320> mostly<00:14:54.720> it's<00:14:55.040> evolved worse i think mostly it’s evolved
worse i think mostly it’s evolved and<00:14:57.040> it<00:14:57.199> sort<00:14:57.440> of<00:14:57.519> just<00:14:57.760> takes<00:14:58.800> it's<00:14:58.959> a and it sort of just takes it’s a
and it sort of just takes it’s a slightly<00:14:59.760> different<00:15:00.079> form<00:15:00.480> and<00:15:00.720> shape<00:15:01.120> but slightly different form and shape but
slightly different form and shape but we’ve<00:15:01.519> not<00:15:01.760> really<00:15:02.079> moved<00:15:02.399> forward<00:15:02.880> as<00:15:02.959> sort we’ve not really moved forward as sort
we’ve not really moved forward as sort of<00:15:03.199> just<00:15:03.360> going<00:15:03.680> around of just going around
of just going around skirting<00:15:05.199> around<00:15:05.600> the<00:15:05.760> edge<00:15:07.279> um skirting around the edge um
skirting around the edge um i<00:15:09.040> think<00:15:09.600> i'll<00:15:09.760> go<00:15:09.920> back<00:15:10.160> to<00:15:10.240> what<00:15:10.399> i<00:15:10.480> said<00:15:10.639> at i think i’ll go back to what i said at
i think i’ll go back to what i said at the<00:15:10.880> beginning<00:15:11.360> i<00:15:11.519> think<00:15:11.760> black<00:15:12.160> lives<00:15:12.560> matter the beginning i think black lives matter
the beginning i think black lives matter have<00:15:14.880> really<00:15:15.360> changed<00:15:15.760> the<00:15:15.920> landscape<00:15:16.639> in have really changed the landscape in
have really changed the landscape in terms<00:15:17.120> of terms of
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making people think about what positive<00:15:21.600> action<00:15:22.160> they<00:15:22.399> can<00:15:22.560> do<00:15:23.360> it's<00:15:23.600> not<00:15:23.760> a positive action they can do it’s not a
positive action they can do it’s not a question<00:15:24.320> more<00:15:24.560> analysis<00:15:25.279> more<00:15:25.519> reports<00:15:26.240> more question more analysis more reports more
question more analysis more reports more thinking<00:15:27.519> it's<00:15:27.839> what<00:15:28.160> action<00:15:28.639> can<00:15:28.800> we<00:15:38.839> take you
[Transcript of video is available at the bottom of this page]
In January 2020 Robert Julian-Borchak Williams of Detroit was arrested in front of his wife and children, interrogated, held for 30 hours in a detention centre, his DNA and fingerprints taken – all solely on the basis of a face recognition algorithm. He was later released and in court all charges were dropped. Mr Williams is black [1]. In this modern age we are surrounded by algorithms, equations that do things. These may be advertised in smart phones, smart TVs or smart cars, or hidden when we apply for a loan [2], search on the internet [3] or go to a hospital [4]. These algorithms typically revolve around data, and make predictions based on that data. When the data involves human beings, human biases and prejudices are in danger of affecting the behaviour of the algorithm. This can have potentially disastrous effects on people’s lives [5]. Through easy to understand recent examples Shail Patel explains how this arises and what we can do to counter these effects [6].
References
(1) Kashmir Hill, Wrongly accused by an Algorithm, New York Times, 24.6.20 https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html
(2) Bertrand, Jérémie, and Laurent Weill. “Do Algorithms Discriminate Against African Americans in Lending?.” Présentation à la Conférence Fintech and Digital Finance, Skema, Nice. 2019.
(3) Safiya Umoja Noble, (2020) Algorithms of Oppression: How Search Engines Reinforce Racism, NYU Press
(4) Ledford, Heidi. “Millions of black people affected by racial bias in health-care algorithms.” Nature, vol. 574, no. 7780, 2019, p. 608+
(5) Richardson, Rashida and Schultz, Jason and Crawford, Kate, Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice (February 13, 2019). 94 N.Y.U. Law Review Online 192 (2019), https://ssrn.com/abstract=3333423
(6) Kusner, Matt & Loftus, Joshua. (2020). The long road to fairer algorithms. Nature. 578. 34-36. 10.1038/d41586-020-00274-3.
(7) Julian Goldsmith, A Level Shambles has lessons for Justice, Law Society Gazette, 24.8.20 https://www.lawgazette.co.uk/commentary-and-opinion/a-level-shambles-has-lessons-for-justice/5105403.article
Shail Patel is retired, previously Research Director for Digital Consumers & Markets, Unilever R&D
This article is from the online course:
Decolonising Education: From Theory to Practice
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Decolonising Education: From Theory to Practice
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