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Joy Buolamwini: How I'm fighting bias in algorithms
Džoj Bulamvini (Joy Buolamwini): Kako se borim protiv pristrasnosti u algoritmima
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Postdiplomac MIT-a, Džoj Bulamvini, je radila sa softverom za prepoznavanje lica kada je primetila problem. Softver nije prepoznavao njeno lice, jer ljudi koji su kodirali algoritam ga nisu naučili da identifikuje širok spektar boja kože i struktura lica. Sada je na misiji da se bori protiv pristrasnosti u mašinskom učenju, pojave koju naziva „kodirani pogled“. Radi se o govoru koji širi vidike, o potrebi za odgovornošću u kodiranju dok algoritmi ovladavaju sve većim brojem aspekata u našem životu.
Joy Buolamwini - Poet of code
Joy Buolamwini's research explores the intersection of social impact technology and inclusion. Full bio
Joy Buolamwini's research explores the intersection of social impact technology and inclusion. Full bio
Double-click the English transcript below to play the video.
00:13
Hello, I'm Joy, a poet of code,
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Zdravo, ja sam Džoj, pesnikinja kodova,
na misiji da zaustavim
neviđenu silu u usponu,
neviđenu silu u usponu,
00:16
on a mission to stop
an unseen force that's rising,
an unseen force that's rising,
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00:21
a force that I called "the coded gaze,"
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silu koju nazivam „kodirani pogled“,
00:24
my term for algorithmic bias.
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što je moj termin
za algoritamsku pristrasnost.
za algoritamsku pristrasnost.
Algoritamska pristrasnost, kao i ljudska,
ima za posledicu nepravednost.
ima za posledicu nepravednost.
00:27
Algorithmic bias, like human bias,
results in unfairness.
results in unfairness.
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00:31
However, algorithms, like viruses,
can spread bias on a massive scale
can spread bias on a massive scale
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Međutim, algoritmi, poput virusa,
mogu raširiti pristrasnost u ogromnoj meri
mogu raširiti pristrasnost u ogromnoj meri
00:37
at a rapid pace.
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velikom brzinom.
00:39
Algorithmic bias can also lead
to exclusionary experiences
to exclusionary experiences
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Algoritamska pristrasnost može dovesti
i do izloženosti isključivanju
i do izloženosti isključivanju
00:44
and discriminatory practices.
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i prakse diskriminacije.
00:46
Let me show you what I mean.
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Dozvolite da vam pokažem
šta hoću da kažem.
šta hoću da kažem.
00:48
(Video) Joy Buolamwini: Hi, camera.
I've got a face.
I've got a face.
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(Video) Džoj Buolamvini:
Zdravo, kamero. Imam lice.
Zdravo, kamero. Imam lice.
00:52
Can you see my face?
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Možeš li da vidiš moje lice?
00:54
No-glasses face?
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Lice bez naočara?
00:55
You can see her face.
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Možeš videti njeno lice.
A moje?
00:58
What about my face?
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01:03
I've got a mask. Can you see my mask?
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Imam masku. Možeš li da vidiš moju masku?
01:08
Joy Buolamwini: So how did this happen?
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Džoj Buolamvini: Pa, kako se ovo dogodilo?
01:10
Why am I sitting in front of a computer
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Zašto sedim ispred kompjutera
01:14
in a white mask,
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sa belom maskom,
01:15
trying to be detected by a cheap webcam?
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pokušavajući da me prepozna
jeftina kamera?
jeftina kamera?
01:19
Well, when I'm not fighting the coded gaze
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Kada se ne borim protiv kodiranog pogleda
01:21
as a poet of code,
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kao pesnikinja kodova,
01:23
I'm a graduate student
at the MIT Media Lab,
at the MIT Media Lab,
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postdiplomac sam
medijske laboratorije MIT-a
medijske laboratorije MIT-a
01:26
and there I have the opportunity to work
on all sorts of whimsical projects,
on all sorts of whimsical projects,
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i tamo imam priliku da radim
na raznim neobičnim projektima,
na raznim neobičnim projektima,
01:31
including the Aspire Mirror,
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uključujući „Ogledalo aspiracije“,
projekat koji sam sprovela
projekat koji sam sprovela
01:33
a project I did so I could project
digital masks onto my reflection.
digital masks onto my reflection.
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tako da mogu da projektujem
digitalne maske na svoj odraz.
digitalne maske na svoj odraz.
Tako bih ujutru,
ako želim da se osećam snažno,
ako želim da se osećam snažno,
01:38
So in the morning, if I wanted
to feel powerful,
to feel powerful,
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mogla da stavim lava.
01:40
I could put on a lion.
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Ako bih htela da podignem raspoloženje,
možda bih dobila citat.
možda bih dobila citat.
01:42
If I wanted to be uplifted,
I might have a quote.
I might have a quote.
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Koristila sam generički softver
za prepoznavanje lica
za prepoznavanje lica
01:45
So I used generic
facial recognition software
facial recognition software
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da bih napravila sistem,
01:48
to build the system,
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ali sam otkrila da ga je teško testirati
ukoliko ne nosim belu masku.
ukoliko ne nosim belu masku.
01:50
but found it was really hard to test it
unless I wore a white mask.
unless I wore a white mask.
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01:56
Unfortunately, I've run
into this issue before.
into this issue before.
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Nažalost, već sam ranije nailazila
na ovaj problem.
na ovaj problem.
Kada sam bila na osnovnim studijama
na Tehnološkom institutu u Džordžiji,
na Tehnološkom institutu u Džordžiji,
02:00
When I was an undergraduate
at Georgia Tech studying computer science,
at Georgia Tech studying computer science,
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gde sam studirala informatiku,
02:04
I used to work on social robots,
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radila sam na društvenim robotima,
02:07
and one of my tasks was to get a robot
to play peek-a-boo,
to play peek-a-boo,
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a jedan od mojih zadataka je bio
da navedem robota da se igra skrivanja,
da navedem robota da se igra skrivanja,
02:10
a simple turn-taking game
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jednostavne igre menjanja uloga
02:12
where partners cover their face
and then uncover it saying, "Peek-a-boo!"
and then uncover it saying, "Peek-a-boo!"
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u kojoj partneri pokrivaju lice,
a zatim ga otkriju i kažu: „Uja!“
a zatim ga otkriju i kažu: „Uja!“
02:16
The problem is, peek-a-boo
doesn't really work if I can't see you,
doesn't really work if I can't see you,
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Problem je što igra skrivanja
ne funkcioniše ako ne mogu da vas vidim,
ne funkcioniše ako ne mogu da vas vidim,
02:21
and my robot couldn't see me.
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a moj robot nije mogao da me vidi.
02:23
But I borrowed my roommate's face
to get the project done,
to get the project done,
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No, pozajmila sam lice svoje cimerke
da bih završila projekat,
da bih završila projekat,
02:27
submitted the assignment,
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predala sam zadatak,
02:29
and figured, you know what,
somebody else will solve this problem.
somebody else will solve this problem.
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i mislila sam: „Znate šta,
neko drugi će rešiti ovaj problem.“
neko drugi će rešiti ovaj problem.“
02:33
Not too long after,
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Nedugo potom,
02:35
I was in Hong Kong
for an entrepreneurship competition.
for an entrepreneurship competition.
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bila sam u Hongkongu
na takmičenju preduzetnika.
na takmičenju preduzetnika.
02:40
The organizers decided
to take participants
to take participants
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Organizatori su rešili da povedu učesnike
02:43
on a tour of local start-ups.
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u obilazak lokalnih startapova.
Jedan od startapova
imao je društvenog robota
imao je društvenog robota
02:45
One of the start-ups had a social robot,
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02:48
and they decided to do a demo.
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i rešili su da naprave demonstraciju.
02:50
The demo worked on everybody
until it got to me,
until it got to me,
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Demonstracija je radila kod svih
dok nisu stigli do mene
dok nisu stigli do mene
i možete verovatno pretpostaviti
šta se dogodilo.
šta se dogodilo.
02:53
and you can probably guess it.
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02:55
It couldn't detect my face.
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Nije mogao da prepozna moje lice.
02:58
I asked the developers what was going on,
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Pitala sam programere šta se dešava
i ispostavilo se da smo koristili
isti generički softver prepoznavanja lica.
isti generički softver prepoznavanja lica.
03:00
and it turned out we had used the same
generic facial recognition software.
generic facial recognition software.
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03:06
Halfway around the world,
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Preko pola sveta,
03:07
I learned that algorithmic bias
can travel as quickly
can travel as quickly
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saznala sam da algoritamska pristrasnost
može putovati toliko brzo
može putovati toliko brzo
03:11
as it takes to download
some files off of the internet.
some files off of the internet.
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koliko treba da se skine
nešto fajlova sa interneta.
nešto fajlova sa interneta.
Pa, šta se dešava?
Zašto se moje lice ne prepoznaje?
Zašto se moje lice ne prepoznaje?
03:15
So what's going on?
Why isn't my face being detected?
Why isn't my face being detected?
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Pa, moramo pogledati
kako mašini dajemo vid.
kako mašini dajemo vid.
03:18
Well, we have to look
at how we give machines sight.
at how we give machines sight.
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Kompjuterski vid koristi
tehnike mašinskog učenja
tehnike mašinskog učenja
03:22
Computer vision uses
machine learning techniques
machine learning techniques
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da bi prepoznao lica.
03:25
to do facial recognition.
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To funkcioniše tako što napravite
komplet za vežbanje sa primerima lica.
komplet za vežbanje sa primerima lica.
03:27
So how this works is, you create
a training set with examples of faces.
a training set with examples of faces.
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Ovo je lice. Ovo je lice. Ovo nije lice.
03:31
This is a face. This is a face.
This is not a face.
This is not a face.
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Vremenom možete naučiti kompjuter
kako da prepoznaje druga lica.
kako da prepoznaje druga lica.
03:34
And over time, you can teach a computer
how to recognize other faces.
how to recognize other faces.
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Međutim, ako kompleti za vežbanje
baš i nisu tako raznovrsni,
baš i nisu tako raznovrsni,
03:38
However, if the training sets
aren't really that diverse,
aren't really that diverse,
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svako lice koje previše odstupa
od uspostavljene norme
od uspostavljene norme
03:42
any face that deviates too much
from the established norm
from the established norm
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biće teže da se prepozna,
03:46
will be harder to detect,
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a to je ono što se događa sa mnom.
03:47
which is what was happening to me.
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Ali ne brinite, ima dobrih vesti.
03:49
But don't worry -- there's some good news.
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Kompleti za vežbanje
ne dolaze tek tako niotkuda.
ne dolaze tek tako niotkuda.
03:52
Training sets don't just
materialize out of nowhere.
materialize out of nowhere.
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Možemo ih stvoriti.
03:55
We actually can create them.
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Postoji mogućnost za stvaranje
kompleta za vežbu celokupnog spektra
kompleta za vežbu celokupnog spektra
03:56
So there's an opportunity to create
full-spectrum training sets
full-spectrum training sets
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koji odražavaju
bogatiji portret čovečanstva.
bogatiji portret čovečanstva.
04:01
that reflect a richer
portrait of humanity.
portrait of humanity.
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Videli ste u mojim primerima
04:04
Now you've seen in my examples
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da sam preko društvenih robota
04:07
how social robots
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04:09
was how I found out about exclusion
with algorithmic bias.
with algorithmic bias.
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saznala za isključivanje
kroz algoritamsku pristrasnost.
kroz algoritamsku pristrasnost.
04:13
But algorithmic bias can also lead
to discriminatory practices.
to discriminatory practices.
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Ali algoritamska pristrasnost
može dovesti i do prakse diskriminacije.
može dovesti i do prakse diskriminacije.
04:19
Across the US,
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Širom SAD-a,
04:20
police departments are starting to use
facial recognition software
facial recognition software
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policijske uprave počinju da koriste
softver za prepoznavanje lica
softver za prepoznavanje lica
04:25
in their crime-fighting arsenal.
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u svom arsenalu za borbu protiv kriminala.
04:27
Georgetown Law published a report
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Zakon Džordžtauna je objavio izveštaj
04:29
showing that one in two adults
in the US -- that's 117 million people --
in the US -- that's 117 million people --
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koji pokazuje da se jednoj
od dve odrasle osobe u SAD-u -
od dve odrasle osobe u SAD-u -
to je 117 miliona ljudi -
04:36
have their faces
in facial recognition networks.
in facial recognition networks.
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lice nalazi u mrežama
za prepoznavanje lica.
za prepoznavanje lica.
04:40
Police departments can currently look
at these networks unregulated,
at these networks unregulated,
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Odeljenja policije trenutno mogu
da neregulisano pregledaju ove mreže,
da neregulisano pregledaju ove mreže,
04:44
using algorithms that have not
been audited for accuracy.
been audited for accuracy.
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pomoću algoritama
kojima nije proverena tačnost.
kojima nije proverena tačnost.
04:48
Yet we know facial recognition
is not fail proof,
is not fail proof,
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Znamo da prepoznavanje lica nije bez mane,
04:52
and labeling faces consistently
remains a challenge.
remains a challenge.
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a naznačavanje lica stalno ostaje izazov.
04:56
You might have seen this on Facebook.
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Možda ste to videli na Fejsbuku.
04:58
My friends and I laugh all the time
when we see other people
when we see other people
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Moji prijatelji i ja se uvek smejemo
kad vidimo druge ljude
kad vidimo druge ljude
05:01
mislabeled in our photos.
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koji su pogrešno označeni
na našim fotografijama.
na našim fotografijama.
05:04
But misidentifying a suspected criminal
is no laughing matter,
is no laughing matter,
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Ali pogrešno identifikovanje
osumnjičenog zločinca nije za smejanje,
osumnjičenog zločinca nije za smejanje,
05:09
nor is breaching civil liberties.
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kao ni kršenje građanskih sloboda.
05:12
Machine learning is being used
for facial recognition,
for facial recognition,
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Mašinsko učenje se koristi
za prepoznavanje lica,
za prepoznavanje lica,
05:15
but it's also extending beyond the realm
of computer vision.
of computer vision.
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ali se takođe proteže
i van dometa kompjuterskog vida.
i van dometa kompjuterskog vida.
05:21
In her book, "Weapons
of Math Destruction,"
of Math Destruction,"
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U svojoj knjizi „Oružja
za matematičko uništenje“,
za matematičko uništenje“,
05:25
data scientist Cathy O'Neil
talks about the rising new WMDs --
talks about the rising new WMDs --
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naučnica u oblasti podataka Keti O'Nil
govori o usponu novih RMD-a,
govori o usponu novih RMD-a,
05:32
widespread, mysterious
and destructive algorithms
and destructive algorithms
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rasprostranjenih, misterioznih
i destruktivnih algoritama
i destruktivnih algoritama
koji se sve više koriste
za donošenje odluka
za donošenje odluka
05:36
that are increasingly being used
to make decisions
to make decisions
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koje utiču na sve više aspekata
našeg života.
našeg života.
05:39
that impact more aspects of our lives.
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Koga će zaposliti ili otpustiti?
05:42
So who gets hired or fired?
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Da li ćete dobiti taj kredit?
Da li ćete dobiti osiguranje?
Da li ćete dobiti osiguranje?
05:44
Do you get that loan?
Do you get insurance?
Do you get insurance?
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05:46
Are you admitted into the college
you wanted to get into?
you wanted to get into?
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Da li ste primljeni na fakultet
u koji ste želeli da upadnete?
u koji ste želeli da upadnete?
05:50
Do you and I pay the same price
for the same product
for the same product
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Da li vi i ja plaćamo istu cenu
za isti proizvod
za isti proizvod
05:53
purchased on the same platform?
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kupljen na istoj platformi?
05:56
Law enforcement is also starting
to use machine learning
to use machine learning
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Sprovođenje zakona takođe počinje
da koristi mašinsko učenje
da koristi mašinsko učenje
05:59
for predictive policing.
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za prediktivni rad policije.
06:02
Some judges use machine-generated
risk scores to determine
risk scores to determine
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Neke sudije koriste
mašinski generisane procene rizika
mašinski generisane procene rizika
da bi odredile koliko vremena
će neki pojedinac provesti u zatvoru.
će neki pojedinac provesti u zatvoru.
06:05
how long an individual
is going to spend in prison.
is going to spend in prison.
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Zato zaista treba da razmislimo
o ovim odlukama.
o ovim odlukama.
06:10
So we really have to think
about these decisions.
about these decisions.
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Jesu li pravedne?
06:12
Are they fair?
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A videli smo da algoritamske predrasude
06:13
And we've seen that algorithmic bias
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ne dovode nužno uvek do pravednih ishoda.
06:16
doesn't necessarily always
lead to fair outcomes.
lead to fair outcomes.
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Šta možemo da uradimo u vezi sa time?
06:20
So what can we do about it?
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Pa, možemo početi da razmišljamo
o tome kako da stvorimo inkluzivniji kod
o tome kako da stvorimo inkluzivniji kod
06:22
Well, we can start thinking about
how we create more inclusive code
how we create more inclusive code
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i da upotrebimo inkluzivne
postupke kodiranja.
postupke kodiranja.
06:25
and employ inclusive coding practices.
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To zapravo počinje od ljudi.
06:28
It really starts with people.
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Zato je bitno ko kodira.
06:31
So who codes matters.
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Da li kreiramo timove celokupnog spektra
sa različitim pojedincima
sa različitim pojedincima
06:33
Are we creating full-spectrum teams
with diverse individuals
with diverse individuals
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koji mogu da jedno drugome ispitaju
stvari za koje su slepi?
stvari za koje su slepi?
06:37
who can check each other's blind spots?
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06:40
On the technical side,
how we code matters.
how we code matters.
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Sa tehničke strane,
bitno je kako kodiramo.
bitno je kako kodiramo.
Da li uzimamo u obzir pravičnost
dok razvijamo sisteme?
dok razvijamo sisteme?
06:43
Are we factoring in fairness
as we're developing systems?
as we're developing systems?
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I najzad, bitno je zašto kodiramo.
06:47
And finally, why we code matters.
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Koristili smo alate računarskog stvaranja
da bismo otključali ogromno bogatstvo.
da bismo otključali ogromno bogatstvo.
06:50
We've used tools of computational creation
to unlock immense wealth.
to unlock immense wealth.
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Sada imamo priliku da otključamo
još veću ravnopravnost
još veću ravnopravnost
06:55
We now have the opportunity
to unlock even greater equality
to unlock even greater equality
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ako učinimo društvene promene prioritetom,
07:00
if we make social change a priority
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a ne da ih naknadno promišljamo.
07:03
and not an afterthought.
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07:06
And so these are the three tenets
that will make up the "incoding" movement.
that will make up the "incoding" movement.
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Dakle, ovo su tri principa
koji će sačinjavati pokret „inkodiranja“.
koji će sačinjavati pokret „inkodiranja“.
07:10
Who codes matters,
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Bitno je ko kodira,
07:12
how we code matters
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kako kodiramo
07:13
and why we code matters.
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i zašto kodiramo.
Stoga, da bismo išli u pravcu inkodiranja,
možemo početi da razmišljamo
možemo početi da razmišljamo
07:15
So to go towards incoding,
we can start thinking about
we can start thinking about
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07:18
building platforms that can identify bias
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o izgradnji platforma
koje mogu da identifikuju pristrasnost
koje mogu da identifikuju pristrasnost
07:22
by collecting people's experiences
like the ones I shared,
like the ones I shared,
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prikupljanjem iskustava ljudi
poput onih koje sam podelila,
poput onih koje sam podelila,
07:25
but also auditing existing software.
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ali i pregledom postojećeg softvera.
07:28
We can also start to create
more inclusive training sets.
more inclusive training sets.
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Takođe možemo početi da stvaramo
inkluzivnije komplete za vežbanje.
inkluzivnije komplete za vežbanje.
07:32
Imagine a "Selfies for Inclusion" campaign
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Zamislite kampanju „Selfiji za inkluziju“
07:34
where you and I can help
developers test and create
developers test and create
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u kojoj vi i ja možemo pomoći
programerima da testiraju i naprave
programerima da testiraju i naprave
inkluzivnije komplete za vežbanje.
07:38
more inclusive training sets.
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07:41
And we can also start thinking
more conscientiously
more conscientiously
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Takođe možemo početi
da savesnije razmišljamo
da savesnije razmišljamo
07:44
about the social impact
of the technology that we're developing.
of the technology that we're developing.
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o društvenom uticaju
tehnologije koju razvijamo.
tehnologije koju razvijamo.
07:49
To get the incoding movement started,
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Da bih otpočela pokret inkodiranja,
07:51
I've launched the Algorithmic
Justice League,
Justice League,
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pokrenula sam Ligu za algoritamsku pravdu,
07:54
where anyone who cares about fairness
can help fight the coded gaze.
can help fight the coded gaze.
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gde svako ko se brine o pravičnosti
može pomoći u borbi
protiv kodiranog pogleda.
protiv kodiranog pogleda.
08:00
On codedgaze.com, you can report bias,
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Na codedgaze.com
možete prijaviti pristrasnost,
možete prijaviti pristrasnost,
08:04
request audits, become a tester
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zatražiti proveru, postati tester
08:06
and join the ongoing conversation,
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i pridružiti se aktuelnom razgovoru,
08:09
#codedgaze.
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#codedgaze.
08:12
So I invite you to join me
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Pozivam vas da mi se pridružite
u stvaranju sveta
u kome tehnologija radi za sve nas,
u kome tehnologija radi za sve nas,
08:15
in creating a world where technology
works for all of us,
works for all of us,
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a ne samo za neke od nas,
08:18
not just some of us,
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sveta u kome cenimo inkluziju
i stavljamo u središte društvene promene.
i stavljamo u središte društvene promene.
08:20
a world where we value inclusion
and center social change.
and center social change.
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Hvala.
08:25
Thank you.
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(Aplauz)
08:26
(Applause)
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08:32
But I have one question:
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Ali imam jedno pitanje.
08:35
Will you join me in the fight?
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Hoćete li mi se pridružiti u borbi?
08:37
(Laughter)
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(Smeh)
08:39
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Joy Buolamwini - Poet of codeJoy Buolamwini's research explores the intersection of social impact technology and inclusion.
Why you should listen
Joy Buolamwini is a poet of code on a mission to show compassion through computation. As a graduate researcher at the MIT Media Lab, she leads the Algorithmic Justice League to fight coded bias. Her research explores the intersection of social impact technology and inclusion. In support of this work, Buolamwini was awarded a $50,000 grant as the Grand Prize winner of a national contest inspired by the critically acclaimed film Hidden Figures, based on the book by Margot Lee Shetterly.
Driven by an entrepreneurial spirit, Buolamwini's global interest in creating technology for social impact spans multiple industries and countries. As the inaugural Chief Technology Officer for Techturized Inc., a hair care technology company, and Swift Tech Solutions, a global health tech consultancy, she led software development for underserved communities in the United States, Ethiopia, Mali, Nigeria and Niger. In Zambia, she explored empowering citizens with skills to create their own technology through the Zamrize Project. In the United Kingdom, Buolamwini piloted a Service Year Initiative to launch Code4Rights which supports youth in creating meaningful technology for their communities in partnership with local organizations.
Through Filmmakers Collaborative, Buolamwini produces media that highlight diverse creators of technology. Her short documentary, The Coded Gaze: Unmasking Algorithmic Bias, debuted at the Museum of Fine Arts Boston and her pilot of the Code4Rights: Journey To Code training series debuted at the Vatican. She has presented keynote speeches and public talks at various forums including #CSforAll at the White House, Harvard University, Saïd Business School, Rutgers University, NCWIT, Grace Hopper Celebration and SXSWedu.
Buolamwini is a Rhodes Scholar, Fulbright Fellow, Google Anita Borg Scholar, Astronaut Scholar, A Stamps President's Scholar and Carter Center technical consultant recognized as a distinguished volunteer. She holds a master's degree in Learning and Technology from Oxford University and a bachelor's degree in Computer Science from the Georgia Institute of Technology. Buolamwini serves as a Harvard resident tutor at Adams House where she mentors students seeking scholarships or pursuing entrepreneurship.
Joy Buolamwini | Speaker | TED.com