Xiga xigaHagaha xiga
Aqoonsiga Wajiga ee Tooska ah ee Goobaha Dadweynaha
Bulshada
HAGAHA Bulshada
Hooska Jinsiga iyo Xisaabinta Eexda Aqoonsiga Wajiga
Gender Shades waa daraasad la sameeyay 2018 oo ay qoreen Joy Buolamwini iyo Timnit Gebru, taas oo baaritaan ku samaysay nidaamyada ganacsi ee falanqaynta wajiga ee IBM, Microsoft iyo Face++.
Dhageyso hagahanAI-waa laga sheekeeyay · ~3 min
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Dulmar
Waxay ogaatay in kala soocidda jinsigoodu ay aad uga saxnaan yarayd haweenka maqaarka madow marka loo eego ragga maqaarka cad. Daraasaddu waxay muhiim u tahay sababtoo ah waxay ka dhigtay baaritaanka isgoyska (intersectional auditing), kaas oo natiijooyinka u kala jajabiya sifooyin isku dhafan halkii uu mid mid u eegi lahaa, hab caadi ah oo lagu soo bandhigo guuldarrooyinka AI ee qarsoon.
quusid qoto dheer
Joy Buolamwini, then at the MIT Media Lab, noticed that face detection software did not register her face until she put on a white mask. With Timnit Gebru, she designed a structured audit and published it at the 2018 Conference on Fairness, Accountability and Transparency. Existing benchmarks were dominated by lighter-skinned men, so they built their own, the Pilot Parliaments Benchmark. It contains 1,270 images of members of parliament from three African and three European countries, labeled by binary gender and by skin type on the dermatologists' Fitzpatrick scale. They tested the gender classification services of IBM, Microsoft and Face++. All three did better on men than on women and better on lighter skin than on darker skin. The worst results appeared where the two overlapped. Error rates for darker-skinned women reached about 35 percent for the worst system, compared with under 1 percent for lighter-skinned men. A single overall accuracy figure hid this gap. A 2019 follow-up by Inioluwa Deborah Raji and Buolamwini, "Actionable Auditing," found that the audited companies had narrowed their gaps after being publicly named. Vendors that had not been audited, including Amazon, showed similar disparities. Amazon disputed the methodology. A common misconception is that Gender Shades measured the face identification used by police. It measured gender classification. Evidence on identification came from NIST's 2019 demographic effects report, which tested around 200 algorithms. Many had higher false positive rates for some groups, including African and East Asian faces, often by large factors, while the most accurate algorithms showed much smaller differences. Real harm followed. Robert Williams, Nijeer Parks and Porcha Woodruff, all Black, were each wrongly arrested after facial recognition leads. In June 2020, IBM said it would leave the general-purpose facial recognition business, Amazon paused police use of Rekognition, and Microsoft said it would not sell to US police until a federal law was in place.
Dhaqangelinta Adduunka-dhabta ah
Kooxda wax soo saarka ayaa si gooni ah u soo bandhigta saxnaanta xaqiijinta wajiga ee haweenka maqaarka madow, ragga maqaarka madow, haweenka maqaarka cad iyo ragga maqaarka cad, halkii ay ka daabici lahaayeen hal tiro guud oo qarin karta koox hoosaad aan saxnayn.
Robert Williams, oo ah nin madow oo ku nool Detroit, ayaa la xiray 2020 ka dib markii aqoonsiga wajiga uu sawirkiisa shatiga darawalnimada ku waafajiyay muuqaal laga duubay tuugnimo. Dacwaddii waa la laalay, heshiis dambe ayaana beddelay xeerarka booliiska Detroit ee ku saabsan isticmaalka isbarbardhigga noocaas ah.
Ka hor inta aan la ansixin iibiyaha, xafiiska wax iibsiga ee magaalada ayaa hubiya natiijooyinka tijaabada dadweynaha ee NIST ee nooca algorithm-ka gaarka ah ee la bixiyay, sababtoo ah farqiga khaladaadka ayaa aad ugu kala duwan algorithms-ka.
Cilmi-baare ayaa dhisa qayb tijaabo ah oo lagu dheellitiray nooca maqaarka iyo jinsiga, isagoo raacaya habka Pilot Parliaments Benchmark, si uu u hubiyo moodel waji cusub oo loogu talagalay daldaloolada ka hor inta aan la sii deyn.
Mustaqbalka Gender Shades iyo Baaritaannada Eexda ee Aqoonsiga Wajiga
Aqoonsiga wajiga ayaa celcelis ahaan noqday mid aad u sax ah, tijaabooyinka joogtada ah ee NIST waxay muujinayaan in kala duwanaanshaha dadweynaha ay ku yaraanayaan algorithms-ka hormuudka ah, inkastoo aysan meesha ka baxayn, oo ay weli waaweyn yihiin kuwa kale oo badan. Siyaasaddu weli way kala qaybsan tahay. Qaar ka mid ah magaalooyinka Maraykanka ayaa xaddidaya isticmaalka dawladda, Xeerka AI ee EU-da wuxuu si adag u xaddidayaa aqoonsiga biometric-ga fog ee waqtiga-dhabta ah ee meelaha dadweynaha ay booliisku isticmaalaan, dhowr iibiyana way ka noqdeen qaar ka mid ah alaabada. Kiisaska xarigga aan caddaaladda ahayn ayaa sii wada inay ku cadaadiyaan waaxyaha booliiska inay ula dhaqmaan isbarbardhigga sidii tilmaamo oo kaliya. Habka Gender Shades, oo daabacaya natiijooyinka kala qaybsan oo magacaabaya iibiyeyaasha, ayaa hadda si ballaaran loo isticmaalaa si loo baaro nidaamyada kale ee AI, oo ay ku jiraan kuwa soo saara luqadda iyo sawirrada.
Ku dhaqangelinta
Maxay muhiim u tahay
- Khatarta iyo badbaadada. Masiibada iyo waxyeellada maalinlaha ah ee AI waxay labaduba ku xiran yihiin cidda fahmaysa khataraha iyo cidda wax ka qaban karta.
- Go'aamo cad. Aqoonta dadweynaha iyo aqoonta xirfadeed waxay qaabaysaa in siyaasadda badbaadada xooggani ay suurtogal tahay siyaasad ahaan.
- Ka gudub xiisaha. Sharaxaada cad waxay yareeyaan qabsashada buunbuuninta, shaybaarka PR, iyo masraxa anshaxa aan caddayn.
Iska ilaali
- Daawaynta khatarta jirta sida sci-fi halka awoodaha isku-dhisyada.
- jahawareerka badbaadada alaabta dusha sare leh oo la jaanqaadaysa madax-bannaani sare.
- Ka tagista daawadayaasha aan Ingiriisiga ahayn iyo kuwa aan khabiirka ahayn ee leh ilo tayo hooseeya oo keliya.
Tallaabooyinka xiga
- Kala soocida waxyeelada alaabta, si xun u isticmaalka, iyo luminta xakamaynta / khataraha khalkhalgelinta.
- Weydii caddaynta bedeli doonta aragtidaada waqtiyada iyo darnaanta.
- Ka door bida ilaha aasaasiga ah iyo qiimaynta la taaban karo ee sheegashooyinka suuq-geynta.
- Aqoonso hal waddo oo hawleed: xirfad, siyaasad, maalgelin, ama xirfado - kaliya maaha wacyigelin.
Sii wad Sahaminta
Warqad bilaash ah
Hel warbixinta maalinlaha ah ee AI
Saddex sheeko oo la xaqiijiyay oo AI ah maalin kasta oo shaqo ah, oo lagu qoray Ingiriis fudud. Bilaash had iyo jeer, aan xayaysiis lahayn.
Hal iimayl maalin kasta oo shaqo ah. Ka bax hal gujin. Marna ma iibinno ama ma wadaagno ciwaankaaga.
Is tijaabi
Qaado su'aalaha Gender Shades iyo Baaritaannada Eexda ee Aqoonsiga Wajiga
Fikrad degdeg ah oo ku saabsan jawaab kasta, iyo shahaado la wadaajin karo oo leh aqoonsi la xaqiijin karo markaad ka gudubto koorso.
Taageer waxbarashada AI bilaashka ah. AI Understanding waa 501(c)(3) aan faa'iido doon ahayn — xayaysiis la'aan, albaab lacag ah ma jiro, had iyo jeer. Samee deeq
Su'aalaha soo noqnoqda
Waa maxay Gender Shades iyo Baaritaannada Eexda ee Aqoonsiga Wajiga?
Gender Shades waa daraasad la sameeyay 2018 oo ay qoreen Joy Buolamwini iyo Timnit Gebru, taas oo baaritaan ku samaysay nidaamyada ganacsi ee falanqaynta wajiga ee IBM, Microsoft iyo Face++. Waxay ogaatay in kala soocidda jinsigoodu ay aad uga saxnaan yarayd haweenka maqaarka madow marka loo eego ragga maqaarka cad. Daraasaddu waxay muhiim u tahay sababtoo ah waxay ka dhigtay baaritaanka isgoyska, kaas oo natiijooyinka u kala jajabiya sifooyin isku dhafan halkii uu mid mid u eegi lahaa, hab caadi ah oo lagu soo bandhigo guuldarrooyinka AI ee qarsoon.
Adeegyada falanqaynta wajiga ee shirkaddee ayay daraasadda asalka ah ee Gender Shades baartay?
Daraasadda 2018 waxay tijaabisay IBM, Microsoft iyo Face++. Amazon waxaa la baaray 2019-kii markii la raacay.
Maxay ka koobnayd Pilot Parliaments Benchmark?
Heerka tixraacu wuxuu isticmaalay 1,270 sawir oo ka mid ah xildhibaannada lix waddan, kuwaas oo loo doortay inay dheellitiraan jinsiga iyo nooca maqaarka.
Miisaankee ayay cilmi-baarayaashu u isticmaaleen inay ku calaamadeeyaan nooca maqaarka?
Waxay isticmaaleen miisaanka Fitzpatrick ee laga isticmaalo maqaar-saarka. Shaqooyin dambe ayaa isticmaala miisaanno ballaaran sida miisaanka Monk.
Waa maxay hawsha ay Gender Shades dhab ahaan cabbiraysay?
Daraasadu waxay cabbiraysay kala-soocidda jinsiga. Fikir khaldan oo caadi ah ayaa ah inay cabbiraysay nidaamyada aqoonsiga ee ay isticmaalaan booliisku.
Waa kee koox-hoosaadkii lahaa heerarka qalad ee ugu sarreeya ee Gender Shades?
Qaladaadku waxay gaareen meeshii ugu sarreysay halka labada sifo ay is-dul-saarnaayeen, taas oo ahayd haweenka maqaarka madow, iyadoo gaartay ilaa 35 boqolkiiba nidaamkii ugu xumaa.
Sii wad waxbarashada
Tilmaamaha la xidhiidha
Tilmaamayaal badan ayaa loo doortay mawduucan