 Limbola instance spécifique ya bias na AI.
Ndakisa moko ya sikisiki ya bias na AI ezali kosalela technologie ya reconnaissance faciale bias ya race. Yango esalemaka ntango ba données ya formation oyo epesameli na ba systèmes AI ezali libosoliboso na bilili ya bato ya loposo ya pete, oyo esalaka ete algorithme ezala na mokakatano mpo na koyeba na bosikisiki mpe koyeba bato oyo bazali na ba tons ya loposo ya moindo. Ndakisa ya likambo oyo emonanaki na boyekoli moko oyo esalemaki na Institut national des normes et technologie (NIST) na 2019, epai batalelaki ba algorithmes 189 ya koyeba elongi oyo euti na bato 99 oyo basalaki yango. Boyekoli yango emonisaki ete ba algorithmes yango emonisaki ba taux ya likolo ya ba faux positifs mpo na bilongi ya Afro-Américains mpe ya Azia soki tokokanisi yango na bilongi ya mindele. Longola yango, basi ya Afro-Américains bayebanaki na ndenge ya mabe na mayele yango mbala mingi koleka ba démographie mosusu nyonso. Lolenge oyo ya bias na AI ekoki kozala na ba conséquences négatives ya makasi, lokola identification incorrecité na ba situations criminelles to exclusion na ba technologies mpe ba services mosusu. Pona kobundisa likambo oyo, ezali na tina pona ba développeurs koluka na molende ba données ya formation diverse pe representative pe ko évaluer pe ko régler continuellement ba bias oyo ekoki kozala na ba systèmes AI.