Algorithmic bias and racial inequality: a critical review.
Most definitions of algorithmic bias and fairness encode decision-maker interests, such as profits, rather than the interests of disadvantaged groups (e.g. racial minorities): bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of automa...
| Published in: | Oxford Review of Economic Policy Vol. 40; no. 3; pp. 530 - 547 |
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| Format: | Literature Review |
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Oxford University Press / USA
Autumn2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=181096027&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 181096027 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0266903X 3DJ jtl: Oxford Review of Economic Policy issn: 0266903X maglogo: N pubinfo: dt: Autumn2024 vid: 40 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 181096027 10.1093/oxrep/grae031 ppf: 530 ppct: 17 formats: tig: atl: Algorithmic bias and racial inequality: a critical review. aug: au: Kasy, Maximilian affil: Department of Economics, University of Oxford su: Algorithmic bias Statistical bias Profit maximization Racial inequality Racial minorities sug: subj: Algorithmic bias Statistical bias Profit maximization Racial inequality Racial minorities keyword: AI discrimination fairness inequality ab: Most definitions of algorithmic bias and fairness encode decision-maker interests, such as profits, rather than the interests of disadvantaged groups (e.g. racial minorities): bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of automated decisions on the distribution of welfare, both across and within groups. The literature emphasizes some apparent contradictions between different notions of fairness, and between fairness and profits. These contradictions vanish, however, when profits are maximized. Existing work involves conceptual slippages between statistical notions of bias and misclassification errors, economic notions of profit, and normative notions of bias and fairness. Notions of bias nonetheless carry some interest within the welfare paradigm that I advocate for, if we understand bias and discrimination as mechanisms and potential points of intervention. pubtype: Academic Journal doctype: Literature Review src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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