Algorithmic fairness and resentment: Algorithmic fairness and resentment: B. Babic, Z. J. King.
In this paper we develop a general theory of algorithmic fairness. Drawing on Johnson King and Babic's work on moral encroachment, on Gary Becker's work on labor market discrimination, and on Strawson's idea of resentment and indignation as responses to violations of the demand for goodwill toward o...
| Publicado en: | Philosophical Studies Vol. 182; no. 1; pp. 87 - 120 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=182303659&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182303659 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318116 4L8 jtl: Philosophical Studies issn: 00318116 maglogo: N pubinfo: dt: Jan2025 vid: 182 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 182303659 10.1007/s11098-023-02006-5 ppf: 87 ppct: 33 formats: fmt: – @attributes: type: T – @attributes: type: P size: 968KB tig: atl: Algorithmic fairness and resentment: Algorithmic fairness and resentment: B. Babic, Z. J. King. aug: au: Babic, Boris Johnson King, Zoë affil: https://ror.org/03dbr7087 University of Toronto, St. George, Toronto, Canada https://ror.org/03vek6s52 Harvard University, Cambridge, USA su: Algorithms Fairness Resentment Philosophy Statistical evidence (Law) sug: subj: Algorithms Fairness Resentment Philosophy Statistical evidence (Law) keyword: Algorithmic ethics Bias Priors Statistical evidence ab: In this paper we develop a general theory of algorithmic fairness. Drawing on Johnson King and Babic's work on moral encroachment, on Gary Becker's work on labor market discrimination, and on Strawson's idea of resentment and indignation as responses to violations of the demand for goodwill toward oneself and others, we locate attitudes to fairness in an agent's utility function. In particular, we first argue that fairness is a matter of a decision-maker's relative concern for the plight of people from different groups, rather than of the outcomes produced for different groups. We then show how an agent's preferences, including in particular their attitudes to error, give rise to their decision thresholds. Tying these points together, we argue that the agent's relative degrees of concern for different groups manifest in a difference in decision thresholds applied to these groups. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Philosophical Studies is a copyright of Springer, 2025. All Rights Reserved. item: Philosophical Studies holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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