Informational richness and its impact on algorithmic fairness: Informational richness and its impact on algorithmic...: M. Di Bello, R. Gong.
The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy m...
| Publicado en: | Philosophical Studies Vol. 182; no. 1; pp. 25 - 54 |
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| Autores principales: | , |
| Formato: | Artículo |
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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=182303658&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182303658 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: 182303658 10.1007/s11098-023-02004-7 ppf: 25 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Informational richness and its impact on algorithmic fairness: Informational richness and its impact on algorithmic...: M. Di Bello, R. Gong. aug: au: Di Bello, Marcello Gong, Ruobin affil: https://ror.org/03efmqc40 School of Historical, Philosophical and Religious Studies, Arizona State University, 975 S. Myrtle Ave, 85287, Tempe, AZ, USA https://ror.org/05vt9qd57 Department of Statistics, Rutgers University, 110 Frelinghuysen Road, Hill Center 404, 08854, Piscataway, NJ, USA su: Algorithms Computer simulation Fairness Centrality Philosophy sug: subj: Algorithms Computer simulation Fairness Centrality Philosophy keyword: Algorithmic fairness Bias-variance trade off Classification parity Conscientiousness Impossibility theorems Informational richness Predictive parity ab: The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of a computer simulation, we show that informational richness is the engine that drives improvements in the performance of a predictive algorithm, in terms of both accuracy and fairness. The centrality of informational richness suggests that classification parity, a popular criterion of algorithmic fairness, should be given relatively little weight. But we caution that the centrality of informational richness should be taken with a grain of salt in light of practical limitations, in particular, the so-called bias-variance trade off. 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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