Epistemic injustice and data science technologies.
Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or...
| Publicado en: | Synthese Vol. 200; no. 2; pp. 1 - 22 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Apr2022
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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=155715370&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 155715370 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Apr2022 vid: 200 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 155715370 10.1007/s11229-022-03631-z ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 381KB tig: atl: Epistemic injustice and data science technologies. aug: au: Symons, John Alvarado, Ramón affil: Department of Philosophy, University of Kansas, Lawrence, USA Department of Philosophy, University of Oregon, Eugene, USA sug: keyword: Artificial intelligence Big data Data science Epistemic injustice Epistemic opacity ab: Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data science technologies. However, we argue that epistemic injustices can be distinguished conceptually from more familiar kinds of harm. We argue that epistemic harms are morally relevant even in cases where those who suffer them are unharmed in other ways. Via a series of examples from the criminal justice system, workplace hierarchies, and educational contexts we explain the kinds of epistemic injustice that can result from common uses of data science technologies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2022. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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