What we owe to decision-subjects: beyond transparency and explanation in automated decision-making.
The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resul...
| Publicado en: | Philosophical Studies Vol. 182; no. 1; pp. 55 - 86 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Jan2025
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| Materias: | |
| 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=182303660&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182303660 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: 182303660 10.1007/s11098-023-02013-6 ppf: 55 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P size: 816KB tig: atl: What we owe to decision-subjects: beyond transparency and explanation in automated decision-making. aug: au: Grant, David Gray Behrends, Jeff Basl, John affil: https://ror.org/02y3ad647 University of Florida, Gainesville, USA https://ror.org/02yxdv803 Jain Family Institute, New York, USA https://ror.org/03vek6s52 Harvard University, Cambridge, USA https://ror.org/04t5xt781 Northeastern University, Boston, USA su: Artificial intelligence Decision making Machine learning Data Automation sug: subj: Artificial intelligence Decision making Machine learning Data Automation keyword: Explanation Interpretability Mathematical Sciences Statistics Information and Computing Sciences Artificial Intelligence and Image Processing Opacity Right to explanation Transparency ab: The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. 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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