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dc.contributor.authorAltman, Micah
dc.contributor.authorWood, Alexandra
dc.contributor.authorVayena, Effy
dc.date.accessioned2020-05-22T15:47:26Z
dc.date.available2020-05-22T15:47:26Z
dc.date.issued2018-05
dc.identifier.issn1540-7993
dc.identifier.issn1558-4046
dc.identifier.urihttps://hdl.handle.net/1721.1/125415
dc.description.abstractIn this article, we recognize the profound effects that algorithmic decision making can have on people's lives and propose a harm-reduction framework for algorithmic fairness. We argue that any evaluation of algorithmic fairness must take into account the foreseeable effects that algorithmic design, implementation, and use have on the well-being of individuals. We further demonstrate how counterfactual frameworks for causal inference developed in statistics and computer science can be used as the basis for defining and estimating the foreseeable effects of algorithmic decisions. Finally, we argue that certain patterns of foreseeable harms are unfair. An algorithmic decision is unfair if it imposes predictable harms on sets of individuals that are unconscionably disproportionate to the benefits these same decisions produce elsewhere. Also, an algorithmic decision is unfair when it is regressive, that is, when members of disadvantaged groups pay a higher cost for the social benefits of that decision.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/msp.2018.2701149en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMicah Altmanen_US
dc.titleA Harm-Reduction Framework for Algorithmic Fairnessen_US
dc.typeArticleen_US
dc.identifier.citationAltman, Micah et al. "A Harm-Reduction Framework for Algorithmic Fairness." IEEE Security and Privacy 16, 3 (May 2018) © 2018 IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Librariesen_US
dc.relation.journalIEEE Security and Privacyen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-05-22T14:04:32Z
dspace.date.submission2020-05-22T14:04:35Z
mit.journal.volume16en_US
mit.journal.issue3en_US
mit.licenseOPEN_ACCESS_POLICY


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