The Limits of Value Transparency in Machine Learning.
Transparency has been proposed as a way of handling value-ladenness in machine learning (ML). This article highlights limits to this strategy. I distinguish three kinds of transparency: epistemic transparency, retrospective value transparency, and prospective value transparency. This corresponds to...
| Publicado en: | Philosophy of Science Vol. 89; no. 5; pp. 1054 - 1065 |
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| Formato: | Artículo |
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Cambridge University Press
Dec2022
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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=161723328&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 161723328 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00318248 PSC jtl: Philosophy of Science issn: 00318248 maglogo: N pubinfo: dt: Dec2022 vid: 89 iid: 5 pid: 15979 pub: Cambridge University Press artinfo: ui: 161723328 10.1017/psa.2022.61 ppf: 1054 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P size: 146KB tig: atl: The Limits of Value Transparency in Machine Learning. aug: au: Nyrup, Rune affil: Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK su: Machine learning Artificial intelligence Information design Network governance sug: subj: Machine learning Artificial intelligence Information design Network governance ab: Transparency has been proposed as a way of handling value-ladenness in machine learning (ML). This article highlights limits to this strategy. I distinguish three kinds of transparency: epistemic transparency, retrospective value transparency, and prospective value transparency. This corresponds to different approaches to transparency in ML, including so-called explainable artificial intelligence and governance based on disclosing information about the design process. I discuss three sources of value-ladenness in ML—problem formulation, inductive risk, and specification gaming—and argue that retrospective value transparency is only well-suited for dealing with the first, while the third raises serious challenges even for prospective value transparency. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Philosophy of Science is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Philosophy of Science holder: Cambridge University Press dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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