On Hedden's proof that machine learning fairness metrics are flawed.
Brian Hedden, in a recent article in Philosophy and Public Affairs [Hedden 2021. "On Statistical Criteria of Algorithmic Fairness." Philosophy and Public Affairs 49 (2): 209–231. https://doi.org/10.1111/papa.v49.2.], presented a thought experiment designed to probe the validity of the fairness metri...
| Publicado en: | Inquiry Vol. 68; no. 4; pp. 1198 - 1218 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
May2025
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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=184864751&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 184864751 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 0020174X B8P jtl: Inquiry issn: 0020174X maglogo: N pubinfo: dt: May2025 vid: 68 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 184864751 10.1080/0020174X.2024.2315169 ppf: 1198 ppct: 20 formats: tig: atl: On Hedden's proof that machine learning fairness metrics are flawed. aug: au: Søgaard, Anders Kappel, Klemens Grünbaum, Thor affil: Center for Philosophy of Artificial Intelligence, University of Copenhagen, Copenhagen, Denmark su: Machine learning Thought experiments Coins Fairness Forecasting Classification sug: subj: Machine learning Thought experiments Coins Fairness Forecasting Classification keyword: fairness machine learning metrics ab: Brian Hedden, in a recent article in Philosophy and Public Affairs [Hedden 2021. "On Statistical Criteria of Algorithmic Fairness." Philosophy and Public Affairs 49 (2): 209–231. https://doi.org/10.1111/papa.v49.2.], presented a thought experiment designed to probe the validity of the fairness metrics used in machine learning (ML). The thought experiment has caused a great stir, also within machine learning [Viganó et al. "People are Not Coins: Morally Distinct Types of Predictions Necessitate Different Fairness Constraints." In 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT '22, 2293–2301, New York, NY: Association for Computing Machinery.]. Brian Hedden describes a particular prediction problem p – involving 40 people divided into two rooms flipping biased coins – and a binary classification model m for predicting the outcome of these 40 coin flips. Brian Hedden argues that in the thought experiment, m is 'perfectly fair', but at the same time, he shows that almost all existing fairness metrics would score m as unfair. He concludes that almost all existing fairness metrics are flawed. If he is right, this seriously undermines most recent work on fair ML. We present three counter-arguments to Brian Hedden's thought experiment, of which the first is the most important: (a) the prediction problem p is irrelevant for ML because p is not (evaluated as) a learning problem, (b) the model m is not actually fair and (c) the prediction problem p is irrelevant for fairness metrics, because group assignment in p is random. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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