Causal scientific explanations from machine learning
In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 202, Heft 6
ISSN: 1573-0964
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In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 202, Heft 6
ISSN: 1573-0964
In: AI and ethics
ISSN: 2730-5961
AbstractTo monitor and prevent bias in AI systems, we can use a wide range of (statistical) fairness measures. However, it is mathematically impossible to optimize all of these measures at the same time. In addition, optimizing a fairness measure often greatly reduces the accuracy of the system (Kozodoi et al., Eur J Oper Res 297:1083–1094, 2022). As a result, we need a substantive theory that informs us how to make these decisions and for what reasons. I show that by using Rawls' notion of justice as fairness, we can create a basis for navigating fairness measures and the accuracy trade-off. In particular, this leads to a principled choice focusing on both the most vulnerable groups and the type of fairness measure that has the biggest impact on that group. This also helps to close part of the gap between philosophical accounts of distributive justice and the fairness literature that has been observed by (Kuppler et al. Distributive justice and fairness metrics in automated decision-making: How much overlap is there? arXiv preprint arXiv:2105.01441, 2021), and to operationalise the value of fairness.
In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 197, Heft 9, S. 3779-3796
ISSN: 1573-0964
In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 197, Heft 3, S. 1241-1261
ISSN: 1573-0964
In: Metascience: an international review journal for the history, philosophy and social studies of science, Band 26, Heft 3, S. 507-509
ISSN: 1467-9981
In: Synthese: an international journal for epistemology, methodology and philosophy of science, Band 194, Heft 6, S. 2233-2250
ISSN: 1573-0964
In: AI and ethics, Band 4, Heft 1, S. 1-3
ISSN: 2730-5961