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- W4220875808 abstract "Organizations are increasingly employing complex black-box machine learning models in high-stakes decision-making. A popular approach to addressing the problem of opacity of black-box machine learning models is the use of post-hoc explainability methods. These methods approximate the logic of underlying machine learning models with the aim of explaining their internal workings, so that human examiners can understand them. In turn, it has been alluded that the insights from post-hoc explainability methods can be used to help regulate black-box machine learning. This article examines the validity of these claims. By examining whether the insights derived from post-hoc explainability methods in post-model deployment can prima facie meet legal definitions in European (read European Union) non-discrimination law, we argue that machine learning post-hoc explanation methods cannot guarantee the insights they generate. Ultimately, we argue that the use of post-hoc explanatory methods is useful in many cases, but that these methods have limitations that prohibit reliance as the sole mechanism to guarantee fairness of model outcomes in high-stakes decision-making. By way of an ancillary function, the inadequacy of European Non-Discrimination Law for algorithmic decision-making is demonstrated too." @default.
- W4220875808 created "2022-04-03" @default.
- W4220875808 creator A5007903070 @default.
- W4220875808 creator A5051844168 @default.
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- W4220875808 date "2022-03-15" @default.
- W4220875808 modified "2023-10-03" @default.
- W4220875808 title "Explainable artificial intelligence (XAI) post-hoc explainability methods: risks and limitations in non-discrimination law" @default.
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- W4220875808 doi "https://doi.org/10.1007/s43681-022-00142-y" @default.
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