Evolutionary Algorithms for Fair Machine Learning

Author:

Freitas Alex,Brookhouse James

Publisher

Springer Nature Singapore

Reference46 articles.

1. Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias: there’s software used across the country to predict future criminals, and it’s biased against blacks (2016)

2. Binns, R.: Fairness in machine learning: lessons from political philosophy. J. Mach. Learn. Res. 81, 1–11 (2018)

3. Brookhouse, J., Freitas, A.A.: Fair feature selection with a lexicographic multi-objective genetic algorithm. In: Proceedings of the 2022 Parallel Problem Solving from Nature Conference (PPSN 2022), LNCS 13399, pp. 151–163. Springer (2022)

4. Buolamwini, J., Gebru, T.: Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of Machine Learning Research: Conference on Fairness, Accountability and Transparency, vol. 81, pp. 1–15 (2018)

5. Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Mach. Learn. Res. 70, 245–317 (2021)

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