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<https://discovery.ucl.ac.uk/id/eprint/10130186> <http://purl.org/dc/terms/title> "Fairea: A Model Behaviour Mutation Approach to Benchmarking Bias Mitigation Methods"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10130186> <http://purl.org/ontology/bibo/abstract> "The increasingly wide uptake of Machine Learning (ML) has raised the significance of the problem of tackling bias (i.e., unfairness), making it a primary software engineering concern. In this paper, we introduce Fairea, a model behaviour mutation approach to benchmarking ML bias mitigation methods. We also report on a large-scale empirical study to test the effectiveness of 12 widely-studied bias mitigation methods. Our results reveal that, surprisingly, bias mitigation methods have a poor effectiveness in 49% of the cases. In particular, 15% of the mitigation cases have worse fairness-accuracy trade-offs than the baseline established by Fairea; 34% of the cases have a decrease in accuracy and an increase in bias.\r\n\r\nFairea has been made publicly available for software engineers and researchers to evaluate their bias mitigation methods."^^<http://www.w3.org/2001/XMLSchema#string> .
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