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The method does not assume predefined bias terms, does not anchor on specific fairness metrics, and is independent of Alice\u2019s classifier choice. We consider that data attributes have different concentrations of the latent bias axes; assessing attributes\u2019 concentrations in the ruled bias hyperspace helps identify bias-prone attributes and inform bias-mitigating data transforms. To this end, we assess attributes\u2019 contribution to the separating capability of Bob\u2019s conceptual classifier. We then compute the pairwise distances between attributes, and by applying multidimensional scaling to the distance matrix, we infer the axes of bias and establish a bias-attribute mapping. Bias mitigation is achieved by greedily applying appropriate data transforms to bias-prone attributes. The method works desirably across 21 classifiers and 7 datasets, bringing about substantial bias reduction under different choices of the protected dimension and the fairness metric. Compared to adversarial debiasing, the method better exploits the fairness-utility trade-off in machine classification.<\/jats:p>","DOI":"10.34133\/icomputing.0083","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T15:31:24Z","timestamp":1710948684000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":2,"title":["Metric-Independent Mitigation of Unpredefined Bias in Machine Classification"],"prefix":"10.34133","volume":"3","author":[{"given":"Zhoufei","family":"Tang","sequence":"first","affiliation":[{"name":"Department of Information Systems and Management Engineering, \rSouthern University of Science and Technology, Shenzhen, Guangdong, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Management Engineering, \rSouthern University of Science and Technology, Shenzhen, Guangdong, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Decisions, Operations and Technology, \rThe Chinese University of Hong Kong, Hong Kong, China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41562-019-0686-3"},{"issue":"5","key":"e_1_3_4_3_2","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1038\/s41562-020-01029-w","article-title":"Officer bias, over-patrolling and ethnic disparities in stop and search","volume":"5","author":"Vomfell L","year":"2021","unstructured":"Vomfell L, Stewart N. 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