Popularizing Fairness: Group Fairness and Individual Welfare

نویسندگان

چکیده

Group-fair learning methods typically seek to ensure that some measure of prediction efficacy for (often historically) disadvantaged minority groups is comparable the majority population. When a principal seeks adopt group-fair approach replace another, may face opposition from those who feel they be harmed by switch, and this, in turn, deter adoption. We propose potential mitigation this concern model also popular, sense that, target population, it yields preferred distribution over outcomes compared with conventional model. In paper, we show state art fair approaches are often unpopular sense. several efficient algorithms postprocessing an existing scheme improve its popularity while retaining fairness. Through extensive experiments, demonstrate proposed highly effective practice.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2023

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v37i6.25910