Abstract
Alongside the increased use of algorithms as decision making tools, there have been an increase of cases where minority classes have been harmed. This gives rise to study of algorithmic fairness that deals with how to include fairness aspects in the design of algorithms. With this in mind, we define a new problem of fair coverage called Multi-Attribute Fairer Cover, that deals with the task of selecting a subset for training that is as fair as possible. We applied our method to the age regression model using instances from the UTKFace dataset. We also present computational experiments for an Integer Linear Programming model and for the age regression model. The experiments showed significant reduction on the error of the regression model when compared to a random selection.
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Acknowledgements
The authors would like to thank the São Paulo Research Foundation [grants #2017/12646-3, #2020/16439-5]; the National Council for Scientific and Technological Development [grants #306454/2018-1, #161015/2021-2, #302530/2022-3, #304836/2022-2]; the Coordination for the Improvement of Higher Education Personnel; and Santander Bank, Brazil.
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Dantas, A.P.S., de Oliveira, G.B., Pedrini, H., de Souza, C.C., Dias, Z. (2023). The Multi-attribute Fairer Cover Problem. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14195. Springer, Cham. https://doi.org/10.1007/978-3-031-45368-7_11
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DOI: https://doi.org/10.1007/978-3-031-45368-7_11
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