A multi-objective-based approach for Fair Principal Component Analysis
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In dimension reduction problems, the adopted technique may produce disparities between the representation errors of two or more different groups. For instance, in the projected space, a specific class can be better represented in comparison with the other ones. Depending on the situation, this unfair result may introduce ethical concerns. In this context, this paper investigates how a fairness measure can be considered when performing dimension reduction through principal component analysis. Since both reconstruction error and fairness measure must be taken into account, we propose a multi-objective-based approach to tackle the Fair Principal Component Analysis problem. The experiments attest that a fairer result can be achieved with a very small loss in the reconstruction error.
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