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arXiv · 2006.06137

Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization

Guilherme D. Pelegrina·Renan D. B. Brotto·Leonardo T. Duarte·Romis Attux·João M. T. Romano
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Abstract

In dimensionality reduction problems, the adopted technique may produce disparities between the representation errors of different groups. For instance, in the projected space, a specific class can be better represented in comparison with another one. In some situations, this unfair result may introduce ethical concerns. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimensionality reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the overall re-construction error. In this context, this paper proposes to address this trade-off by means of a multi-objective-based approach. For this purpose, we adopt a fairness measure associated with the disparity between the representation errors of different groups. Moreover, we investigate if the solution of a classical Principal Component Analysis can be used to find a fair projection. Numerical experiments attest that a fairer result can be achieved with a very small loss in the overall reconstruction error.

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Guilherme D. Pelegrina, Renan D. B. Brotto, Leonardo T. Duarte, Romis Attux, João M. T. Romano. 2020-06-11. Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization. https://doi.org/10.1109/ijcnn55064.2022.9892809

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