A novel multi-objective-based approach to analyze trade-offs in Fair Principal Component Analysis
Year: 2020
Type: preprint
Abstract: 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. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimension reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the reconstruction error. In other words, there is a trade-off between equity and performance. In this context, this paper proposes to address this trade-off in Fair Principal Component Analysis problems 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 reconstruction error.
Source: arXiv (Cornell University)
Institution: Universidade Estadual de Campinas (UNICAMP)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W3164292655