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

Author(s)

Pelegrina, Guilherme D. ; Brotto, Renan D. B. ; Duarte, Leonardo T. ; Attux, Romis ; Romano, Joao M. T. ; IEEE

• Document type: Journal article
• Source: 2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2022-01-01.

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. (AU)

• FAPESP's process: 20/09838-0 - BI0S - Brazilian Institute of Data Science
• Grantee: João Marcos Travassos Romano
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 19/20899-4 - Antisparsity and Equidity in signal processing: from blind source separation to fairness machine learning
• Grantee: Renan Del Buono Brotto
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 20/01089-9 - Unsupervised signal separation: a study on the applicability of Generative Adversarial Networks and on nonlinear models based on the Choquet Integral
• Grantee: Leonardo Tomazeli Duarte
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/10572-5 - Novel approaches for fairness and transparency in machine learning problems
• Grantee: Guilherme Dean Pelegrina
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 21/11086-0 - Interpretability and fairness in machine learning: Capacity-based functions and interaction indices
• Grantee: Guilherme Dean Pelegrina
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor