Work

How fair can we go in machine learning ? Assessing the boundaries of accuracy and fairness

Year: 2021

Type: article

Abstract: Fair machine learning has been focusing on the development of equitable algorithms that address discrimination. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. In this study, a novel methodology is presented to explore the tradeoff in terms of a Pareto front between accuracy and fairness. To this end, we propose a multiobjective framework that seeks to optimize both measures. The experimental framework is focused on logistiregression and decision tree classifiers since they are well-known by the machine learning community. We conclude experimentally that our method can optimize classifiers by being fairer with a small cost on the classification accuracy. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used.

Source: International Journal of Intelligent Systems

DOI: 10.1002/int.22354

Authors: Ana Valdivia, Javier Sánchez‐Monedero, Jorge Casillas

Institutions: King's College London, Cardiff University, Universidad de Granada

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: bronze

Funders: European Commission, Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III, H2020 European Research Council