Developing a Philosophical Framework for Fair Machine Learning: Lessons From The Case of Algorithmic Collusion

Abstract

Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. However, as machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different than those presently studied. The existing research paradigm in machine learning which develops metrics and definitions of fairness cannot account for these qualitatively different types of injustice. One example of this is the problem of algorithmic collusion and market fairness. The negative consequences of algorithmic collusion affect all consumers, not only particular members of a protected class. Drawing on this case study, I propose an ethical framework for researchers and practitioners in machine learning seeking to develop and apply fairness metrics that extends to new domains. This contribution ties the development of formal metrics of fairness to specifically scoped normative principles. This enables fairness metrics to reflect different concerns from discrimination. I conclude with the limitations of my proposal and discuss promising avenues for future research.