Algorithmic Fairness and its Limits in Group-Formation

Abstract

Abstract. Algorithmic group formation has become a flourishing research area in the computer sciences, and more recently in the field of data mining and fair machine learning. Application domains for algorithmic solutions to grouping span wide, from team-recommendation and formation in work settings to ability-grouping in education. Recent work has also focused on fairness in group-formation. We briefly review literature on algorithmic team-formation and consider fairness in different group-formation contexts. We articulate different dimensions and constraints that are relevant for fair group-formation and discuss the tension between utility and fairness. Many problems and limitations regarding formal definitions of fairness explicated in the fair machine learning literature apply also in the context of group-formation. We suggest some limits to the relevance of fairness in general and algorithmic fairness, in particular. We argue that algorithmic fairness is less relevant to some groups because of the way they come to existence or because fairness is not a central value for them. Other central values are subjective rights; autonomy or liberty; legitimacy and authority; solidarity; and diversity, each of which can be in tension with optimal fairness-and-utility. But within acceptable limits, we argue that fairness is indeed a valuable goal that may be in tension with maximization of the relevant types of utility