Algorithmic Fairness and its Limits in Group-Formation

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