It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games
This work addresses a foundational issue in AI fairness for hiring systems, proposing a shift from distributional to relational equality, which is novel but incremental in its specific domain.
The paper tackles the problem that existing computational fairness definitions, which focus on distributional equality, fail to prevent unequal social relations, as demonstrated by a hiring market example where a self-confirming equilibrium satisfies distributional fairness but exhibits relational inequality.
Existing efforts to formulate computational definitions of fairness have largely focused on distributional notions of equality, where equality is defined by the resources or decisions given to individuals in the system. Yet existing discrimination and injustice is often the result of unequal social relations, rather than an unequal distribution of resources. Here, we show how optimizing for existing computational and economic definitions of fairness and equality fail to prevent unequal social relations. To do this, we provide an example of a self-confirming equilibrium in a simple hiring market that is relationally unequal but satisfies existing distributional notions of fairness. In doing so, we introduce a notion of blatant relational unfairness for complete-information games, and discuss how this definition helps initiate a new approach to incorporating relational equality into computational systems.