Fair Division Without Disparate Impact
This addresses fairness in resource allocation for applications like recommender systems, but it is incremental as it builds on existing CEEI methods.
The paper tackled the problem of fair division by modifying the competitive equilibrium from equal incomes (CEEI) mechanism to address disparate impact across protected classes, showing that removing disparate impact in allocations breaks desirable properties like envy and Pareto optimality, while removing it in utility levels preserves these properties without affecting efficiency.
We consider the problem of dividing items between individuals in a way that is fair both in the sense of distributional fairness and in the sense of not having disparate impact across protected classes. An important existing mechanism for distributionally fair division is competitive equilibrium from equal incomes (CEEI). Unfortunately, CEEI will not, in general, respect disparate impact constraints. We consider two types of disparate impact measures: requiring that allocations be similar across protected classes and requiring that average utility levels be similar across protected classes. We modify the standard CEEI algorithm in two ways: equitable equilibrium from equal incomes, which removes disparate impact in allocations, and competitive equilibrium from equitable incomes which removes disparate impact in attained utility levels. We show analytically that removing disparate impact in outcomes breaks several of CEEI's desirable properties such as envy, regret, Pareto optimality, and incentive compatibility. By contrast, we can remove disparate impact in attained utility levels without affecting these properties. Finally, we experimentally evaluate the tradeoffs between efficiency, equity, and disparate impact in a recommender-system based market.