Equality of Effort via Algorithmic Recourse
This work addresses fairness in algorithmic decision-making for protected groups, offering a more flexible and applicable approach, though it builds incrementally on existing definitions of equality of effort.
The paper tackles the problem of measuring fairness in automated systems by proposing a method based on equality of effort, which quantifies the minimal cost for protected individuals or groups to reverse unfavorable outcomes, and validates it on synthetic and real-world datasets like the German credit dataset.
This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions. Equality of effort is a property that can be quantified at both the individual and the group level. It answers the counterfactual question: what is the minimal cost for a protected individual or the average minimal cost for a protected group of individuals to reverse the outcome computed by an automated system? Algorithmic recourse increases the flexibility and applicability of the notion of equal effort: it overcomes its previous limitations by reconciling multiple treatment variables, introducing feasibility and plausibility constraints, and integrating the actual relative costs of interventions. We extend the existing definition of equality of effort and present an algorithm for its assessment via algorithmic recourse. We validate our approach both on synthetic data and on the German credit dataset.