On Discrimination Discovery and Removal in Ranked Data using Causal Graph
This addresses fairness issues in ranked data for organizations using predictive models, offering a causal approach to mitigate discrimination, but it is incremental as it builds on existing causal methods for classification.
The paper tackled fairness-aware ranking by proposing a causal graph method to discover and remove discrimination in ranked data, mapping rank positions to continuous scores and extending path-specific effects to mixed-variable graphs, with experiments on real datasets showing effectiveness.
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.