33 research outputs found
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
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.Comment: 9 page