Opportunistic Multi-aspect Fairness through Personalized Re-ranking
This work addresses fairness in high-impact domains like employment and housing by considering multiple sensitive attributes and individual preferences, though it is incremental as it builds on existing re-ranking techniques.
The paper tackles the problem of fairness in recommender systems by introducing a personalized re-ranking approach that learns individual preferences across multiple fairness dimensions, achieving a better trade-off between accuracy and fairness than prior methods.
As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing, researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized along with recommendation accuracy. However, the previous work had largely ignored how individual preferences may limit the ability of an algorithm to produce fair recommendations. Furthermore, with few exceptions, researchers have only considered scenarios in which fairness is measured relative to a single sensitive feature or attribute (such as race or gender). In this paper, we present a re-ranking approach to fairness-aware recommendation that learns individual preferences across multiple fairness dimensions and uses them to enhance provider fairness in recommendation results. Specifically, we show that our opportunistic and metric-agnostic approach achieves a better trade-off between accuracy and fairness than prior re-ranking approaches and does so across multiple fairness dimensions.