Exploring Social Choice Mechanisms for Recommendation Fairness in SCRUF
This addresses fairness problems in recommender systems for users and developers, offering a flexible multi-aspect approach, though it is incremental in applying social choice to this domain.
The paper tackled the complexity of fairness in recommender systems by exploring social choice mechanisms within a multi-agent architecture, showing that different mechanisms yield consistent fairness/accuracy tradeoffs and adapt to user dynamics.
Fairness problems in recommender systems often have a complexity in practice that is not adequately captured in simplified research formulations. A social choice formulation of the fairness problem, operating within a multi-agent architecture of fairness concerns, offers a flexible and multi-aspect alternative to fairness-aware recommendation approaches. Leveraging social choice allows for increased generality and the possibility of tapping into well-studied social choice algorithms for resolving the tension between multiple, competing fairness concerns. This paper explores a range of options for choice mechanisms in multi-aspect fairness applications using both real and synthetic data and shows that different classes of choice and allocation mechanisms yield different but consistent fairness / accuracy tradeoffs. We also show that a multi-agent formulation offers flexibility in adapting to user population dynamics.