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.

翻译：推荐系统中的公平性问题在实践中往往具有复杂性，而简化的研究公式无法充分捕捉这种复杂性。基于公平关切的多智能体架构，采用社会选择理论对公平问题进行建模，为公平感知推荐方法提供了灵活且多方面的替代方案。利用社会选择理论能够增强通用性，并有可能借助经过充分研究的社会选择算法来解决多个相互竞争的公平关切之间的冲突。本文利用真实数据和合成数据，探索了多方面公平性应用中多种选择机制选项，并表明不同类别的选择与分配机制会产生不同但一致的公平性/准确性权衡。我们还表明，多智能体框架在适应用户群体动态方面具有灵活性。