Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of achieving equality of outcomes between a protected and unprotected group, and built algorithmic interventions on this basis. We argue that fairness in real-world application settings in general, and especially in the context of personalized recommendation, is much more complex and multi-faceted, requiring a more general approach. We propose a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. In particular, we express recommendation fairness as a novel combination of an allocation and an aggregation problem, which integrate both fairness concerns and personalized recommendation provisions, and derive new recommendation techniques based on this formulation. Simulations demonstrate the ability of the framework to integrate multiple fairness concerns in a dynamic way.
翻译:研究分类的研究人员一般认为公平是实现受保护和无保护群体之间结果平等的问题,并在此基础上建立了算法干预。我们争辩说,在现实世界应用环境中,特别是在个性化建议中,公平性要复杂得多,涉及多个方面,需要采取更普遍的办法。我们提出了一个模式,将建议系统中的多方利益攸关者公平性正式确定为两个阶段的社会选择问题。我们特别将建议公平性作为分配和汇总问题的新组合,将公平性关切和个性化建议条款结合起来,并以此为基础提出新的建议方法。模拟表明框架有能力以动态的方式将多重公平问题综合起来。</s>