2020/09/05 by Nasim Sonboli, Sonboli, Nasim, Robin Burke +7
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Mobile Crowdsensing and Crowdsourcing #Privacy, Security, and Data Protection
paper · pdf · doi:10.48550/arxiv.2009.02590
openalex publication_date 2020/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As recommender systems are being designed and deployed for an increasing number of socially-consequential applications, it has become important to consider what properties of fairness these systems exhibit. There has been considerable research on recommendation fairness. However, we argue that the previous literature has been based on simple, uniform and often uni-dimensional notions of fairness assumptions that do not recognize the real-world complexities of fairness-aware applications. In this paper, we explicitly represent the design decisions that enter into the trade-off between accuracy and fairness across multiply-defined and intersecting protected groups, supporting multiple fairness metrics. The framework also allows the recommender to adjust its performance based on the historical view of recommendations that have been delivered over a time horizon, dynamically rebalancing between fairness concerns. Within this framework, we formulate lottery-based mechanisms for choosing between fairness concerns, and demonstrate their performance in two recommendation domains.