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1. Dynamic fairness-aware recommendation through multi-agent social choice

   https://doi.org/10.1145/3690653  

   Aird, Amanda; Farastu, Paresha; Sun, Joshua; Stefancová, Elena; All, Cassidy; Voida, Amy; Mattei, Nicholas; Burke, Robin (September 2024, ACM Transactions on Recommender Systems)

   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 (or some other metric) 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. To address the fundamental problem of fairness in the presence of multiple stakeholders, with different definitions of fairness, we propose the Social Choice for Recommendation Under Fairness – Dynamic (SCRUF-D) architecture, which formalizes multistakeholder fairness in recommender systems as a two-stage social choice problem. In particular, we express recommendation fairness as a 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. We demonstrate the ability of our framework to dynamically incorporate multiple fairness concerns using both real-world and synthetic datasets. 

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2. Opportunistic Multi-aspect Fairness through Personalized Re-ranking

   https://doi.org/10.1145/3340631.3394846  

   Sonboli, Nasim; Eskandanian, Farzad; Burke, Robin; Liu, Weiwen; Mobasher, Bamshad (July 2020, ACM Conference on User Modeling, Adaptation and Personalization (UMAP))

   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. 

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3. The multisided complexity of fairness in recommender systems

   https://doi.org/10.1002/aaai.12054  

   Sonboli, Nasim; Burke, Robin; Ekstrand, Michael; Mehrotra, Rishabh (June 2022, AI Magazine)

   Abstract Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area. 

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4. Dynamic Fairness-Aware Recommendation through Multi-Agent Social Choice

   Aird, A.; Farastu, P.; Sun, J.; Voida, A.; Mattei, N.; Burke, R. (July 2023, th International Workshop on Computational Social Choice (COMSOC 2023))

   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 

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5. Fair Link Prediction with Multi-Armed Bandit Algorithms

   https://doi.org/10.1145/3578503.3583624  

   Wang, Weixiang; Soundarajan, Sucheta (April 2023, WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023)

   Recommendation systems have been used in many domains, and in recent years, ethical problems associated with such systems have gained serious attention. The problem of unfairness in friendship or link recommendation systems in social networks has begun attracting attention, as such unfairness can cause problems like segmentation and echo chambers. One challenge in this problem is that there are many fairness metrics for networks, and existing methods only consider the improvement of a single specific fairness indicator. In this work, we model the fair link prediction problem as a multi-armed bandit problem. We propose FairLink, a multi-armed bandit based framework that predicts new edges that are both accurate and well-behaved with respect to a fairness property of choice. This method allows the user to specify the desired fairness metric. Experiments on five real-world datasets show that FairLink can achieve a significant fairness improvement as compared to a standard recommendation algorithm, with only a small reduction in accuracy. 

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