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1. Fairness-aware Recommendation with librec-auto

   https://doi.org/10.1145/3383313.3411525  

   Sonboli, Nasim; Burke, Robin; Liu, Zijun; Mansoury, Masoud (September 2020, RecSys '20: Fourteenth ACM Conference on Recommender Systems)

   null (Ed.)

   Comparative experimentation is important for studying reproducibility in recommender systems. This is particularly true in areas without well-established methodologies, such as fairness-aware recommendation. In this paper, we describe fairness-aware enhancements to our recommender systems experimentation tool librec-auto. These enhancements include metrics for various classes of fairness definitions, extension of the experimental model to support result re-ranking and a library of associated re-ranking algorithms, and additional support for experiment automation and reporting. The associated demo will help attendees move quickly to configuring and running their own experiments with librec-auto. 

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2. Data Generation via Latent Factor Simulation for Fairness-aware Re-ranking

   Štefancová, E; All, C; Paup, J; Homola, M; Mattei, Nicholas; Burke, Robin (October 2024, FAccTRec 2024 Workshop on Responsible Recommendation)

   Synthetic data is a useful resource for algorithmic research. It allows for the evaluation of systems under a range of conditions that might be difficult to achieve in real world settings. In recommender systems, the use of synthetic data is somewhat limited; some work has concentrated on building user-item interaction data at large scale. We believe that fairness-aware recommendation research can benefit from simulated data as it allows the study of protected groups and their interactions without depending on sensitive data that needs privacy protection. In this paper, we propose a novel type of data for fairness-aware recommendation: synthetic recommender system outputs that can be used to study re-ranking algorithms. 

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3. Social Choice for Heterogeneous Fairness in Recommendation

   Aird, A; Štefancová, E; All, C; Voida, A; Homola, M; Mattei, Nicholas; Burke, Robin (October 2024, Proceedings of the 18th ACM Conference on Recommender Systems: Late-Breaking Results)

   Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests. Previous work in this area has often been limited by fixed, single-objective definitions of fairness, built into algorithms or optimization criteria that are applied to a single fairness dimension or, at most, applied identically across dimensions. These narrow conceptualizations limit the ability to adapt fairness-aware solutions to the wide range of stakeholder needs and fairness definitions that arise in practice. Our work approaches recommendation fairness from the standpoint of computational social choice, using a multi-agent framework. In this paper, we explore the properties of different social choice mechanisms and demonstrate the successful integration of multiple, heterogeneous fairness definitions across multiple data sets. 

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4. Exploring “Just Noticeable” Group Fairness in Rankings

   Alkhathlan, Mallak; Shrestha, Hilson; Harrison, Lane; Rundensteiner, Elke (October 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org).)

   The plethora of fairness metrics developed for ranking-based decision-making raises the question: which metrics align best with people’s perceptions of fairness, and why? Most prior studies examining people’s perceptions of fairness metrics tend to use ordinal rating scales (e.g., Likert scales). However, such scales can be ambiguous in their interpretation across participants, and can be influenced by interface features used to capture responses.We address this gap by exploring the use of two-alternative forced choice methodologies— used extensively outside the fairness community for comparing visual stimuli— to quantitatively compare participant perceptions across fairness metrics and ranking characteristics. We report a crowdsourced experiment with 224 participants across four conditions: two alternative rank fairness metrics, ARP and NDKL, and two ranking characteristics, lists of 20 and 100 candidates, resulting in over 170,000 individual judgments. Quantitative results show systematic differences in how people interpert these metrics, and surprising exceptions where fairness metrics disagree with people’s perceptions. Qualitative analyses of participant comments reveals an interplay between cognitive and visual strategies that affects people’s perceptions of fairness. From these results, we discuss future work in aligning fairness metrics with people’s perceptions, and highlight the need and benefits of expanding methodologies for fairness studies. 

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5. Fairness and Transparency in Recommendation: The Users’ Perspective

   https://doi.org/10.1145/3450613.3456835  

   Sonboli, Nasim; Smith, Jessie J.; Cabral Berenfus, Florencia; Burke, Robin; Fiesler, Casey (June 2021, UMAP '21: Proceedings Of The 29th ACM Conference On User Modeling, Adaptation And Personalization)

   null (Ed.)

   Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems. 

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