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Rated preference aggregation is conventionally performed by averaging ratings from multiple evaluators to create a consensus ordering of candidates from highest to lowest average rating. Ideally, the consensus is fair, meaning critical opportunities are not withheld from marginalized groups of candidates, even if group biases may be present in the to-be-combined ratings. Prior work operationalizing fairness in preference aggregation is limited to settings where evaluators provide rankings of candidates (e.g., Joe > Jack > Jill). Yet, in practice, many evaluators assign ratings such as Likert scales or categories (e.g., yes, no, maybe) to each candidate. Ratings convey different information than rankings leading to distinct fairness issues during their aggregation. The existing literature does not characterize these fairness concerns nor provide applicable bias-mitigation solutions. Unlike the ranked setting studied previously, two unique forms of bias arise in rating aggregation. First, biased rating stems from group disparities in to-be-aggregated evaluator ratings. Second, biased tie-breaking occurs because ties in average ratings must be resolved when aggregating ratings into a consensus ranking, and this tie-breaking act can unfairly advantage certain groups. To address this gap, we define the open fair rated preference aggregation problem and introduce the corresponding Fate methodology. Fate offers the first group fairness metric specifically for rated preference data. We propose two Fate algorithms. Fate-Break works in settings when ties need to be broken, explicitly fairness-enhancing such processes without lowering consensus utility. Fate-Rate mitigates disparities in how groups are rated, by using a Markov-chain approach to generate outcomes where groups are, in as much as possible, equally represented. Our experimental study illustrates the FATE methods provide the most bias-mitigation compared to adapting prior methods to fair tie-breaking and rating aggregation.
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This content will become publicly available on June 7, 2026
Balancing Producer Fairness and Efficiency via Prior-Weighted Rating System Design
Online marketplaces use rating systems to promote the discovery of high-quality products. However, these systems also lead to high variance in producers' economic outcomes: a new producer who sells high-quality items, may unluckily receive a low rating early, severely impacting their future popularity. We investigate the design of rating systems that balance the goals of identifying high-quality products (``efficiency'') and minimizing the variance in outcomes of producers of similar quality (individual ``producer fairness'').We show that there is a trade-off between these two goals: rating systems that promote efficiency are necessarily less individually fair to producers. We introduce prior-weighted rating systems as an approach to managing this trade-off. Informally, the system we propose sets a system-wide prior for the quality of an incoming product; subsequently, the system updates that prior to a posterior for each product's quality based on user-generated ratings over time. We show theoretically that in markets where products accrue reviews at an equal rate, the strength of the rating system's prior determines the operating point on the identified trade-off: the stronger the prior, the more the marketplace discounts early ratings data (increasing individual fairness), but the slower the platform is in learning about true item quality (so efficiency suffers). We further analyze this trade-off in a responsive market where customers make decisions based on historical ratings. Through calibrated simulations in 19 different real-world datasets sourced from large online platforms, we show that the choice of prior strength mediates the same efficiency-consistency trade-off in this setting. Overall, we demonstrate that by tuning the prior as a design choice in a prior-weighted rating system, platforms can be intentional about the balance between efficiency and producer fairness.
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- Award ID(s):
- 2339427
- PAR ID:
- 10616976
- Publisher / Repository:
- Proceedings of the International AAAI Conference on Web and Social Media
- Date Published:
- Journal Name:
- Proceedings of the International AAAI Conference on Web and Social Media
- Volume:
- 19
- ISSN:
- 2162-3449
- Page Range / eLocation ID:
- 1139 to 1157
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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