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Online marketplaces use rating systems to promote the discovery ofhigh-quality products. However, these systems also lead to high variance inproducers' economic outcomes: a new producer who sells high-quality items, mayunluckily receive one low rating early on, negatively impacting their futurepopularity. We investigate the design of rating systems that balance the goalsof identifying high-quality products (efficiency) and minimizing the variancein economic outcomes of producers of similar quality (individual producerfairness).  We show that there is a trade-off between these two goals: rating systemsthat promote efficiency are necessarily less individually fair to producers. Weintroduce prior-weighted rating systems as an approach to managing thistrade-off. Informally, the system we propose sets a system-wide prior for thequality of an incoming product; subsequently, the system updates that prior toa posterior for each producer's quality based on user-generated ratings overtime. We show theoretically that in markets where products accrue reviews at anequal rate, the strength of the rating system's prior determines the operatingpoint on the identified trade-off: the stronger the prior, the more themarketplace discounts early ratings data (increasing individual fairness), butthe slower the platform is in learning about true item quality (so efficiencysuffers). We further analyze this trade-off in a responsive market wherecustomers make decisions based on historical ratings. Through calibratedsimulations, we show that the choice of prior strength mediates the sameefficiency-consistency trade-off in this setting. Overall, we demonstrate thatby tuning the prior as a design choice in a prior-weighted rating system,platforms can be intentional about the balance between efficiency and producerfairness.

Balancing Producer Fairness and Efficiency via Prior-Weighted Rating System Design