Fairness Dynamics in Digital Economy Platforms with Biased Ratings

TL;DR

This paper models how digital platforms can address biased ratings that perpetuate discrimination, revealing a trade-off between user satisfaction and fairness, and proposing interventions to reduce unfairness effectively.

Contribution

It introduces an evolutionary game theoretical model to analyze rating-based discrimination and suggests proactive design strategies to promote fairness without significantly harming user experience.

Findings

Promoting highly-rated providers benefits users but can increase discrimination against marginalized groups.

Tuning search result demographics effectively reduces unfairness with minimal impact on user satisfaction.

Interventions are possible even without precise bias measurements, improving fairness in reputation systems.

Abstract

The digital services economy consists of online platforms that facilitate interactions between service providers and consumers. This ecosystem is characterized by short-term, often one-off, transactions between parties that have no prior familiarity. To establish trust among users, platforms employ rating systems which allow users to report on the quality of their previous interactions. However, while arguably crucial for these platforms to function, rating systems can perpetuate negative biases against marginalised groups. This paper investigates how to design platforms around biased reputation systems, reducing discrimination while maintaining incentives for all service providers to offer high quality service for users. We introduce an evolutionary game theoretical model to study how digital platforms can perpetuate or counteract rating-based discrimination. We focus on the platforms' decisions to promote service providers who have high reputations or who belong to a specific protected group. Our results demonstrate a fundamental trade-off between user experience and fairness: promoting highly-rated providers benefits users, but lowers the demand for marginalised providers against which the ratings are biased. Our results also provide evidence that intervening by tuning the demographics of the search results is a highly effective way of reducing unfairness while minimally impacting users. Furthermore, we show that even when precise measurements on the level of rating bias affecting marginalised service providers is unavailable, there is still potential to improve upon a recommender system which ignores protected characteristics. Altogether, our model highlights the benefits of proactive anti-discrimination design in systems where ratings are used to promote cooperative behaviour.