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.

翻译：数字经济中的数字服务由促进服务提供者与消费者互动的在线平台构成。该生态系统的特点是交易双方通常缺乏前期了解，进行短期且往往一次性的交易。为在用户间建立信任，平台采用评级系统，允许用户报告其先前互动的质量。然而，尽管评级系统对这些平台的运作至关重要，它们也可能使针对边缘群体的负面偏见长期存在。本文探讨如何在存在偏见的声誉系统基础上设计平台，在减少歧视的同时，保持所有服务提供者为用户提供高质量服务的激励。我们引入演化博弈理论模型，研究数字平台如何延续或抵消基于评级的歧视。我们重点关注平台推广高声誉服务提供者或属于特定受保护群体的服务提供者的决策。我们的研究结果揭示了用户体验与公平性之间的根本权衡：推广高评级提供者使用户受益，但会降低对评级存在偏见的边缘化提供者的需求。我们的结果还证明，通过调整搜索结果的人口结构进行干预，是减少不公平性同时最小化对用户影响的高效方法。此外，我们表明即使无法精确测量影响边缘化服务提供者的评级偏见程度，仍有潜力改进忽略受保护特征的推荐系统。总之，我们的模型凸显了在利用评级促进合作行为的系统中，主动反歧视设计的益处。