This study examines fairness within the rideshare industry, focusing on both drivers' wages and riders' trip fares. Through quantitative analysis, we found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, health insurance status, tenure to the platform, and working hours. Despite platforms' policies not intentionally embedding biases, disparities persist based on these characteristics. For ride fares, we propose a method to audit the pricing policy of a proprietary algorithm by replicating it; we conduct a hypothesis test to determine if the predicted rideshare fare is greater than the taxi fare, taking into account the approximation error in the replicated model. Challenges in accessing data and transparency hinder our ability to isolate discrimination from other factors, underscoring the need for collaboration with rideshare platforms and drivers to enhance fairness in algorithmic wage determination and pricing.

翻译：本研究考察了网约车行业的公平性，重点关注司机收入和乘客出行费用。通过定量分析，我们发现司机的时薪显著受到种族/民族、医疗保险状况、平台服务年限和工作时长等因素的影响。尽管平台政策并非有意嵌入偏见，但基于这些特征的差异仍然存在。针对乘车费用，我们提出了一种通过复制算法来审计专有定价策略的方法；在考虑复制模型近似误差的前提下，我们进行了假设检验以判断预测的网约车费用是否高于出租车费用。数据获取和透明度方面的挑战阻碍了我们从其他因素中分离歧视性影响的能力，这凸显了与网约车平台及司机合作以提升算法化薪资决定和定价公平性的必要性。