  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.
