Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago

TL;DR

This paper investigates fairness issues in Chicago's rideshare industry, analyzing driver wages and fare pricing, revealing persistent disparities influenced by demographic factors despite no intentional bias in platform policies.

Contribution

It introduces a method to audit proprietary fare algorithms and highlights the challenges of data access and transparency in ensuring fairness.

Findings

Driver wages vary significantly by race, health insurance, tenure, and hours.

A new approach to replicate and test fare algorithms was proposed.

Disparities persist despite no intentional bias in platform policies.

Abstract

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.