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

Yuhan Liu, Yuhan Zheng, Siyuan Zhang, Lydia T. Liu

TL;DR

It is found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, health insurance status, tenure to the platform, and working hours, which underscores the need for collaboration with rideshare platforms and drivers to enhance fairness in algorithmic wage determination and pricing.

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.

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

TL;DR

It is found that drivers' hourly wages are significantly influenced by factors such as race/ethnicity, health insurance status, tenure to the platform, and working hours, which underscores the need for collaboration with rideshare platforms and drivers to enhance fairness in algorithmic wage determination and pricing.

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.
Paper Structure (14 sections, 5 figures)

This paper contains 14 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Related work
  3. Data & Method
  4. Dataset
  5. Evaluating fairness in driver's compensation
  6. Evaluating fairness in customer's payment
  7. Results
  8. Fairness in driver's compensation
  9. Fairness in customer's payment
  10. Discussion and Conclusion
  11. Disparities in drivers' compensation calls for policy intervention and more transparency
  12. Transparency is needed to increase riders' perception of fairness towards dynamic pricing system
  13. Conclusion and Future Work
  14. Figures

Figures (5)

  • Figure 1: Comparison between the actual taxi fares and the predicted fares if trips were fulfilled by rideshare platforms. Each data point represents one trip. Points above the red dotted line indicate predicted fares are higher than actual fares, while points below indicate predicted fares are lower than actual fares.
  • Figure 2: Distribution of race groups in each hourly wage category.
  • Figure 3: Distribution of insurance groups in each hourly wage category.
  • Figure 4: Distribution of hourly wage category for each tenure group.
  • Figure 5: Distribution of hourly wage category for each working hour group.