Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy

TL;DR

This paper investigates how transparency in AI platform design affects rideshare workers by identifying harms, worker needs, and policy solutions. It employs a mixed-methods approach, combining nine driver interviews with a large-scale LLM-assisted analysis of over 1 million Reddit posts to reveal four core information drivers: promotions, fares, routes, and task allocation. The authors argue that voluntary redesigns are unlikely to close the transparency gap and propose regular rideshare transparency reports with detailed indicators to enable auditing and regulation. These reports, grounded in empirical findings, aim to improve worker well-being, accountability, and safety, while acknowledging trade-offs and potential unintended consequences. The work contributes a practical policy roadmap for regulators and designers seeking to address information asymmetries in gig economies.

Abstract

Rideshare platforms exert significant control over workers through algorithmic systems that can result in financial, emotional, and physical harm. What steps can platforms, designers, and practitioners take to mitigate these negative impacts and meet worker needs? In this paper, we identify transparency-related harms, mitigation strategies, and worker needs while validating and contextualizing our findings within the broader worker community. We use a novel mixed-methods study combining an LLM-based analysis of over 1 million comments posted to online platform worker communities with semi-structured interviews with workers. Our findings expose a transparency gap between existing platform designs and the information drivers need, particularly concerning promotions, fares, routes, and task allocation. Our analysis suggests that rideshare workers need key pieces of information, which we refer to as indicators, to make informed work decisions. These indicators include details about rides, driver statistics, algorithmic implementation details, and platform policy information. We argue that instead of relying on platforms to include such information in their designs, new regulations requiring platforms to publish public transparency reports may be a more effective solution to improve worker well-being. We offer recommendations for implementing such a policy.

Figures (17)

• Figure 1: Algorithmic Landscape of Rideshare Platforms described using Uber's terminologies
• Figure 2: Overview of our Methods
• Figure 3: Annotations of Driver Concerns for Surge Pricing (left) and Ride Offer (right)
• Figure 4: Hold button (left, P3) and Ride Available Nearby feature (right, P6). P3 and P6 propose these interventions to prioritize safety over algorithmic management during a ride.
• Figure 5: Evidence of algorithmic wage and price discrimination shared by P8. The earnings are redacted to preserve participant privacy.