When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms

Abstract

Providing transparency into operational processes can change consumer and worker behavior. However, it is unclear whether operational transparency is beneficial with potentially biased service providers. We explore this in the context of ridesharing platforms where early evidence documents bias similar to what has been observed in traditional transportation systems. Platforms responded by reducing operational transparency through removing information about riders’ gender and race from the ride request presented to drivers. However, following this change, bias may still manifest through driver cancelation after a request is accepted, at which point the rider’s picture is displayed. Our primary research question is to what extent a rider’s gender, race, and perception of support for lesbian, gay, bisexual, and transgender (LGBT) rights impact cancelation rates. We investigate this through a large field experiment on a major ridesharing platform in Washington, DC. By manipulating rider names and profile pictures, we observe drivers’ behavior patterns in accepting and canceling rides. Our results confirm that bias at the ride request stage has been eliminated. However, after acceptance, racial and LGBT biases are persistent, while we find no evidence of gender biases. We also explore whether peak times moderate (through increased pay to drivers) or exacerbate (by signaling that there are many riders, allowing drivers to be more selective) these biases. We find a moderating effect of peak timing, with lower cancelation rates for non-Caucasian riders. We do not find a similar moderating effect for riders that signal support for the LGBT community. This paper was accepted by Vishal Gaur, operations management.