When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms

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