Uber's Surge Pricing: Potential Bias along Racial Lines - A Case Study in Safe and Secure AI
February 3, 2016
Uber's surge-pricing algorithm, designed to regulate car availability, may have unintentionally resulted in disproportionate service quality. Shorter wait times were reportedly observed in majority white neighborhoods. This AI incident maps to the Govern function in HISPI Project Cerebellum Trusted AI Model (TAIM). Ready to help prevent such incidents and promote trustworthy AI? JOIN US
- Alleged deployer
- uber
- Alleged developer
- uber
- Alleged harmed parties
- poor-neighborhoods, neighborhoods-of-color
Source
Data from the AI Incident Database (AIID). Cite this incident: https://incidentdatabase.ai/cite/407
Data source
Incident data is from the AI Incident Database (AIID).
When citing the database as a whole, please use:
McGregor, S. (2021) Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database. In Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). Virtual Conference.
Pre-print on arXiv · Database snapshots & citation guide
We use weekly snapshots of the AIID for stable reference. For the official suggested citation of a specific incident, use the “Cite this incident” link on each incident page.