A preprint study published by researchers at George Washington University presents evidence of social bias in the algorithms ride-sharing startups like Uber, Lyft, and Via use to price fares. In a large-scale fairness analysis of Chicago-area ride-hailing samples — made in conjunction with the U.S. Census Bureau’s American Community Survey (ACS) data — metrics from tens of millions of rides indicate ethnicity, age, housing prices, and education influence the dynamic fare pricing models used by ride-hailing apps.
The idea that dynamic algorithmic pricing disproportionately — if unintentionally — affects certain demographics is not new. In 2015, a model used by the Princeton Review was found to be twice as likely to charge Asian Americans higher test-preparation prices than other customers, regardless of income. As the use of algorithmic dynamic pricing proliferates in other domains, the authors of this study argue it’s crucial that unintended consequences — like racially based disparities — are identified and accounted for.