Requesting an Uber or Lyft to a lower-income community? That could cost you – in fact, the algorithms they use may be biased against you, or at least your travel plans.
This is according to a study by George Washington University published last month.
The report found that passengers being picked up or dropped off in lower-income communities or in sectors with minorities were being charged more per mile.
“Uber determines demand for rides using machine learning models, using forecasting based on prior demand to determine which areas drivers will be needed most at a given time,” reads the study by Aylin Caliskan and Akshat Pandey. “While the use of machine learning to forecast demand may improve ride-hailing applications’ ability to provide services to their riders, machine learning methods have been known to adopt policies that display demographic disparity in online recruitment, online advertisements, and recidivism prediction.”