Large ride-hailing companies have been engaging in 'algorithmic wage discrimination' or paying workers different amounts to do the same thing for the same amount of time.

Research by UC Hastings law professor Veena Dubal discovered that Amazon, Lyft, Uber and other firms were personalising fares to individual drivers based on the reams of data they collect, using pay mechanisms to influence their behaviour, and manipulating pay to get the most out of them.

From a drivers' perspective, the algorithms resulted in lost, unpredictable and variable pay, according to Dubal, who drew on hundreds of interviews with gig workers themselves. And, given the proprietary nature of the algorithms, they had no knowledge of how the systems work or how their data is being used, Dubal said.

Uber denied that it personalises fares to individual drivers, and explicitly claims that it does not base rates on a driver’s ethnicity, their acceptance rate, nor prior trip history. Meanwhile, a spokesperson for Lyft claimed that the Dubal’s paper was biased and cherry-picked data.

The study raised concerns about the fairness and legality of rideshare company pay, and about the opacity of the systems being used.