Thomas Liu, who hails from Hawaii, now lives in San Diego and of Asian descent, says that he was “deactivated” from Uber in October 2015 because according to court documents, “he noticed riders cancelling ride requests after he had already accepted the ride and the rider was able to view his picture…He also experienced riders asking where he was from in an unfriendly way.”Uber’s Rating System
Further, Liu’s lawsuit alleges that his rating fell below 4.6 stars out of five and “Uber drivers must maintain a minimum average rating that “has frequently been set very high, even close to a perfect a score.” In a blog post from 2016, Uber acknowledges that “tipping is influenced by personal bias” and cited a wage and litigation study from Cornell that determined both white and black people tipped white restaurant servers and taxi drivers better than black servers and drivers. With that knowledge it decided against including a tipping option into the app. “In the end, we decided against including one because we felt it would be better for riders and drivers to know for sure what they would pay or earn on each trip,” Uber posted. “Whether consciously or unconsciously, we tend to tip certain types of people better than others. This means two people providing the same level of service get paid different amounts. With Uber, drivers know that they earn the same for doing the same trip, no matter who they are or where they’re from.”
On June 10, Judge Vince Chhabria said "The answer in this case may be that the inference that Uber's practice is racially discriminatory is so strong that it's enough to have general allegations about the effect on people like Liu.” He said that one study Liu refers to in his amended complaint (Lui first filed in October and the judge said it was sparse and poorly drafted ) is titled "Discriminating Tastes: Customer Ratings as Vehicles for Bias," which argues that consumer-sourced ratings are highly likely to be influenced by bias, including by factors such as race. In fact the study uses Uber as a model and says the following:
“Our paper analyzes the Uber platform as a case study to explore how bias may creep into evaluations of drivers through consumer-sourced rating systems. A good deal of social science research suggests that aggregated consumer ratings are likely to be inflected with biases against members of legally protected groups… While companies are legally prohibited from making employment decisions based on protected characteristics of workers, their reliance on potentially biased consumer ratings to make material determinations may nonetheless lead to disparate impact in employment outcomes.”