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Ninth Circuit:
Uber’s Reliance on Ratings in Deactivating Drivers Survives
Informal Survey Conducted by Counsel Failed to Support Claim of Disparate Impact Where Polling Did Not Establish Proper Denominators for Each Racial Group
By Kimber Cooley, Staff Writer
The Ninth U.S. Circuit Court of Appeals held yesterday that a survey—conducted by counsel for a plaintiff in a putative class action against an app-based rideshare company alleging racial discrimination based on the use of customer ratings in termination decisions—is insufficient to support the asserted claims where it fails to compare the number of drivers let go due to low ratings against the total number of drivers of that race.
The panel held that the survey, as conducted, cannot establish disparate impact based on race despite assertions in the complaint that the rideshare company has expressed concerns that racial discrimination can play a part in how well a particular driver is rated.
A three-judge panel affirmed, in a memorandum decision, the dismissal for failure to state a claim under Federal Rule of Civil Procedure 12(b)(6) by District Court Judge Vince Chhabria for the Northern District of California. The case was heard before Circuit Judges Daniel P. Collins, Danielle J. Forrest, and Jennifer Sung.
Class Action
Appealing the dismissal was Thomas Liu, who filed a putative class action in October 2020 against Uber Technologies, Inc. asserting racial discrimination claims under Title VII of the Civil Rights Act of 1964, codified at 42 U.S.C. §2000e-2, and California’s Fair Employment and Housing Act (“FEHA”), found at Government Code §12940.
Liu, who according to the complaint is “Asian and from Hawaii and speaks with a slight accent,” alleges that Uber’s use of a star-rating system, by which passengers are asked to rate their experience on a scale of one to five after each ride, in making decisions to terminate drivers discriminates against non-white contractors. The complaint invokes theories of both disparate impact and disparate treatment.
Liu alleges that he was terminated by the company as a driver in the San Diego area when his average star rating fell below 4.6.
Chhabria granted Uber’s motion to dismiss on Sept. 28, 2022. After Liu failed to file an amended complaint, the judge dismissed the case on Oct. 27, 2022.
Disparate Impact
The panel noted that, in order to state a claim for discrimination under Title VII and FEHA, a plaintiff must plausibly allege that specific employment practices have caused a significant disparate impact on a protected class or group.
To establish a disparate impact in the present case, the complaint describes the results of a survey of Uber drivers conducted by Liu’s counsel Shannon Liss-Riordan, a labor attorney with the Boston firm Lichten & Liss-Riordan specializing in class actions. The survey was conducted by email to approximately 20,000 drivers who are also clients of Liss-Riordan.
The complaint alleges that approximately 4,000 drivers responded and summarizes the results of the survey by saying:
“17.4% of white respondents indicated that they had been deactivated by Uber based on star ratings. In contrast, 24.6% of Asian respondents, 24.1% of Black respondents, and 24.9% of respondents who identified their race as ‘Other’ than the choices provided indicated that they had been deactivated by Uber based on star ratings. Only 16.9% of Latinx respondents indicated that they had been deactivated by Uber based on star ratings.” Liu’s complaint asserts that Mark Killingsworth, a professor in the Rutgers University Department of Economics, has “examined the survey responses and found the results to be highly statistically significant that race is associated with Uber drivers in the survey reporting that they had been deactivated based on their star ratings.”
Unpersuaded by Killingsworth’s analysis, the judges said that “[f]or several reasons, we agree with the district court that the allegations concerning counsel’s survey are insufficient to raise a plausible inference that there is a significant racial disparity in star-ratings-based terminations among Uber drivers.”
Insufficient Survey
The panel pointed out that the “crucial element” of a disparate impact claim requires a showing that the employment practice in question causes a disparate impact on the basis of race. In the present case, they opined:
“[T]he survey described in the operative complaint does not actually show that non-white drivers are terminated due to low star ratings at different rates than white drivers…[T]he survey failed to compare, for each racial group, the number of drivers of that race who were terminated due to low star ratings against the total number of drivers of that race in the entire survey pool (assuming arguendo that, at the pleading stage, the entire survey pool is a reasonable proxy for the entire driver population).”
The jurists continued:
“Because the survey says nothing about the composition of the overall population of Uber drivers from which these star-based terminated drivers were drawn, it says nothing about whether Uber terminates white drivers due to the challenged practice at different rates than non-white drivers…. Indeed, as the district court recognized, because the survey used the incorrect denominator, the survey could show a disparity even if the challenged practice did not actually have a disparate impact….”
Respondent Confusion
The panel noted that there was confusion among the drivers responding to the survey, saying:
“[T]he survey ended up comparing a set of respondents who said they had been terminated to a set of respondents that included both persons terminated for other reasons as well as a large number of persons who were not terminated at all. The resulting apples-to-oranges comparison means that the survey question was so poorly framed that it did not even accomplish its declared goal of comparing star-ratings-based terminations to terminations based on other grounds.”
The judges took issue with the language used in the survey and said that “a further design flaw—which the complaint itself candidly acknowledged—is that the survey’s use of the term ‘Latinx’ apparently caused numerous respondents who identify as Latino or Hispanic” to check the box marked “Other” in response to a question inquiring about their race.
The panel declared that this flaw made it “impossible to draw any ‘meaningful’ conclusions about the survey’s ‘Latinx’ and ‘Other’ numbers.”
Social Science Literature
Liu’s complaint cites social science literature analyzing the effects of racial bias in customer ratings, noting that Uber had relied on such concerns in defending its now-abandoned decision to disallow tipping on its app.
The judges remarked that “[t]his literature raises an important concern about rating systems, and it may support an inference of a discriminatory causal relationship if Uber’s rating system is producing a significant racial disparity in terminations.” However, they concluded:
“But even assuming that, in an appropriate case, reliance on publicly available reports and studies providing relevant evidence of real-world conditions may provide a basis for plausibly inferring a statistical disparity with respect to a particular defendant, that is not the case here. The cited materials in Liu’s complaint lack sufficient data concerning relevant actual conditions to provide a non-speculative basis for plausibly inferring that any such significant disparity is actually occurring with respect to Uber.”
Disparate Treatment
A plaintiff seeking to rely on a disparate treatment theory of liability must establish that the defendant acted with a discriminatory intent or motive. Liu asserts that the complaint gives rise to an inference of intentional discrimination based on Uber’s decision to use the star-rating system despite its awareness that racial biases play into such ranking practices.
Rejecting this theory, the panel explained that the court has “squarely held” that allegations that an employer is aware of adverse consequences of a policy on a protected group are insufficient to establish discriminatory motive.
They declined to find intentional discrimination based on Liu’s contention of a disparate impact as “no such disparity has been adequately pleaded here.”
The case is Liu v. Uber Technologies, Inc., 22-16507, 22-16712.
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