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Lawsuit filed over allegations of Uber face biometrics bias

Lawsuit filed over allegations of Uber face biometrics bias

A former Uber driver who lost his job after the company’s face biometric identity verification system failed to recognize him is now suing Uber, The Guardian reports.

The driver in the test case, who asked not to be named, accused the firm of indirect race discrimination after the app locked him out, first temporarily, and then permanently.

Uber has used Microsoft-powered biometric software since April 2020 in the UK, and this is not the first time it has come under scrutiny for allegedly flawed face biometrics that prompted the blocking out of drivers.

Last March, an investigation by Wired showed 14 Uber drivers said they had their contracts terminated after being accused of subcontracting their shifts to other individuals because the selfie biometric system in place failed to recognize who they were.

In the same month, the App Drivers and Courier Union (ADCU) and Worker Info Exchange (WIE) then called on Microsoft to temporarily suspend and review the biometric identity verification system.

Fast forward to this week, and the recent Guardian investigation seems to suggest 35 other drivers have had their registration with Uber terminated as a result of alleged mistakes with the facial verification software since the start of the pandemic.

Commenting on the news, Uber said it “strongly refutes the completely unfounded claims” and that it was “committed to fighting racism and being a champion for equality – both inside and outside our company.”

The firm also clarified that drivers can choose human verification of their picture, and when technology is chosen “there is always a minimum of two human expert reviews prior to any decision to remove a driver.”

The Independent Workers’ Union of Great Britain (IWGB) and the Black Lives Matter organization are now backing the action. Microsoft has so far declined to comment on the issue as it is currently an ongoing legal case.

Demographic biases are a known issue in facial biometric applications, with several researchers around the world currently working to mitigate and ultimately eradicate them.

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