Uber face AI facial recognition discrimination claim
The Case
Uber Eats is at the centre or an Employment Tribunal case where the risks of AI are at its centre. The claimant was a courier for Uber Eats. He was deactivated from the Uber Eats platform after Uber’s facial recognition system failed to verify his identity. The system uses AI and facial recognition technology to confirm the identity of couriers before beginning a shift; the Claimant argues that the system’s failure to recognise him was due to his race, and that the subsequent deactivation was discriminatory.Uber Eats argue that the deactivation was not due to racial discrimination but was a result of the claimant’s failure to comply with the company’s delivery standards. They also stated that the system was not capable of racial bias.
Key Issues
The case has brought to light several key issues surrounding the use of AI and facial recognition systems, particularly regarding discrimination and bias. The claimant’s argument raises the question of whether AI systems can be racially biassed. While Uber Eats argued that their system does not see race or skin colour, there is concern that these systems are trained on data associated with white male subjects, leading to discriminatory outcomes.
The case also highlights the uncertainty of AI liability. If an AI system makes a decision that has a significant impact on a person’s life, such as deactivating a courier’s account, who is responsible for that decision? This question is difficult to address, given the “black box” nature of these systems which makes it harder to identify how an outcome was made, and therefore who (if anyone) was responsible for it.
Following the dismissal of Uber’s request to strike out last year, the case is awaiting a final decision in the Employment Tribunal.
Discussion Questions
- How can we improve the transparency of AI systems to better understand their decision-making processes?
- What legislation (if any) should be put in place to regulate discriminatory AI outputs?