Aug 19, 2020 - Technology

Beating AI bias in facial recognition

Illustration of a face being scanned by artifical intelligence
Illustration: Sarah Grillo/Axios

Identity-verification startup Onfido is training its machine-learning system to reduce the bias that leads AI to make more facial recognition errors with dark-complexioned customers than those with lighter skin.

Why it matters: The pandemic-driven boom in telemedicine and fintech has made accurate remote identity-verification technology increasingly important, but these systems will only work fairly if they can identify customers of all races and ethnicities.

How it works: Onfido provides remote identity verification by analyzing the face on a government-issued ID document and comparing it to a freshly captured selfie or video.

  • The company's face-matching algorithm is able to use image recognition to determine whether the face in the selfie is the same as the one on the ID document, confirming identity for remote banking, admission into an event and more.
  • "Essentially, we're replicating what happens in-person in a bank branch and making it digital," says Husayn Kassai, Onfido's CEO.
  • That service that has become more valuable as the pandemic has pushed such interactions online.

By the numbers: Onfido has a market-leading false acceptance rate of 0.01%, which means there's only a 1 in 10,000 chance of incorrectly matching a selfie with an ID.

  • But while ID holders of European nationalities have a false acceptance rate of 0.019% and those in the Americas 0.008%, ID holders of African nationalities had a false acceptance rate of 0.038%.

Yes, but: Onfido's rate for African nationalities still represents a 60-fold improvement from a year ago — and that improvement required deliberate training.

  • Because Onfido has a much larger customer base in Europe, the dataset used to train the algorithm was unbalanced. With more light-skinned faces to learn from, the algorithm unsurprisingly performed best with light-skinned users.
  • To reduce bias, says Onfido's director of product Susana Lopes, the company "changed the way it trained the algorithm to help it learn from an unbalanced dataset."
  • Onfido is working with the U.K.'s Information Commissioner's Office to directly tackle facial recognition bias.

The bottom line: AI bias is almost invariably the result of bias in the real world. If companies offering AI solutions want to change that, says Kassai, they need to specifically "focus on fairness."

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