Facial recognition technology has faced widespread allegations of discrimination in recent years, leading some cities to restrict its use—but exactly how valid are these claims? Rob and Jackie sit down with ITIF’s vice president and director of the Center for Data Innovation, Daniel Castro, to discuss why many of the claims are misleading, and how facial recognition can make public and private services more accessible, efficient, and useful.

Mentioned:

Joy Buolamwini and Timnit Gebru, Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (FAT, 2018).
Jacob Snow, Amazon’s Face Recognition Falsely Matched 28 Members of Congress With Mugshots (ACLU, 2018).
NIST, NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software (NIST, 2019).

Related:

Daniel Castro, Note to Press: Facial Analysis Is Not Facial Recognition (ITIF, 2019).
Daniel Castro and Michael McLaughlin, Banning Police Use of Facial Recognition Would Undercut Public Safety (ITIF, 2019).
Daniel Castro and Michael McLaughlin, The Critics Were Wrong: NIST Data Shows the Best Facial Recognition Algorithms Are Neither Racist Nor Sexist (ITIF, 2020).
Information Technology and Innovation Foundation, ITIF Technology Explainer: What Is Facial Recognition? (ITIF, 2020).

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