A Doorman for the Masses—Debunking Attacks on Facial Recognition, With Daniel Castro

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).