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Gender Shades: Uncovering gender and skin-type bias in commercial AI products

Gender Shades is an excavation of inadvertent negligence that will cripple the age of automation and exacerbate inequality if left to fester.

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Bug Bounties for Algorithmic Harms Report

Lessons from cybersecurity vulnerability disclosure for algorithmic harms discovery, disclosure, and redress, led by AJL researchers Josh Kenway, Camille François, Sasha Costanza-Chock, Inioluwa Deborah Raji, and Dr. Joy Buolamwini.

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Facial Recognition Technologies in the Wild: A Call for a Federal Office

In Facial Recognition Technologies in the Wild: A Call for a Federal Office, researchers Erik Learned-Miller, Joy Buolamwini, Vicente Ordóñez, and Jamie Morgenstern propose an FDA-inspired model that categorizes facial recognition technologies.

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Facial Recognition Technologies: A Primer

Facial Recognition Technologies: A Primer provides a basic introduction to the terminology, applications, and difficulties of evaluating this complex set of technologies.

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Actionable Auditing: Investigating the Impact of Biased Performance Results of Commercial AI Products

Although algorithmic auditing has emerged as a key strategy to expose systematic biases in software platforms, we struggle to understand the real impact of these audits.

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