research-article

Open Access

The Fallacy of AI Functionality

Authors:
Inioluwa Deborah Raji

University of California, Berkeley, USA

University of California, Berkeley, USA
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,
I. Elizabeth Kumar

Brown University, USA

Brown University, USA
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,
Aaron Horowitz

American Civil Liberties Union, USA

American Civil Liberties Union, USA
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,
Andrew Selbst

University of California, Los Angeles, USA

University of California, Los Angeles, USA
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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and TransparencyJune 2022Pages 959–972https://doi.org/10.1145/3531146.3533158

Published:20 June 2022Publication History

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 959–972

ABSTRACT

Deployed AI systems often do not work. They can be constructed haphazardly, deployed indiscriminately, and promoted deceptively. However, despite this reality, scholars, the press, and policymakers pay too little attention to functionality. This leads to technical and policy solutions focused on “ethical” or value-aligned deployments, often skipping over the prior question of whether a given system functions, or provides any benefits at all. To describe the harms of various types of functionality failures, we analyze a set of case studies to create a taxonomy of known AI functionality issues. We then point to policy and organizational responses that are often overlooked and become more readily available once functionality is drawn into focus. We argue that functionality is a meaningful AI policy challenge, operating as a necessary first step towards protecting affected communities from algorithmic harm.

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency
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The Fallacy of AI Functionality

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

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The Fallacy of AI Functionality

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