The Fallacy of AI Functionality

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.

The authors' own abstract, copied from arxiv.org.

How to cite it

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Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. D. (2022). The Fallacy of AI Functionality. Preprint, not yet peer-reviewed. https://arxiv.org/abs/2206.09511

Raji, Inioluwa Deborah, et al. "The Fallacy of AI Functionality." Preprint, not yet peer-reviewed, 2022, https://arxiv.org/abs/2206.09511.

@article{raji2022the,
  title = {The Fallacy of AI Functionality},
  author = {Inioluwa Deborah Raji and I. Elizabeth Kumar and Aaron Horowitz and Andrew D. Selbst},
  journal = {Preprint, not yet peer-reviewed},
  year = {2022},
  url = {https://arxiv.org/abs/2206.09511},
}

What it is

Raji and colleagues argue many deployed AI systems fail on their own stated terms — they simply do not work — and that this belongs in the harm conversation alongside bias.

Topics