Resource Automation + AI Audits + Accountability

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing

This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.

Organization: ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
Authors: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes
Published Year: 2020
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This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.

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