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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The document presents the SMACTR framework, an end-to-end internal algorithmic auditing process designed to embed ethical, transparent, and systematic audits throughout the AI development lifecycle.

It emphasizes the importance of proactive internal audits before deployment to identify and mitigate ethical risks, with a focus on documentation, stakeholder involvement, and continuous risk assessment to ensure responsible AI innovation and alignment with organizational values.