Resources
Automation + AI

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

Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
2020
ACM Conference on Fairness, Accountability, and Transparency
Author(s): 
Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, Parker Barnes
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
Source Sector(s)
Academic
Benefits Program
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Level of Government
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Location
Format
Academic Article

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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