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Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing

2020/01/03 by Inioluwa Deborah Raji, Andrew Smart, Raji, Inioluwa Deborah +15 · 1 voice · 161 citations
Computer Science · Mathematics · Social Sciences · #Accountability #Accounting #Audit #Blockchain Technology Applications and Security #Business #Closing (real estate) #Computer science #Computer security #Computers and Society (cs.CY) #Dead end #End-to-end principle #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Finance #Flow (mathematics) #Internal audit #Law #Mathematics #Political science #Process management #cs.CY

paper · pdf · doi:10.48550/arxiv.2001.00973

published in arXiv (Cornell University) (Cornell University) · Accepted to ACM FAT* (Fariness, Accountability and Transparency) conference 2020. Full workable templates for the documents of the SMACTR framework presented in the paper can be found here https://drive.google.com/drive/folders/1GWlq8qGZXb2lNHxWBuo2wl-rlHsjNPM0?usp=sharing

arxiv created 2020/01/03 · openalex publication_date 2020/01/03 · arxiv published 2020/01/03 · arxiv updated 2020/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, emergent issues can become difficult or impossible to trace back to their source. In this paper, we introduce a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle. Each stage of the audit yields a set of documents that together form an overall audit report, drawing on an organization's values or principles to assess the fit of decisions made throughout the process. The proposed auditing framework is intended to contribute to closing the accountability gap in the development and deployment of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity.

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