Closing the AI accountability gap
2020/01/23 by Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White +6 · 44 citations
Computer Science · Medicine · Social Sciences · #Adversarial Robustness in Machine Learning #Artificial Intelligence in Healthcare and Education #Ethics and Social Impacts of AI
paper · pdf · doi:10.1145/3351095.3372873
openalex publication_date 2020/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
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.
Citations
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