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

, , , , , , , , and . FAT*, page 33-44. ACM, (2020)

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Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs., , , and . FAccT, page 217-226. ACM, (2023)Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing., , , , , , , , and . FAT*, page 33-44. ACM, (2020)The Use and Misuse of Counterfactuals in Ethical Machine Learning., and . FAccT, page 228-236. ACM, (2021)Why Reliabilism Is not Enough: Epistemic and Moral Justification in Machine Learning., , , , and . AIES, page 372-377. ACM, (2020)Beyond the ML Model: Applying Safety Engineering Frameworks to Text-to-Image Development., , , , , and . AIES, page 70-83. ACM, (2023)Towards a critical race methodology in algorithmic fairness., , , and . FAT*, page 501-512. ACM, (2020)Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure., , , , , , , and . FAccT, page 560-575. ACM, (2021)From Plane Crashes to Algorithmic Harm: Applicability of Safety Engineering Frameworks for Responsible ML., , , , , , and . CHI, page 2:1-2:18. ACM, (2023)Statistical Methods for Auditing the Quality of Manual Content Reviews., , and . Tiny Papers @ ICLR, OpenReview.net, (2023)Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker Incentives., , , , and . CoRR, (2020)