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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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Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker Incentives., , , , and . CoRR, (2020)Non-portability of Algorithmic Fairness in India., , , and . CoRR, (2020)Perturbation Sensitivity Analysis to Detect Unintended Model Biases., , and . EMNLP/IJCNLP (1), page 5739-5744. Association for Computational Linguistics, (2019)Advances and Open Problems in Federated Learning., , , , , , , , , and 48 other author(s). CoRR, (2019)Unintended machine learning biases as social barriers for persons with disabilitiess., , , , , and . ACM SIGACCESS Access. Comput., (2020)Dependency-based Semantic Interpretation for Answer Extraction, and . Proc. 2002 Australasian NLP Workshop, (2002)Evaluation Gaps in Machine Learning Practice., , , , and . FAccT, page 1859-1876. ACM, (2022)Re-imagining Algorithmic Fairness in India and Beyond., , , , and . FAccT, page 315-328. ACM, (2021)Model Cards for Model Reporting., , , , , , , , and . FAT, page 220-229. ACM, (2019)In Vitro and In Vivo Evaluations of Parsing Systems Within the Context of Answer Extraction, and . (2002)In preparation.