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Datasheets for Datasets, , , , , , and . (2018)cite arxiv:1803.09010Comment: Working Paper, comments are encouraged.Improving fairness in machine learning systems: What do industry practitioners need?, , , , and . (2018)cite arxiv:1812.05239Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019).Manipulating and Measuring Model Interpretability., , , , and . CoRR, (2018)Locally Private Bayesian Inference for Count Models., , , and . CoRR, (2018)Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata., , , , and . Proc. ACM Hum. Comput. Interact., 6 (CSCW2): 1-29 (2022)Social and Technical Trade-Offs in Data Science., , , and . Big Data, 5 (2): 71-72 (2017)Learning the Structure of Deep Sparse Graphical Models., , and . AISTATS, volume 9 of JMLR Proceedings, page 1-8. JMLR.org, (2010)A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications., , , , , , , , , and 7 other author(s). CoRR, (2023)From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence., , , , and . HCOMP, page 35-47. AAAI Press, (2021)Bridging the gap from AI ethics research to practice., , , , , , , and . FAT*, page 682. ACM, (2020)