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Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?

, , and . Proc. ACM Hum. Comput. Interact., 6 (GROUP): 34:1-34:14 (2022)

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The Exploited Labor Behind Artificial Intelligence, , and . Noema, (Oct 13, 2022)Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?, , and . Proc. ACM Hum. Comput. Interact., 6 (GROUP): 34:1-34:14 (2022)Investigating Data Work Across Domains: New Perspectives on the Work of Creating Data., , , , , , , , , and . CHI Extended Abstracts, page 87:1-87:6. ACM, (2022)Studying Up Machine Learning Data: Why Talk About Bias When We Mean Power?, , and . CoRR, (2021)Wisdom for the Crowd: Discoursive Power in Annotation Instructions for Computer Vision., and . CoRR, (2021)Biased Priorities, Biased Outcomes: Three Recommendations for Ethics-oriented Data Annotation Practices., and . AIES, page 71. ACM, (2020)Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision., , and . Proc. ACM Hum. Comput. Interact., 4 (CSCW2): 115:1-115:25 (2020)The Data-Production Dispositif., and . CoRR, (2022)Documenting Data Production Processes: A Participatory Approach for Data Work., , , , , , and . CoRR, (2022)Documenting Computer Vision Datasets: An Invitation to Reflexive Data Practices., , , , , and . FAccT, page 161-172. ACM, (2021)