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Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large.
Limitations of Pinned AUC for Measuring Unintended Bias
Daniel Borkan, Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2019 · 1903
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Evaluating the Predictive Validity of the Compas Risk and Needs Assessment System
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Certifying and Removing Disparate Impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15) . ACM, New York, NY, USA, 259–268
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Perspective API
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Wikipedia Talk Labels: Toxicity
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Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research) , Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR, New York, NY, USA, 77–91
Adversarial Removal of Demographic Attributes from Text Data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 11–21
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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Counterfactual Fairness in Text Classification through Robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. 2018 · 2018
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Unintended bias and names of frequently targeted groups
Lucy Vasserman, John Li, CJ Adams, Lucas Dixon. 2018 · 2018
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The cost of fairness in binary classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research) , Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR, New York, NY, USA, 107–118
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Joy Buolamwini and Timnit Gebru. 2018 · 2018
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Measuring and Mitigating Unintended Bias in Text Classification. In Proceedings of AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society
Lucas Dixon, John Li, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman. 2018 · 2018
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Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation
S. J. Mason and N. E. Graham. [n. d.]
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Reducing Gender Bias in Abusive Language Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2799–2804
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Model Cards for Model Reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19) . ACM, New York, NY, USA, 220–229
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
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