Cyber & IT Supervisory Forum - Additional Resources

Aylin Caliskan, Joanna J. Bryson, and Arvind Narayanan. “Semantics Derived Automatically from Language Corpora Contain Human-Like Biases.” Science 356, no. 6334 (April 14, 2017): 183–86. Sina Fazelpour and Maria De-Arteaga. “Diversity in Sociotechnical Machine Learning Systems.” Big Data and Society 9, no. 1 (2022). Fairlearn. “Fairness in Machine Learning.” Fairlearn 0.8.0 Documentation, n.d. URL Safiya Umoja Noble. Algorithms of Oppression: How Search Engines Reinforce Racism. New York, NY: New York University Press, 2018. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science 366, no. 6464 (October 25, 2019): 447–53. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. "A Reductions Approach to Fair Classification." arXiv preprint, submitted July 16, 2018. Moritz Hardt, Eric Price, and Nathan Srebro. "Equality of Opportunity in Supervised Learning." arXiv preprint, submitted October 7, 2016. Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu. "Fair Regression: Quantitative Definitions and Reduction-Based Algorithms." Proceedings of the 36th International Conference on Machine Learning, PMLR 97:120-129, 2019. Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. “Fairness and Abstraction in Sociotechnical Systems.” FAT* '19: Proceedings of the Conference on Fairness, Accountability, and Transparency, January 29, 2019, 59–68. Matthew Kay, Cynthia Matuszek, and Sean A. Munson. “Unequal Representation and Gender Stereotypes in Image Search Results for Occupations.” CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, April 18, 2015, 3819–28.

Software Resources aequitas AI Fairness 360: Python R algofairness fairlearn fairml fairmodels

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