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Anne L. Washington

Assistant Professor of Data Policy

Applied Statistics, Social Science, and Humanities

Anne L. Washington is an Assistant Professor of Data Policy. She applies her expertise in digital innovation to data governance issues. As a computer scientist trained in organizational ethnography, she unites inductive qualitative research methods with emerging technology. At the broadest level, her multi-disciplinary work considers the impact of technology on society through the lens of digital record keeping. Her academic contributions have roots in management information systems, law, and informatics. 

She has been PI for over $1 millions in grants including the prestigious National Science Foundation CAREER award. She has testified before Congress on Artificial Intelligence in financial systems and also was chair of the international AIES AI, Ethics, and Society conference. 

She holds an undergraduate degree in computer science from Brown University, a graduate degree in Library & Information Science from Rutgers University, and a doctorate in Information Systems and Technology Management from The George Washington University. She has served as a fellow at the Data & Society Research Institute of New York,  the Peter Pribilla Foundation of Munich and Leipzig Germany and currently is faculty fellow at Berkman Klein Center for Internet & Society.  She is a member of the ACM Association of Computing Machinery, AOM Academy of Management, and the IEEE - Institute of Electrical and Electronics Engineers

Her book, Ethical Data Science: Prediction in the Public Interest, will be published in the winter 2023/2024 with Oxford University Press UK.


Applied Statistics for Social Science Research

Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

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Ethics of Data Science

Course is designed to build students’ ethical imaginations and skills for collecting, storing, sharing and analyzing data derived from human subjects including data used in algorithms. The course provides historical background to understand the tenets of informed consent, discrimination, and privacy. Using case study design, students will explore current applications of quantitative reasoning in organizations, algorithmic transparency, and unintended automation of discrimination via data that contains biases rooted in race, gender, class, and other characteristics.
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Applied Statistics, Social Science, and Humanities