Privacy Resources

Choose Privacy Every Day. An American Library Associations project to promote privacy awareness, with resources for librarians and the general public.

Coded Bias. Automated decision making has the unprecedented power to disseminate bias at scale. The artificial intelligence algorithms that power facial-recognition and decision-making software already often decide who gets hired, who gets health care, who gets home loans, and who gets undue police scrutiny. Coded Bias tells the story of mathematicians and data scientists who are leading the fight for ethical use of these and future technologies. The film starts with the fallout from an MIT Media Lab researcher’s discovery that facial recognition does not identify dark-skinned faces and women's faces accurately, then follows her journey to push for the first legislation in the U.S. to govern against bias in the algorithms.

Data Privacy Day 2021 Outreach: Six Words about Privacy. Boiling thoughts down to six words highlights what people consider to be the most important dimensions of a topic. On Data Privacy Day 2021, we look into the topic of privacy through the lens of the University of Michigan's Six Words about Privacy project. EDUCAUSE, January 2021.

Digital Contact Tracing for Pandemic Response: Ethics and Governance Guidance. A team of experts from Johns Hopkins University and other world-renowned institutions has crafted clear and detailed guidelines to help manage the creation, implementation, and application of digital contact tracing. Edited by Jeffrey Kahn, PhD, MPH, and Johns Hopkins Project on Ethics and Governance of Digital Contact Tracing Technologies, 2020.

Digital Ethics in Higher Education: 2020 John O’Brien discusses how new technologies, especially those relying on artificial intelligence or data analytics, are exciting but also present ethical challenges that deserve our attention and action. (EDUCAUSE, May 2020)

Disparities in Students’ Propensity to Consent to Learning Analytics. Use of university students’ educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases are unintentionally introduced into predictive models. This study finds that students identifying ethnically as Black were significantly less likely to respond and self-reported lower levels of institutional trust. Female students reported concerns with data collection but were also more comfortable with use of their data by instructors for learning engagement purposes. Students’ comments corroborate these findings and suggest that agency alone is insufficient; institutional leaders and instructors also play a large role in alleviating the issue of bias.

Warren Li, Kaiwen Sun, Florian Schaub & Christopher Brooks; International Journal of Artificial Intelligence in Education (2021)

Dissonance 2023 presented a screening of another film from Women Make Movies, TikTok, Boom., and hosted a conversation with filmmaker Shalini Kantayya and a panel of U-M faculty, staff, and students.

Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. A 2012 U.S. Department of Education Office of Educational Technology issue brief intended to help policymakers and administrators understand how analytics and data mining have been—and can be—applied for educational improvement.

Ethics, Big Data, and Analytics: A Model for Application. The use of big data and analytics to predict student success presents unique ethical questions for higher education administrators relating to the nature of knowledge; in education, "to know" entails an obligation to act on behalf of the student. The Potter Box framework can help administrators address these questions and provide a framework for action. (EDUCAUSE 2013)

From Ancient to Modern: The Changing Face of Personal Data. A look at privacy from ancient to modern times by Cherri-Ann Beckles. (IAPP Privacy Perspectives, April 2017).