Welcome to Data Ethics

On this site we map the burgeoning field of data ethics, an interdisciplinary area of study, spanning computer science, data science, statistics, the social sciences, and the humanities. There is growing recognition of the importance of data ethics as a foundation of professional practice and a pillar of education in data-driven fields. We have designed this roadmap as a tool for educators to explore the expanding field of data ethics coursework and literature in the hopes that it will contribute to new and expanded data ethics course design. This tool was developed through a collaboration at Columbia University between Jonathan Reeve, Isabelle Zaugg, Tian Zheng, Serena Yuan, and Zhuohan Zhang. To develop our tool, we used syllabi crowdsourced from Fiesler et al.’s 2017 study and elsewhere, and created a graph database, using semantic web technologies (linked open data, in Turtle RDF), that encodes: 1) courses related to data ethics, 2) their assigned texts, scraped from syllabi, and 3) other texts related to the field, and the texts they cite. We then created a graphical visualization, an explorer which is designed to provide insight into the following questions relevant to educators:

• Which texts are most frequently assigned, and cited? And which texts are excluded? Are there important outliers that deserve more attention?
• Where are the disciplinary divides, and how can they be bridged?
• What are similarities and differences between data ethics courses?
• Which institutions, scholars, educators are innovating in this space?
• What are the major topic areas?

Read the syllabus for the pilot course which resulted from this project, People vs. Algorithms: Data Ethics in the 21st Century.

We would like to thank the following scholars and literature that informed our work: Casey Fiesler, Michael Zimmer, Karina Alexanyan, Daniel Castaño, Frédérick Bruneault

Brusseau, J. (n.d.). AI Ethics Site—List of Courses and Course Materials. AI Ethics Workshop. Retrieved July 8, 2021, from http://ai.ethicsworkshop.org/course-materials

Dencik, L., Hintz, A., Redden, J., & Treré, E. (2019). Exploring Data Justice: Conceptions, Applications and Directions. Information, Communication & Society, 22, 873–881.

Fiesler, C. (2019, November 21). Tech Ethics Curricula: A Collection of Syllabi. Medium. https://cfiesler.medium.com/tech-ethics-curricula-a-collection-of-syllabi-3eedfb76be18

Fiesler, C., Garrett, N., & Beard, N. (2020). What Do We Teach When We Teach Tech Ethics?: A Syllabi Analysis. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 289–295.

Metcalf, J., Crawford, K., & Keller, E. (2015). Pedagogical Approaches to Data Ethics (p. 16) [Draft Version, Produced for Council for Big Data, Ethics, and Society]. Data & Society Research Institute.

Raji, I. D., Scheuerman, M. K., & Amironesei, R. (2021). You Can’t Sit With Us: Exclusionary Pedagogy in AI Ethics Education. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 515–525.

Zeffiro, A. (2021). From Data Ethics to Data Justice in/as Pedagogy (Dispatch). Studies in Social Justice, 15, 450–457.