Data Ethics is a required course in Florida State University’s Interdisciplinary Data Science Master’s Program (IDS). The IDS program was brand new when I arrived at FSU. Students in the program will soon be beginning, or in some cases have already started, their careers in data science, mostly in private industry and in governmental agencies. Few have any background in philosophy.
As a graduate student, I liked teaching medical ethics to pre-med students because I think developing a sensitivity to ethical issues makes students better future clinicians. I enjoy teaching Data Ethics for a similar reason. It’s easy for a data scientist (or indeed, for any of us in our professional lives!) to get their work done by putting their head down, ignoring the social context of that work and the ethical concerns it might raise. In Data Ethics, I try to help students develop an ability to identify ethical issues as they arise and become comfortable discussing difficult moral and political questions. When concepts like privacy and transparency are mentioned in data science, they’re often treated just as buzzwords. So I also want to equip students with arguments about why exactly such values matter.
My experiences teaching applied ethics to undergrads made me want to avoid framing the course around general moral theories. I find that introducing the “Big Three” ethical theories in a cursory way just leaves students with caricatures of each family of views and encourages them to think of philosophical theories as items on a menu to be selected when it’s convenient. Instead of a theory-driven approach, I structure the class around a number of moral values/topics including privacy, autonomy, and fairness. When developing the initial reading list, I drew from a previous course on the Ethics of Technology that I co-taught with my friend Kathleen Creel (who is more of an expert in this area than me!) and looked at a bunch of syllabi online from folks like Solon Barocas, Moritz Hardt, Karen Levy, Jon Kleinberg, Milo Phillips-Brown, Yoel Roth, and Catherine Stinson. I also keep a folder on my desktop where I save papers that I might want to assign in future iterations of the class as I see them. Keeping the reading list up-to-date is one of the course’s biggest challenges.
I designed the assessments with an eye toward making the course useful for students. I think effective writing is an important skill for a wide range of jobs, including jobs in data science, where the emphasis is more often on programming and statistics. However, mastering the particular form of a philosophical essay didn’t seem all that important given IDS students’ career trajectories. So I decided instead to develop “Policy Memo” assignments, which require students to analyze a specific, real-world case of their choosing using the philosophical texts assigned for the course.
In the current iteration of the class, I assign two policy memos: one on privacy, and the other on discrimination and fairness. The assignment is quite regimented, with specific required sections (e.g., “Case Background,” “Policy Options”). I think this makes it less intimidating and hopefully more similar to writing tasks that the students might be asked to do in the future. The widespread availability of tools like ChatGPT has thrown a wrench into the policy memo assignments, though the requirement to engage with specific course readings—many of which are recent, such that there is little text about them on which LLMs can train—helps somewhat. (I’ve also been experimenting with the Google Docs method of discouraging overuse of LLMs. I have reservations about this approach, but that’s for a different blog post…)
Last summer, when I was updating the course, I saw a post online by Ignacio M. Sánchez Prado (via Anya Plutynski) recommending “The Book Club as a Pedagogical Tool.” I was intrigued by the idea, particularly since the IDS students’ other coursework is much less reading-intensive than a typical philosophy class. A book club assignment seemed like a good way for students to explore a topic in a more sustained way, to get to know one another, and to build confidence in their ability to get through a full-length book. I selected eight recent trade books covering topics like workplace surveillance, the risks of AI, content moderation, and informational privacy. Students chose a book at the beginning of the semester. Seven weeks later, they submitted discussion questions on Canvas and met in small groups over Zoom to discuss their books. Afterward, I asked them to write an individual reflection on the book and their group’s discussion.
Students’ (anonymous) feedback about the book club was overwhelmingly positive. I required each group to record their conversation on Zoom so that I could watch them back. Most groups had wide-ranging, interesting conversations. It is difficult to tell with certainty if a student really read the whole book (the assignment is more “teacher” than “cop”), and some of the groups’ discussions were more natural than others. Still, I plan to use the assignment again, in part because the students seem to really enjoy it. With data science changing constantly, I think it’s especially important for students to leave the class feeling curious and empowered to keep up to date with contemporary ethical challenges. My impression (and hope) is that the book club helps to foster this intrinsic motivation to pay attention to ethical issues in data science long after the course ends.
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Zina B. Ward
I am an Assistant Professor at Florida State University, with research interests in general philosophy of science and the history and philosophy of cognitive science. I received my PhD from the University of Pittsburgh and MPhil from Cambridge University, both in History and Philosophy of Science. My website is here.
