Reflections from UPCEA 2025: Exploring Data Science and Ethics in Higher Education

On March 26, three members of the University of Chicago’s MS in Applied Data Science (MS-ADS) team—Brody Tate (Online Program Manager), Emma Kerr-Ketchum (Assistant Director of Instructional Services), and Taylor Alexander (Associate Director of Instructional Services)—led a roundtable at the 2025 UPCEA Conference, sparking rich conversations on the ethical dimensions of data science in higher education.

The session, titled “From Fear to Responsibility: Embracing Ethical Data Science in Higher Education,” drew a diverse group of education professionals, industry leaders, and faculty interested in sharing their experiences navigating the rapidly evolving world of generative AI and resulting data ethics questions.

Creating Space for Dialogue

From the start, the roundtable was designed to be interactive and reflective. The MS-ADS team prepared a short presentation on their approach to ethical data science, using a “three filters” framework—ethical collection, ethical consumption, and ethical interpretation. From there, participants were encouraged to share their reactions to that framework and the frameworks being used or discussed at their institutions as guardrails for the use of data science broadly, and generative AI more specifically.

“Going into the conference, I was looking forward to learning more about how other schools are thinking about what AI should be for—as educators, we’re building up the future framework of human work and activity and deciding what can and should be offloaded to technology is a central consideration in that process. Just as the rise of the internet reduced the educational focus on memorization, how will the rise of AI change the way our students approach the transformation of ideas and data from one format to another (e.g., from outline to draft, from pseudocode to product, and so on),” says Emma Kerr-Ketchum.

Participants discussed a wide range of topics—from the fear and hype surrounding generative AI to the long-term implications of AI in education. One theme that resonated throughout the session was the need for academic programs to move beyond technical training and engage students in the ethical concerns related to the use of tools like ChatGPT and comprehensive data sets more broadly, and the resulting responsibilities of staff and faculty to ensure that students are prepared for the rapidly evolving technological landscape that will continue to change long after students have graduated from a specific program.

AI in the Classroom: Opportunities and Uncertainties

A key part of the discussion centered on how universities are responding to generative AI tools like ChatGPT and how these technologies are reshaping both teaching and learning.

“It was exciting to hear other organizations had already started engaging in conversations around ethical approaches, some had not seen or heard of these discussions at all, and there was a range of folks saying how this roundtable really made them think or rethink their ethical considerations in AI use on their campuses,” says Brody Tate.

Ethics as a Core Component of Data Science Education

For the MS-ADS team, the roundtable reinforced the importance of embedding ethical questions into the data science curriculum—not as an add-on, but as an essential skill set.
As more universities integrate AI and data science into their offerings, conversations like this one are critical to ensuring those programs reflect not just technical excellence but thoughtful, socially aware innovation.

As a first-time presenter, I had no idea what to expect as AI integration is kind of a hot topic. We had incredibly engaging conversations with educators and administrators from around the country. In a time of uncertainty, it’s motivating to witness the impactful work being done in higher education,” says Taylor Alexander.

Looking Ahead

The team returned from UPCEA 2025 inspired by the thoughtful exchange of ideas and motivated to continue evolving the MS-ADS program in ways that reflect both the promise and responsibility of data science.