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Best Practices for Teaching Ethics in Data Science

Published onJan 31, 2025
Best Practices for Teaching Ethics in Data Science

There is a growing awareness about the ethical and societal implications related to the use of data in AI and Generative AI. It is therefore critical to integrate Ethics in the curriculum of students in Data Science and Computer Science. As future practitioners, students need to be equipped with the language and critical thinking skills that enable them to engage in the discussions around the inherent normativity in building socio-technical systems. However, there are serious challenges in the implementation of normative elements in the curriculum of Data Science and Computer Science, namely the contrasting nature of these disciplines, poor collective knowledge and best practices, and lack of support and training for instructors in technical fields who wish to engage in these discussions. The session on Methods for Teaching Ethics in Data Science aims to foster a reflection on the challenges associated with teaching Ethics in technical or quantitative domains, sharing experiences and progress made, and establishing core best practices. Our goal is to build a community empowering data science instructors in teaching the new generation of data scientists and tech practitioners. Building on the first workshop on methods for teaching ethics in data science which tool place in May 2023, we propose the second edition of the workshop with one 30 minute plenary talk (by Dr. Benedetta Giovanola, Professor, University of Macerata) followed by 10-15 min talks on teaching AI Ethics selected by a technical program committee (see draft CFP below). We will publish the proceedings online through ADSA.

10:15 - 10:45  AI Ethics and Data Science: Why should the human be in the loop?
Benedetta Giovanola, Full Professor of Ethics at University of Macerata, Italy
The plenary talk will argue the importance of ethics for the design, development, deployment and governance of AI systems and will highlight the key role of data ethics to foster human-centered AI. It will provide a critical perspective beyond the AI hype and will offer insights on why and how to integrate ethical concerns into Data Science and Computer Science both at the level of teaching and practice, with the aim of effectively keeping humans in the loop.

10:45 - 11:15  Teaching data science critique and ethics through sociotechnical surveillance studies
Nick Raab, postdoctoral researcher at California State University Los Angeles, USA Ethics have become an urgent concern for data science research, practice, and instruction in the wake of growing critique of algorithms and systems that reinforce structural oppression. There is increasing desire from educators to craft curricula that speak to these critiques, yet much ethics education remains individualized, focused on specific cases, or too abstract and unapplicable. We synthesized critical data science works and designed a data science ethics course that spoke to the social phenomena at the root of critical data studies -- oppression, social systems, power, history, and change -- through analysis surveillance systems. By analyzing student reflections and final projects, we determined that at the conclusion of the semester, students had developed critical analysis skills that allowed them to investigate surveillance systems of their own and identify their benefits, harms, and interplay with social systems, all while considering dimensions of race, class, gender, and more.

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