Integrating Ethical Algorithm Design into Data Science Education: An Accessible, Practical Framework

This approach is designed to make ethics instruction more accessible to statistics instructors without a background in ethics. To address common barriers, including limitations of traditional methods, the breadth of content, and lack of practical guidance, we propose a flexible course framework centered on a core skill we call algorithmic translation: the ability to act as an informed intermediary between algorithmic decision-making processes and the people affected by them.

This framework can be implemented as a full course or as standalone modules within statistics classes. We present a course implementation taught as an upper-level elective in a statistics program at a large public research university, in which students learn to incorporate and evaluate fairness, explainability, interpretability, and privacy using emerging algorithmic techniques. These techniques are implemented in Python using AIF360 and AIX360 and applied to real-world datasets.