Incorporating the Concepts of Fairness and Bias into an Undergraduate Computer Science Course to Promote Fair Automated Decision Systems

Abstract

Data bias and algorithmic bias are the primary contributing factors for fairness-related risks in AI-based decision-making. The concept of fairness is comparatively new and is sometimes only discussed in detail in graduate-level courses such as Ethics of Artificial Intelligence, and Ethics and Governance of Artificial Intelligence. In addition, at the undergraduate level, a standalone course on fairness is not feasible due to the level of difficulty and the many other essential courses that need to be broached in a university's computer science curriculum. Therefore, instead of a standalone course, we have created a concise, high-level concept module on bias and fairness in automated decisions, with (1) lecture material, (2) demonstrations, and (3) assignments (i.e., exercises) on real-world datasets.

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