80-836 Topics on Ethics for AI
12 units, Carnegie Mellon University
This course provides an in-depth introduction to fundamental concepts, structures, and theories in normative ethics with an emphasis on how these relate or apply to the development, deployment, and evaluation of artificial intelligence (AI). One main course goal is to provide students with a precise account of alternative ethical theories, such as various forms of consequentialism and non-consequentialism, deontology, and the role of virtue in various ethical theories. This portion of the course details the elements from which ethical theories are constructed, the relationships or structures between those elements that help to constitute specific theories, and the impact of alterations in those elements or structures on the way theories function. A second goal is to explain the relationship between ethical theories, decision making, and various types of ethical assessment. A third goal is to explicate key concepts from normative ethics that are centrally relevant to ethical issues in AI, including, but not limited to: autonomy, benefit, coercion, deception, domination, exploitation, fairness, harm, justice (and its various types), manipulation, privacy, respect, risk, and others. By the end of the course, students should have sufficient fluency with the above material to: avoid common mistakes and misperceptions about various ethical theories, articulate central challenges to ethical reasoning about AI systems and to creating AI systems that engage in ethical reasoning, understand the crux of debates in AI ethics, and explicate common ethical concepts and their relevance to AI.
Prerequisites: none listed
Offered: Fall 2026, Fall 2025
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