94-885 Responsible AI - Principles, Policies and Practices
6 units, Carnegie Mellon University
As artificial intelligence technologies advance, so do the complexities and risks that organizations and policymakers must monitor, in order to adhere with ethical principles. This course will cover several desired properties for Responsible AI systems to embody, going beyond accuracy and reliability, to include interpretability, privacy, security, safety, and sustainability. Along with this, we will discuss strategies, pitfalls, and limitations for corporations aiming to identify and mitigate risks, such as organizational commitments to transparency via artifacts like model cards. We will also cover governance frameworks aiming to enforce responsibility and prevent societal harms, including the NIST AI Risk Management Framework (RMF), the EU AI Act, and several U.S. laws. This course will be in a seminar/discussion format, and the use of case studies and discussion of real-world incidents will reinforce the challenges in translating the nuances of ethical commitments to measurable organizational practice or enforceable legal policy. Throughout the course, several types of AI systems will be discussed (spanning traditional issues of fairness in predictive machine learning models, to recent high profile security issues or jailbreaks in generative AI), as well as systems used in several domains of application. There are no formal prerequisites for this course, but rigorous critical analysis, thoughtful communication, and reflection on integrity will be expected, as this is a graduate level course at CMU.
Prerequisites: none listed
Offered: Fall 2026, Summer 2026, Fall 2025
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