AI Governance: Identifying & Mitigating Risks in Design & Dev of AI Solutions

Course Number: 17416, 17716, 19416, 19716

With AI and ML finding their way into an increasingly broad range of products and services, it is important to identify and mitigate the risks associated with the adoption of these technologies. This course reviews the different types of risks associated with AI and discusses methodologies and techniques available to identify and mitigate these risks. The course introduces students to ethical frameworks available to identify and analyze risks. It also examines best practices emerging from both government and industry efforts in this area. This includes looking at new regulations such as the EU AI Act as well as emerging frameworks such as the one developed by NIST. The course also examines frameworks developed by leading companies and how these frameworks combine both technical and non-technical approaches. It further discusses changes that need to be enacted by organizations to adopt more systematic approaches to AI governance. This course combines a mix of technical, policy, and management discussions.

Academic Year: 2025-2026
Semester(s): Spring
Required/Elective: Elective
Units: 6,9
Prerequisite(s): The course does not assume a deep technical understanding of AI/ML techniques. Instead, gentle introductions to relevant techniques and concepts will be provided over the course of the semester, as required to follow discussions of different topics. Material and discussions are designed to enable people with diverse technical backgrounds to benefit from topics discussed in the lectures. Students will however be expected to have a basic understanding of probability and statistics. Because AI governance is emerging as an activity that has to involve a broad set of roles within the enterprise (e.g., product managers, AI/ML engineers, legal & compliance, UX/UI designers, security engineers, privacy engineers, safety engineers, software architects, software engineers), the course is designed to take a broad, multi-faceted view of relevant topics and aims to appeal to a broad cross-section of students.
Location(s): Pittsburgh

Format

Lecture

Learning Objectives

This course is intended for a broad cross-section of students, both advanced
undergrads and graduate students, planning to work on the design, development and
deployment of AI-based solutions. The course is designed to introduce students to key
concepts, challenges, principles, methodologies, techniques, best practices, legal
requirements and trends associated with the responsible design, development and
deployment of AI technologies.