Fall
MW 11-12:30pm (9/9/26-10/21/26)
Instructor: Dr. David Niño, GradEL Senior Lecturer (dnino@mit.edu)
Units: 3 (G 2-0-1)
Prerequisites: None
“It’s hard to rank the A.I. players, who you trust the most, or least. My trust is in the collective solutions we create together. The founding fathers [of the U.S.] did not put trust in a single person, and so my hope is not in a single A.I., it is in people.” Fei-Fei Li, during an interview at the Bloomberg Technology Summit in San Francisco in May 2024
This graduate class will equip students with the leadership skills and perspectives needed when evaluating, designing, and implementing AI systems in today’s rapidly evolving environments.
The course centers on trust as a critical enabler of AI’s benefits—and a safeguard against its risks. Students will examine how issues such as transparency, fairness, reliability, and competitive dynamics shape the real-world impact of AI systems.
Learning is structured around three complementary components:
(1) hands-on, experiential assignments that engage students in leading-edge issues and real-world scenarios;
(2) interdisciplinary research spanning technical, social, and organizational dimensions of AI systems;
(3) guest lectures from leading practitioners and scholars at the forefront of engineering and AI development.
By the end of the course, students will be better prepared to lead AI initiatives that are not only technically robust, but also trustworthy, responsible, and aligned with the needs of the people and communities they affect.
Other details:
This half-term, 3-unit (3-0-3) class will meet on Mondays and Wednesday, 11:00-12:30pm, from September 9-October 21, 2026. The lead instructor is David Niño, who will gladly address questions via email, dnino@mit.edu.