Computer Science 690F - Trustworthy and Responsible AI

In the era of intelligent assistants, autonomous agents, and self-driving cars we expect AI systems to not cause harm and withstand adversarial attacks. In this course you will learn advanced methods of building AI models and systems that mitigate privacy, security, societal, and environmental risks. We will go deep into attack vectors and what type of guarantees current research can and cannot provide for modern generative models. The course will feature extensive hands-on experience with model training and regular discussion of key research papers. The course will operate under the assumption that students have previously taken NLP, general ML, and security classes before taking this course.

Open to graduate Computer Science students only. AS THE COURSE HAS INTERDISCIPLINARY PARTS, TOWARD STATISTICS AND BEHAVIORAL SCIENCES, GRADUATE STUDENTS FROM OTHER DEPARTMENTS MAY ENROLL, IF SEATS ARE AVAILABLE. FOLLOWING BACKGROUND IS ASSUMED: INTRODUCTORY MACHINE LEARNING OR DATA SCIENCE (COMPSCI 348 OR 383 OR 389 OR 589 OR EQUIVALENT), BASIC STATISTICS (COMPSCI 240 OR STAT 240 OR PSYCH 240 OR OIM 240 OR STAT 515 OR EQUIVALENT), GOOD PROGRAMMING SKILLS IN PYTHON (LIBRARIES: NUMPY, PANDAS, MATPLOTLIB, SKLEARN). STUDENTS OUTSIDE OF CS MUST REQUEST AN OVERRIDE AND DESCRIBE HOW THEY MEET PREREQUISITES UNDER ADDITIONAL INFORMATION. STUDENTS WHO ARE UNCERTAIN WHETHER THEIR BACKGROUND IS SUFFICIENT SHOULD EMAIL THE INSTRUCTOR ATTACHING THEIR ACADEMIC TRANSCRIPTS TO DETERMINE WHETHER THEY HAVE THE APPROPRIATE BACKGROUND TO BE SUCCESSFUL IN THE COURSE. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDE VIA THE ON-LINE FORM: https://www.cics.umass.edu/overrides.