Series Overview: GradFUTURES is pleased to offer Responsible AI learning cohort in Fall 2024 in partnership with Center for Information Technology Policy (CITP) and Princeton University Press (PUP). Led by graduate students, this learning cohort leverages expertise of Princeton’s faculty, graduate students, staff and alumni, and external partners. The cohort will discuss and examine realities of Responsible AI principles: fairness, inclusiveness, transparency, reliability and safety, privacy and security, and accountability in diverse fields through guest speakers, case studies and immersive capstone. Upon successful completion of learning cohort, graduate students will receive a co-curricular certificate of completion and a micro-credential badge.

Responsible AI dimensions. We will follow the definition and dimensions of Responsible AI as outlined by Microsoft. "Responsible AI is an approach to developing, assessing, and deploying AI systems in a safe, trustworthy, and ethical way. It's a framework for building AI systems according to six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability."

Session Description: In this session, Prof. Lydia Liu will discuss "The Reach of Fairness", an interdisciplinary collaboration with philosopher Joshua Cohen.
“The Reach of Fairness” seeks to broaden the scope of normative discourse about machine learning and algorithmic decision making. Beginning from an understanding of fair cooperation among free and equal persons as a fundamental political value, we argue that concerns about fairness and machine learning need to be expanded in three ways: discrimination beyond group subordination, equality of opportunity beyond organizational decisions, and justice beyond the equality of opportunity. The ideas have ramifications for various commerical applications and ongoing regulatory debates.

Guest Speaker: Lydia Liu, Assistant Professor, Computer Science and Associated Faculty, CITP.