Responsible AI Learning Cohort: Session 2- Constructing datasets for machine learning

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, guest speaker Vikram Ramaswamy (*23, CS and instructor) will discuss how to construct fair datasets for machine learning.
Abstract: Large-scale datasets are essential for machine learning progress, however, datasets can be biased and encode stereotypes in various ways. In this session, we'll consider different challenges faced when constructing datasets, and brainstorm how to move forward as a community.

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