IN-PERSON Making AI Work Legible

Instructor: Alex Gates - Data Science

This session will focus on how to document and justify the use of AI in research to make workflows interpretable, reproducible, and credible. We’ll cover where AI enters the research pipeline, common failure modes, and practical strategies for disclosure, validation, and communicating methods in papers and proposals.

Who should take this: Faculty across disciplines, especially those incorporating AI into writing, literature review, qualitative analysis, or data workflows, and who want to ensure their work remains rigorous and reviewable prototypes, research demos, or data workflows.