2026 Spring DLC: Understanding AI Bias: How It Arises and How to Respond

  • Duration: 52 minutes
  • Date Recorded: March 04, 2026

Speakers

  • Facilitator: Gwen Sinclair, Chair of the Government Documents & Maps Department, University of Hawai‘i at Mānoa Library
  • Kim Nayyer, Edward Cornell Law Librarian, Associate Dean for Library Services, and Professor of the Practice, Cornell Law School and Cornell University Library

Description

DLC: Unique Challenges, Underserved Populations, and Federal Depository Libraries 

This session explores the phenomenon of AI bias—why bias is seen as inherent in machine learning processes and generative AI models. We will examine the impact of training data, design choices, and real-world use on observable and implicit bias in AI outputs, along with the consequent implications for public trust. Participants will learn about techniques developers can use to mitigate bias and strategies information professionals can use to critically assess AI outputs for bias and mitigation efforts. By the end of the session, participants will have a deeper understanding of AI bias and will be able to apply this knowledge in their own work and toward informative interactions with patrons.

Associated Files for Download