Allie Tatarian, Tufts University

Alternative Title

Beyond the Hype: Addressing Bias in Artificial Intelligence (AI) for Information Literacy Instruction

Contributor

University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2024 : Orlando, Fla.)

Location

Mangrove

Start Date

22-7-2024 1:00 PM

End Date

22-7-2024 2:00 PM

Publisher

University of Central Florida Libraries

Keywords:

Bias in AI; Information literacy; Critical analysis; Educational strategies; AI tools

Subjects

Artificial intelligence--Social aspects; Artificial intelligence--Information services; Artificial intelligence--Study and teaching; Information literacy--Study and teaching; Artificial intelligence--Moral and ethical aspects

Description

While generative AI tools offer exciting possibilities, they also raise critical concerns about bias. This session delves into the inherent biases within AI systems and their impact on information literacy instruction. We will examine how data selection, training algorithms, and social factors contribute to bias in AI outputs, and then work together to develop practical strategies to empower students to critically analyze AI-generated content. (This session’s title and abstract written with help from Google Gemini.)

Language

eng

Type

Presentation

Format

application/pdf

Rights Statement

All Rights Reserved

Audience

Students, Faculty, Librarians, Educators

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Jul 22nd, 1:00 PM Jul 22nd, 2:00 PM

Beyond the Hype: Addressing Bias in AI for Information Literacy Instruction

Mangrove

While generative AI tools offer exciting possibilities, they also raise critical concerns about bias. This session delves into the inherent biases within AI systems and their impact on information literacy instruction. We will examine how data selection, training algorithms, and social factors contribute to bias in AI outputs, and then work together to develop practical strategies to empower students to critically analyze AI-generated content. (This session’s title and abstract written with help from Google Gemini.)