Unmasking Deepfakes: Detecting AI-Generated Deception and Addressing Gender Disparities
Alternative Title
Unmasking Deepfakes: Detecting Artificial Intelligence (AI)-Generated Deception and Addressing Gender Disparities
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
Sawgrass
Start Date
22-7-2024 2:15 PM
End Date
22-7-2024 2:45 PM
Publisher
University of Central Florida Libraries
Keywords:
Deepfake detection; Gender disparities; AI ethics; Legislative efforts; Societal implications
Subjects
Deepfakes--Social aspects; Artificial intelligence--Social aspects; Deception--Social aspects; Sexism--Prevention; Artificial intelligence--Law and legislation
Description
This presentation will provide an overview of deepfakes, examining their societal implications, with a focus on gender disparities. Our interactive presentation will cover topics from the mechanics of deepfake creation to visual and audio detection cues, rounding out with an overview of legislative efforts and case studies that emphasize the disproportionate impact on women and minors.
Language
eng
Type
Presentation
Rights Statement
All Rights Reserved
Audience
Students, Faculty, General Audience, Administrators
Recommended Citation
Lowe, Ashley; Delacruz, Daynah; and Paglia, Diana, "Unmasking Deepfakes: Detecting AI-Generated Deception and Addressing Gender Disparities" (2024). Teaching and Learning with AI Conference Presentations. 42.
https://stars.library.ucf.edu/teachwithai/2024/monday/42
Unmasking Deepfakes: Detecting AI-Generated Deception and Addressing Gender Disparities
Sawgrass
This presentation will provide an overview of deepfakes, examining their societal implications, with a focus on gender disparities. Our interactive presentation will cover topics from the mechanics of deepfake creation to visual and audio detection cues, rounding out with an overview of legislative efforts and case studies that emphasize the disproportionate impact on women and minors.