3 papers
cs.HC2026
The Algorithmic Gaze of Image Quality Assessment: An Audit and Trace Ethnography of the LAION-Aesthetics Predictor
Jordan Taylor, William Agnew, Maarten Sap +2
Visual generative AI models are trained using a one-size-fits-all measure of aesthetic appeal. However, what is deemed "aesthetic" is inextricably linked to personal taste and cult…
cs.CY2026
AI Failure Loops in Devalued Work: The Confluence of Overconfidence in AI and Underconfidence in Worker Expertise
Anna Kawakami, Jordan Taylor, Sarah Fox +2
A growing body of literature has focused on understanding and addressing workplace AI design failures. However, past work has largely overlooked the role of the devaluation of work…
cs.HC2025
Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models
Jordan Taylor, Joel Mire, Franchesca Spektor +4
Queer people are often discussed as targets of bias, harm, or discrimination in research on generative AI. However, the specific ways that queer people engage with generative AI, a…