53 citations · 73 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCI
Jordan Taylor, Ellen Simpson, Anh-Ton Tran +3
LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review o…
Seeing Seeds Beyond Weeds: Green Teaming Generative AI for Beneficial Uses
Logan Stapleton, Jordan Taylor, Sarah Fox +2
Large generative AI models (GMs) like GPT and DALL-E are trained to generate content for general, wide-ranging purposes. GM content filters are generalized to filter out content wh…