1 citations · 2 across the 7 of their papers we have counts for
7 papers
AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters
Jacob Lagogiannis, William Agnew, Rosa I. Arriaga +1
Anti-facial recognition (AFR) image filters alter images in ways that are subtle to people but blinding to computer vision. Yet, despite widespread interest in these technologies t…
Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization
Rachel Hong, Yael Eiger, Jevan Hutson +2
Pluralistic alignment has emerged as a promising approach for ensuring that large language models (LLMs) faithfully represent the diversity, nuance, and conflict inherent in human…
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…
How Do Data Owners Say No? A Case Study of Data Consent Mechanisms in Web-Scraped Vision-Language AI Training Datasets
Chung Peng Lee, Rachel Hong, Harry H. Jiang +3
The internet has become the main source of data to train modern text-to-image or vision-language models, yet it is increasingly unclear whether web-scale data collection practices…
A Common Pool of Privacy Problems: Legal and Technical Lessons from a Large-Scale Web-Scraped Machine Learning Dataset
Rachel Hong, Jevan Hutson, William Agnew +3
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off…
Data Defenses Against Large Language Models
William Agnew, Harry H. Jiang, Cella Sum +2
Large language models excel at performing inference over text to extract information, summarize information, or generate additional text. These inference capabilities are implicate…
Sound Check: Auditing Audio Datasets
William Agnew, Julia Barnett, Annie Chu +6
Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some…