1 citations · 1 across the 9 of their papers we have counts for
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"Where is this coming from?" Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking
Vaibhav Balloli, Julia Erickson, Xinyi Li +3
AI-powered tools increasingly promise to fill information gaps in health, especially in domains like maternal and reproductive health that demand timely, accurate, and actionable i…
Cultural Compass: A Framework for Organizing Societal Norms to Detect Violations in Human-AI Conversations
Myra Cheng, Vinodkumar Prabhakaran, Alice Oh +5
Generative AI models ought to be useful and safe across cross-cultural contexts. One critical step toward this goal is understanding how AI models adhere to sociocultural norms. Wh…
Scaling Cultural Resources for Improving Generative Models
Hayk Stepanyan, Aishwarya Verma, Andrew Zaldivar +5
Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been commonly conducted to improve ov…
Ontology of Belief Diversity: A Community-Based Epistemological Approach
Tyler Fischella, Erin van Liemt, Qiuyi +1
AI applications across classification, fairness, and human interaction often implicitly require ontologies of social concepts. Constructing these well, especially when there are ma…
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
Jessica Quaye, Alicia Parrish, Oana Inel +12
With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generatio…