1 citations · 1 across the 9 of their papers we have counts for
9 papers
"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 Authenticity: Comparing LLM Cultural Representations to Native Human Expectations
Erin MacMurray van Liemt, Aida Davani, Sinchana Kumbale +2
Cultural representation in Large Language Model (LLM) outputs has primarily been evaluated through the proxies of cultural diversity and factual accuracy. However, a crucial gap re…
A Unified Framework to Quantify Cultural Intelligence of AI
Sunipa Dev, Vinodkumar Prabhakaran, Rutledge Chin Feman +16
As generative AI technologies are increasingly being launched across the globe, assessing their competence to operate in different cultural contexts is exigently becoming a priorit…
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…
Amplify Initiative: Building A Localized Data Platform for Globalized AI
Qazi Mamunur Rashid, Erin van Liemt, Tiffany Shih +19
Current AI models often fail to account for local context and language, given the predominance of English and Western internet content in their training data. This hinders the glob…
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…
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann +4
We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true ans…
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…