56 citations · 61 across the 7 of their papers we have counts for
7 papers
PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim +5
Recent developments in AI safety research have called for red-teaming methods that effectively surface potential risks posed by generative AI models, with growing emphasis on how r…
PersonaTeaming: Exploring How Introducing Personas Can Improve Automated AI Red-Teaming
Wesley Hanwen Deng, Sunnie S. Y. Kim, Akshita Jha +4
Recent developments in AI governance and safety research have called for red-teaming methods that can effectively surface potential risks posed by AI models. Many of these calls ha…
Understanding Gender Bias in AI-Generated Product Descriptions
Markelle Kelly, Mohammad Tahaei, Padhraic Smyth +1
While gender bias in large language models (LLMs) has been extensively studied in many domains, uses of LLMs in e-commerce remain largely unexamined and may reveal novel forms of a…
The Problems with Proxies: Making Data Work Visible through Requester Practices
Annabel Rothschild, Ding Wang, Niveditha Jayakumar Vilvanathan +3
Fairness in AI and ML systems is increasingly linked to the proper treatment and recognition of data workers involved in training dataset development. Yet, those who collect and an…
Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance
Mohammmad Tahaei, Daricia Wilkinson, Alisa Frik +3
Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values,…
Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence
Marios Constantinides, Mohammad Tahaei, Daniele Quercia +13
With the upcoming AI regulations (e.g., EU AI Act) and rapid advancements in generative AI, new challenges emerge in the area of Human-Centered Responsible Artificial Intelligence…
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
Zijie J. Wang, Chinmay Kulkarni, Lauren Wilcox +2
Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms th…