6 papers
What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them
Megan Li, Wendy Bickersteth, Ningjing Tang +7
Despite growing concerns about the risks of Generative AI (GenAI), there is limited understanding of public perceptions of these risks and their associated failure modes -- defined…
Situated, Dynamic, and Subjective: Envisioning the Design of Theory-of-Mind-Enabled Everyday AI with Industry Practitioners
Qiaosi Wang, Jini Kim, Avanita Sharma +4
Theory of Mind (ToM) -- the ability to infer what others are thinking (e.g., intentions) from observable cues -- is traditionally considered fundamental to human social interaction…
A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents
Megan Li, Wendy Bickersteth, Ningjing Tang +4
Due to its general-purpose nature, Generative AI is applied in an ever-growing set of domains and tasks, leading to an expanding set of risks of harm impacting people, communities,…
Navigating Uncertainties: How GenAI Developers Document Their Models on Open-Source Platforms
Ningjing Tang, Megan Li, Amy Winecoff +3
Model documentation plays a crucial role in promoting transparency and responsible development of AI systems. With the rise of Generative AI (GenAI), open-source platforms have inc…
Global AI Governance: Where the Challenge is the Solution- An Interdisciplinary, Multilateral, and Vertically Coordinated Approach
Huixin Zhong, Thao Do, Ynagliu Jie +2
Current global AI governance frameworks struggle with fragmented disciplinary collaboration, ineffective multilateral coordination, and disconnects between policy design and grassr…
A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems
Alessandro Gambetti, Qiwei Han, Hong Shen +1
Explainable Artificial Intelligence (XAI) is essential for the transparency and clinical adoption of Clinical Decision Support Systems (CDSS). However, the real-world effectiveness…