4 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…
Disclosure or Marketing? Analyzing the Efficacy of Vendor Self-reports for Vetting Public-sector AI
Blaine Kuehnert, Nari Johnson, Ravit Dotan +1
Documentation-based disclosure has become a central governance strategy for responsible AI, particularly in public-sector procurement. Tools such as model cards, datasheets, and AI…
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…