50 citations · 62 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
Beyond the Single Turn: Reframing Refusals as Dynamic Experiences Embedded in the Context of Mental Health Support Interactions with LLMs
Ningjing Tang, Alice Qian, Qiaosi Wang +6
Content Warning: This paper contains participant quotes and discussions related to mental health challenges, emotional distress, and suicidal ideation. Large language models (LLMs)…
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…
Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use
Anna Kawakami, Amanda Coston, Hoda Heidari +2
As public sector agencies rapidly introduce new AI tools in high-stakes domains like social services, it becomes critical to understand how decisions to adopt these tools are made…
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A…
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
Charvi Rastogi, Liu Leqi, Kenneth Holstein +1
Hybrid human-ML systems increasingly make consequential decisions in a wide range of domains. These systems are often introduced with the expectation that the combined human-ML sys…