2 citations · 5 across the 6 of their papers we have counts for
6 papers
The Perils of Agency: How Developers Perceive, Prioritize, and Address Risks in Agentic AI Products
Hao-Ping Lee, Jessica He, David Piorkowski +3
Agentic AI systems act autonomously, use tools, adapt to context, and operate in complex real-world environments. However, these same characteristics can create or exacerbate produ…
Developing a Risk Identification Framework for Foundation Model Uses
David Piorkowski, Michael Hind, John Richards +1
As foundation models grow in both popularity and capability, researchers have uncovered a variety of ways that the models can pose a risk to the model's owner, user, or others. Des…
AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources
Frank Bagehorn, Kristina Brimijoin, Elizabeth M. Daly +17
The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack o…
BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks
Anna Sokol, Elizabeth Daly, Michael Hind +4
Large language models (LLMs) are powerful tools capable of handling diverse tasks. Comparing and selecting appropriate LLMs for specific tasks requires systematic evaluation method…
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
Erik Miehling, Manish Nagireddy, Prasanna Sattigeri +3
Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings. We claim that many of the observed shortcomings can be att…
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…