5 citations · 6 across the 5 of their papers we have counts for
5 papers
Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains
Emanuel Moss, Elizabeth Watkins, Christopher Persaud +2
Generative AI tools have become more prevalent in engineering workflows, particularly through chatbots and code assistants. As the perceived accuracy of these tools improves, quest…
Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines
Ramesh Manuvinakurike, Emanuel Moss, Elizabeth Anne Watkins +3
Agentic pipelines present novel challenges and opportunities for human-centered explainability. The HCXAI community is still grappling with how best to make the inner workings of L…
What's So Human about Human-AI Collaboration, Anyway? Generative AI and Human-Computer Interaction
Elizabeth Anne Watkins, Emanuel Moss, Giuseppe Raffa +1
While human-AI collaboration has been a longstanding goal and topic of study for computational research, the emergence of increasingly naturalistic generative AI language models ha…
ACE, Action and Control via Explanations: A Proposal for LLMs to Provide Human-Centered Explainability for Multimodal AI Assistants
Elizabeth Anne Watkins, Emanuel Moss, Ramesh Manuvinakurike +3
In this short paper we address issues related to building multimodal AI systems for human performance support in manufacturing domains. We make two contributions: we first identify…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…