493 citations · 514 across the 5 of their papers we have counts for
6 papers
Artificial intelligences and human scientists exhibit complementary strengths in theory building
Ke Li, Spyros I. Zoumpoulis, Phanish Puranam +80
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social sci…
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Preregistration for Experiments with AI Agents
Michelle Vaccaro
The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments. Original…
Evaluating Human-AI Safety: A Framework for Measuring Harmful Capability Uplift
Michelle Vaccaro, Jaeyoon Song, Abdullah Almaatouq +1
Current frontier AI safety evaluations emphasize static benchmarks, third-party annotations, and red-teaming. In this position paper, we argue that AI safety research should focus…
When combinations of humans and AI are useful: A systematic review and meta-analysis
Michelle Vaccaro, Abdullah Almaatouq, Thomas Malone
Inspired by the increasing use of AI to augment humans, researchers have studied human-AI systems involving different tasks, systems, and populations. Despite such a large body of…
A Test for Evaluating Performance in Human-Computer Systems
Andres Campero, Michelle Vaccaro, Jaeyoon Song +3
The Turing test for comparing computer performance to that of humans is well known, but, surprisingly, there is no widely used test for comparing how much better human-computer sys…