Capturing Humans' Mental Models of AI: An Item Response Theory Approach
Markelle Kelly Aakriti Kumar Padhraic Smyth Mark Steyvers
Improving our understanding of how humans perceive AI teammates is an
important foundation for our general understanding of human-AI teams. Extending
relevant work from cognitive science, we propose a framework based on item
response theory for modeling these perceptions. We apply this framework to
real-world experiments, in which each participant works alongside another
person or an AI agent in a question-answering setting, repeatedly assessing
their teammate's performance. Using this experimental data, we demonstrate the
use of our framework for testing research questions about people's perceptions
of both AI agents and other people. We contrast mental models of AI teammates
with those of human teammates as we characterize the dimensionality of these
mental models, their development over time, and the influence of the
participants' own self-perception. Our results indicate that people expect AI
agents' performance to be significantly better on average than the performance
of other humans, with less variation across different types of problems. We
conclude with a discussion of the implications of these findings for human-AI
interaction.