2 research outputs found
Variable-Based Calibration for Machine Learning Classifiers
The deployment of machine learning classifiers in high-stakes domains
requires well-calibrated confidence scores for model predictions. In this paper
we introduce the notion of variable-based calibration to characterize
calibration properties of a model with respect to a variable of interest,
generalizing traditional score-based calibration and metrics such as expected
calibration error (ECE). In particular, we find that models with near-perfect
ECE can exhibit significant variable-based calibration error as a function of
features of the data. We demonstrate this phenomenon both theoretically and in
practice on multiple well-known datasets, and show that it can persist after
the application of existing recalibration methods. To mitigate this issue, we
propose strategies for detection, visualization, and quantification of
variable-based calibration error. We then examine the limitations of current
score-based recalibration methods and explore potential modifications. Finally,
we discuss the implications of these findings, emphasizing that an
understanding of calibration beyond simple aggregate measures is crucial for
endeavors such as fairness and model interpretability
Capturing Humans' Mental Models of AI: An Item Response Theory Approach
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.Comment: FAccT 202