Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning
Abstract
Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker’s policy is challenging—with no access to underlying states, no knowledge of environment dynamics, and no allowance for live experimentation. We desire learning a data-driven representation of decision- making behavior that (1) inheres transparency by design, (2) accommodates partial observability, and (3) operates completely offline. To satisfy these key criteria, we propose a novel model-based Bayesian method for interpretable policy learning (“Interpole”) that jointly estimates an agent’s (possibly biased) belief-update process together with their (possibly suboptimal) belief-action mapping. Through experiments on both simulated and real-world data for the problem of Alzheimer’s disease diagnosis, we illustrate the potential of our approach as an investigative device for auditing, quantifying, and understanding human decision-making behavior.
Cite
Text
Hüyük et al. "Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning." International Conference on Learning Representations, 2021.Markdown
[Hüyük et al. "Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning." International Conference on Learning Representations, 2021.](https://mlanthology.org/iclr/2021/huyuk2021iclr-explaining/)BibTeX
@inproceedings{huyuk2021iclr-explaining,
title = {{Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning}},
author = {Hüyük, Alihan and Jarrett, Daniel and Tekin, Cem and van der Schaar, Mihaela},
booktitle = {International Conference on Learning Representations},
year = {2021},
url = {https://mlanthology.org/iclr/2021/huyuk2021iclr-explaining/}
}