  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.
