  Learning controllers with offline data in decision-making systems is an
essential area of research due to its potential to reduce the risk of
applications in real-world systems. However, in responsibility-sensitive
settings such as healthcare, decision accountability is of paramount
importance, yet has not been adequately addressed by the literature. This paper
introduces the Accountable Offline Controller (AOC) that employs the offline
dataset as the Decision Corpus and performs accountable control based on a
tailored selection of examples, referred to as the Corpus Subset. AOC operates
effectively in low-data scenarios, can be extended to the strictly offline
imitation setting, and displays qualities of both conservation and
adaptability. We assess AOC's performance in both simulated and real-world
healthcare scenarios, emphasizing its capability to manage offline control
tasks with high levels of performance while maintaining accountability.
