  Machine learning models are being increasingly deployed to take, or assist in
taking, complicated and high-impact decisions, from quasi-autonomous vehicles
to clinical decision support systems. This poses challenges, particularly when
models have hard-to-detect failure modes and are able to take actions without
oversight. In order to handle this challenge, we propose a method for a
collaborative system that remains safe by having a human ultimately making
decisions, while giving the model the best opportunity to convince and debate
them with interpretable explanations. However, the most helpful explanation
varies among individuals and may be inconsistent across stated preferences. To
this end we develop an algorithm, Ardent, to efficiently learn a ranking
through interaction and best assist humans complete a task. By utilising a
collaborative approach, we can ensure safety and improve performance while
addressing transparency and accountability concerns. Ardent enables efficient
and effective decision-making by adapting to individual preferences for
explanations, which we validate through extensive simulations alongside a user
study involving a challenging image classification task, demonstrating
consistent improvement over competing systems.
