Optimising Human-AI Collaboration by Learning Convincing Explanations
This addresses safety and transparency issues in high-impact AI applications like clinical support, though it is incremental as it builds on existing collaborative methods.
The paper tackles the challenge of ensuring safety in human-AI collaboration by developing Ardent, an algorithm that learns personalized explanations to convince humans in decision-making tasks, validated through simulations and a user study with consistent improvements over competing systems.
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.