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.

提出了一种协作系统，其中人类最终做出决策，同时给予模型以最佳机会进行解释并与其辩论，从而解决了机器学习模型在复杂决策中出现难以检测故障模式且能够无监督采取行动的挑战。通过使用协作方法，我们可以确保安全性，提高性能，并解决透明度和问责制方面的担忧，从而实现有效的决策。

通过学习有说服力的解释优化人工智能与人类的合作

Optimising Human-AI Collaboration by Learning Convincing Explanations

We study continual learning for natural language instruction generation, by
observing human users' instruction execution. We focus on a collaborative
scenario, where the system both acts and delegates tasks to human users using
natural language. We compare user execution of generated instructions to the
original system intent as an indication to the system's success communicating
its intent. We show how to use this signal to improve the system's ability to
generate instructions via contextual bandit learning. In interaction with real
users, our system demonstrates dramatic improvements in its ability to generate
language over time.

研究自然语言指令生成的连续学习，在观察人类用户执行指令的基础上，并比较用户执行结果和系统原始意图的差异，以此作为信号改善系统的指令生成能力。通过与真实用户的交互，系统在语言生成能力方面表现出巨大的进步。