Setting proper evaluation objectives for explainable artificial intelligence
(XAI) is vital for making XAI algorithms follow human communication norms,
support human reasoning processes, and fulfill human needs for AI explanations.
In this article, we examine explanation plausibility, which is the most
pervasive human-grounded concept in XAI evaluation. Plausibility measures how
reasonable the machine explanation is compared to the human explanation.
Plausibility has been conventionally formulated as an important evaluation
objective for AI explainability tasks. We argue against this idea, and show how
optimizing and evaluating XAI for plausibility is sometimes harmful, and always
ineffective to achieve model understandability, transparency, and
trustworthiness. Specifically, evaluating XAI algorithms for plausibility
regularizes the machine explanation to express exactly the same content as
human explanation, which deviates from the fundamental motivation for humans to
explain: expressing similar or alternative reasoning trajectories while
conforming to understandable forms or language. Optimizing XAI for plausibility
regardless of the model decision correctness also jeopardizes model
trustworthiness, as doing so breaks an important assumption in human-human
explanation namely that plausible explanations typically imply correct
decisions, and violating this assumption eventually leads to either undertrust
or overtrust of AI models. Instead of being the end goal in XAI evaluation,
plausibility can serve as an intermediate computational proxy for the human
process of interpreting explanations to optimize the utility of XAI. We further
highlight the importance of explainability-specific evaluation objectives by
differentiating the AI explanation task from the object localization task.

本文研究机器解释符合人类交流规范、支持人类推理过程并满足人类对人工解释的需求的重要性，指出仅仅优化和评估机器解释的合理性（Explanation Plausibility）对于提高模型的可理解性、透明性与可信度是有害无益的，因此提出了一些可行的评估方法，并重点强调了可解释性特定的评估目标的重要性。

重新思考 AI 的可解释性与合理性

Rethinking AI Explainability and Plausibility

The last decade has seen huge progress in the development of advanced machine
learning models; however, those models are powerless unless human users can
interpret them. Here we show how the mind's construction of concepts and
meaning can be used to create more interpretable machine learning models. By
proposing a novel method of classifying concepts, in terms of 'form' and
'function', we elucidate the nature of meaning and offer proposals to improve
model understandability. As machine learning begins to permeate daily life,
interpretable models may serve as a bridge between domain-expert authors and
non-expert users.

本文探讨如何利用概念和意义的构建方式来创建更易理解的机器学习模型，通过提出分类概念的新方法，即 “形式” 和 “功能”，阐明了意义的本质，并提出了提高模型可理解性的建议。