标题： 位置：一种受认知神经科学启示的AI内部可解释性框架 显示英文标题

标题： Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience

摘要： 内部可解释性是一个有前景的新兴领域，其任务是揭示人工智能系统的内部机制，尽管如何开发这些机制理论仍存在很大争议。 此外，最近的批评提出了质疑其对推动人工智能更广泛目标有用性的议题。 然而，人们忽略了这些议题与另一个领域中所面临的议题相似：认知神经科学。 在这里，我们探讨了相关的联系，并强调了可以在这两个领域之间有效转移的经验教训。 基于这些，我们提出了一种通用的概念框架，并给出了在人工智能内部可解释性研究中构建机制解释的具体方法策略。 通过这个概念框架，内部可解释性可以应对批评，并在其解释人工智能系统的发展道路上处于有成效的位置。 显示英文摘要

摘要： Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debated. Moreover, recent critiques raise issues that question its usefulness to advance the broader goals of AI. However, it has been overlooked that these issues resemble those that have been grappled with in another field: Cognitive Neuroscience. Here we draw the relevant connections and highlight lessons that can be transferred productively between fields. Based on these, we propose a general conceptual framework and give concrete methodological strategies for building mechanistic explanations in AI inner interpretability research. With this conceptual framework, Inner Interpretability can fend off critiques and position itself on a productive path to explain AI systems.