立场：借鉴认知神经科学经验的人工智能内部可解释性框架
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
AI总结：
本文借鉴认知神经科学的经验教训，提出面向AI内部可解释性的总体概念框架和具体方法论策略，以回应批评并推动AI系统的机制性解释研究。
摘要：
内部可解释性是一个前景广阔的新兴领域，其任务是揭示人工智能系统的内在机制，但如何发展这些机制性理论仍存在大量争论。此外，近期的批评提出了质疑其对于推进人工智能更广泛目标是否有用的问题。然而，人们忽视了一点：这些问题与另一个领域——认知神经科学——中一直在努力解决的问题十分相似。在此，我们建立相关联系，并强调可以在两个领域之间有效迁移的经验教训。基于这些经验，我们提出一个总体概念框架，并为人工智能内部可解释性研究中的机制性解释构建给出具体的方法论策略。借助这一概念框架，内部可解释性能够抵御批评，并将自身置于一条解释人工智能系统的富有成效的路径上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
