在深度神经网络中,Explananda和Explanantia是神经网络功能的神经网络模型
1Desautels Centre for Integrative Thinking, Rotman School of Management, University of Toronto, Toronto, ON, Canada mihnea.moldoveanu@rotman.utoronto.cahttps://www.rotman.utoronto.ca/FacultyAndResearch/Faculty/FacultyBios/Moldoveanu.
The Behavioral and brain sciences
|December 6, 2023
概括
修改深度神经网络的解释需要重新思考它们的核心组件和训练方法. 这影响了我们如何理解人工智能和模型可解释性.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 深度神经网络 (DNN) 越来越多地用于建模智能行为.
- 在解释DNN的"黑子"性质方面存在挑战.
- 当前的解释可能缺乏足够的深度或范围.
研究的目的:
- 探索解释DNN所需的修订.
- 分析不同的解释概念如何影响DNN建模.
- 调查计算模型的解释功能.
主要方法:
- 在DNN的背景下对"解释"的概念分析.
- 检查DNN构建块 (explananda) 的潜在修订情况.
- 对实验环境和培训协议的修改评估 (explanantia).
主要成果:
- 基于不同的解释框架,出现了两个不同的修订路径.
- 路径需要改变模型的基本单元或其培训/环境.
- 这些路径意味着对AI解释的性质和目的有不同的看法.
结论:
- 为了获得对DNN的更深入,更广泛的解释,需要进行根本性的调整.
- 解释框架的选择极大地影响了研究和模型开发的方向.
- 了解模型的解释功能是提高AI解释性的关键.
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