可解释性在于观察者的思想:人类可解释的表现学习的因果框架
Emanuele Marconato1,2, Andrea Passerini1, Stefano Teso1,3
1Dipartimento di Ingegneria e Scienza dell'Informazione, University of Trento, 38123 Trento, Italy.
Entropy (Basel, Switzerland)
|December 23, 2023
概括
这项研究介绍了人类可解释的表示学习 (hrl) 的数学框架,以创建人类可以理解的AI解释. 它模拟人类的理解,以使人工智能概念与人类的词汇量保持一致,提高人工智能的解释性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 可解释性AI (XAI) 研究正在转向基于概念的解释.
- 目前获得可解释概念的方法缺乏标准化,忽视了人类的理解.
- 一个关键的挑战是,在表达式学习中模拟人类元素.
研究的目的:
- 提出一个数学框架来获得可解释的表示.
- 弥合人工智能中人类和算法解释能力之间的差距.
- 建立一个基础,以未来的研究在人类可解读的代表学习.
主要方法:
- 开发了人类可解释的表示学习 (hrl) 的正式化.
- 综合因果表示学习原则.
- 模拟了一个人类利益相关者作为外部观察员来定义对齐.
主要成果:
- 从机器表示和人类概念词汇库之间对齐的原则概念得出.
- 通过名称转移游戏,通过链接对齐和可解释性.
- 澄清了对齐,解,概念泄漏和内容风格分离之间的关系.
结论:
- 拟议的框架为人类可解释的表示学习提供了一种原则性方法.
- 调整是创建人类可以理解的AI表示的一个关键因素.
- 这项工作为推进人工智能解释性研究提供了一个跳板.
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