一个基于因果概念的模型解释的框架
Anna Rodum Bjøru1, Jacob Lysnæs-Larsen1, Oskar Jørgensen1
1Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Frontiers in artificial intelligence
|February 27, 2026
概括
本研究引入了可解释AI (XAI) 的因果框架,为复杂模型创建可理解和可信的解释. 它使用概念干预来产生本地和全球的见解,确保清晰度和准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 非可解释的人工智能模型对理解它们的决策过程提出了挑战.
- 后期可解释的人工智能 (XAI) 方法旨在提供对黑子模型的见解.
- 现有的XAI方法可能缺乏足够的忠实性或可理解性.
研究的目的:
- 提出基于因果关系概念的临时后期XAI的概念框架.
- 确保解释既可理解又忠于底层AI模型.
- 通过概念干预生成本地和全球解释.
主要方法:
- 开发了一个基于因果概念的XAI的概念框架.
- 计算了概念干预产生解释的足够性概率.
- 使用了在CelebA数据集上训练的概念验证模型进行演示.
主要成果:
- 以概念干预为基础生成示例本地和全球解释.
- 通过清晰,因果解释的概念词汇来证明可理解性.
- 通过概述框架假设和上下文调整的重要性来解决忠实性.
结论:
- 拟议的框架提供了一种在XAI.中创建可理解和可信的解释的方法.
- 因果概念干预提供了一个强大的机制,用于产生本地和全球的见解.
- 将解释上下文与生成上下文对齐对于可靠的XAI至关重要.
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