ML解释性:简单并不容易
1University of Bern, Institute of Philosophy, Länggassstrasse 49a, 3012 Bern, Switzerland.
Studies in history and philosophy of science
|January 4, 2024
概括
本研究阐明了机器学习 (ML) 模型的可解释性,研究了为什么像线性模型这样的简单模型是可解释的,以及复杂模型如何保持一些透明度. 了解可解释性是可信的人工智能的关键.
科学领域:
- 人工智能的人工智能
- 科学哲学的哲学科学哲学
- 计算机科学 计算机科学
背景情况:
- 机器学习 (ML) 模型可解释性的重要性得到了广泛认可.
- 目前的研究往往侧重于复杂的"黑子"模型,如神经网络和可解释AI (XAI) 的方法.
- 在不同模型类型中对可解释性的清晰定义和理解仍然难以捉摸.
研究的目的:
- 为了澄清ML模型可解释性的基本性质.
- 探索可解释性的范围,专注于高度可解释的模型.
- 分析不同ML模型中如何实现不同程度的可解释性.
主要方法:
- 检查固有的可解释模型 (线性模型,决策树).
- 分析具有部分可解释性的模型 (MARS,GAM).
- 对可解释性的哲学和概念分析.
主要成果:
- 可解读性不是一个单一的概念;它因模型类型而异.
- 确定了对更简单模型可解释性的因素.
- 研究了在更复杂的模型中保留可解释性的方法.
结论:
- 虽然实现可解释性的方法有所不同,但对于特定的ML模型,其性质可以明确定义.
- 这项工作为理解和评估ML可解释性提供了更清晰的框架.
- 进一步的研究可以建立在对更透明和可信赖的AI系统的澄清理解的基础上.
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