对于特征归属的不可能定理
Blair Bilodeau1, Natasha Jaques2, Pang Wei Koh2
1Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada.
概括
像集成梯度和SHAP这样的特征归属方法可能会在神经网络中失败. 定义特定的最终任务允许更简单的模型评估超过复杂的方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 可解释的人工智能 (XAI)
背景情况:
- 存在许多可解释性方法,但经验上的失败引起了人们对其实际应用的担忧.
- 实践者缺乏关于原则性使用和选择特征归因方法的明确指导.
研究的目的:
- 为适度丰富的模型类展示完整和线性特征归因方法的理论限制.
- 突出最终任务定义在评估可解释性方法中的重要性.
主要方法:
- 根据特定假设 (完整性,线性) 进行特征归因方法的理论分析.
- 跨常见终端任务的调查:局部行为特征,虚假特征识别和算法诉求.
- 与重复模型评估的基线方法进行比较.
主要成果:
- 完整和线性特征归属方法在推断模型行为方面不能比随机猜测更好地执行.
- 这些限制适用于广泛使用的方法,如集成梯度和Shapley添加式解释 (SHAP).
- 一个简单的重复模型评估策略可以超过复杂的方法,当最终任务是明确的.
结论:
- 当前的特征归因方法可能无法可靠地提高对复杂模型的理解.
- 对最终任务的精确定义对于有效的模型解释至关重要.
- 精确定义的最终任务可以实现更简单,更直接的评估策略,这些策略可能比复杂的特征归因技术更有效.
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