不要被愚弄:说明方法中的标签泄露及其定量评估的重要性
Neil Jethani1, Adriel Saporta2, Rajesh Ranganath3
1Grossman School of Medicine, Courant Institute New York University.
概括
取决于类的特征赋值方法可能会通过泄露类信息来误导用户. 这项研究引入了分布意识的方法,以提供更可靠的模型解释,根据不同的临床数据进行评估.
科学领域:
- 人工智能的人工智能
- 机器学习的可解释性
- 医疗数据分析 医学数据分析
背景情况:
- 特征归因方法通过突出影响力输入特征来解释模型预测.
- 目前流行的方法如SHAP,LIME和Grad-CAM是类依赖的,生成特定于所选类的解释.
- 取决于类的方法有风险"泄露"关于目标类的信息,可能导致误解.
研究的目的:
- 在类依赖的特征归属方法中识别信息泄露的潜力.
- 引入和评估新的分布意识的特征归因方法.
- 为了比较分布意识与类依赖方法在高维临床数据集上的性能.
主要方法:
- 在取决于类的方法 (SHAP,LIME,Grad-CAM) 中证明了信息泄露.
- 引入了分布意识的方法 (SHAP-KL,FastSHAP-KL),重点是保护标签的分布.
- 进行了全面的评估,比较了七种类型依赖和三种分布意识的方法.
主要成果:
- 类依赖的方法被证明可能会膨胀所选类的可能性.
- 提出了分布意识的方法来缓解这种信息泄漏.
- 对图像,生物信号和文本临床数据集进行了评估.
结论:
- 取决于类的特征赋值方法存在由于信息泄漏导致误解的风险.
- 分布意识方法通过保留标签分布,为模型解释提供了更强大的方法.
- 这些发现对于在临床应用中可靠解释AI模型至关重要.
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