SHAP在警报处理中的人性化评估
A Human-Grounded Evaluation of SHAP for Alert Processing
摘要：
在过去的几年中，许多新的解释方法已经被提出来实现机器学习预测的可解释性。然而，这些方法在实际应用中的效用还没有得到广泛的研究。在本文中，我们提出了一个以人为本的SHAP，解释方法，已在XAI和相关社区广受好评的评价结果。特别是，我们研究这种局部模型不可知的解释方法是否可以用于真正的人类领域专家来评估积极预测的正确性，即由分类器生成的警报。我们对三组不同的参与者（共159人）进行了实验，他们都具有可解释的机器学习的基本知识。我们进行了定性分析记录的反射实验参与者执行警报处理和不SHAP信息。结果表明，SHAP解释确实影响决策过程，尽管该模型的置信度得分仍然是主要的证据来源。我们统计测试是否有一个显着的差异，任务之间的任务效用指标的解释是可用的，任务中，它没有提供。与常见的直觉相反，我们没有发现当SHAP解释可用时与不可用时警报处理性能的显着差异。
英文摘要：
In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions. However, the utility of these methods in practical applications has not been researched extensively. In this paper we present the results of a human-grounded evaluation of SHAP, an explanation method that has been well-received in the XAI and related communities. In particular, we study whether this local model-agnostic explanation method can be useful for real human domain experts to assess the correctness of positive predictions, i.e. alerts generated by a classifier. We performed experimentation with three different groups of participants (159 in total), who had basic knowledge of explainable machine learning. We performed a qualitative analysis of recorded reflections of experiment participants performing alert processing with and without SHAP information. The results suggest that the SHAP explanations do impact the decision-making process, although the model's confidence score remains to be a leading source of evidence. We statistically test whether there is a significant difference in task utility metrics between tasks for which an explanation was available and tasks in which it was not provided. As opposed to common intuitions, we did not find a significant difference in alert processing performance when a SHAP explanation is available compared to when it is not.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
