研究不同解释方式如何影响用户对预测流程的决策信任与表现。
The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring
- 对比特征重要性、规则和反事实三种解释风格的效果。
- 高感知准确率下，用户决策正确率提升18%且信心更强。
- 适合关注AI可解释性对实际决策影响的研究者与从业者。
预测流程监控（PPM）常使用深度学习模型预测流程未来行为，如流程结果。尽管这些模型精度高，但缺乏可解释性削弱了用户信任与采纳意愿。可解释人工智能（XAI）通过提供预测依据来应对这一挑战。然而，当前对PPM中XAI的评估主要关注功能指标（如保真度），忽略了用户中心因素，如对任务表现和决策的影响。本研究探究了解释风格（特征重要性、规则基础、反事实）和感知AI准确率（低或高）对PPM中决策制定的影响。我们开展了一项决策实验，让用户在接收到AI预测、感知准确率及不同风格解释后，记录其前后决策行为，评估客观指标（任务表现与一致性）和主观指标（决策信心）。结果显示，感知准确率与解释风格均有显著影响。
原文摘要 · Abstract (English)
Predictive Process Monitoring (PPM) often uses deep learning models to predict the future behavior of ongoing processes, such as predicting process outcomes. While these models achieve high accuracy, their lack of interpretability undermines user trust and adoption. Explainable AI (XAI) aims to address this challenge by providing the reasoning behind the predictions. However, current evaluations of XAI in PPM focus primarily on functional metrics (such as fidelity), overlooking user-centered aspects such as their effect on task performance and decision-making. This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making in PPM. We conducted a decision-making experiment, where users were presented with the AI predictions, perceived accuracy levels, and explanations of different styles. Users' decisions were measured both before and after receiving explanations, allowing the assessment of objective metrics (Task Performance and Agreement) and subjective metrics (Decision Confidence). Our findings show that perceived accuracy and explanation style have a significant effect.
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