The field of explainable artificial intelligence (XAI) attempts to develop methods that provide insight into how complicated machine learning methods make predictions. Many methods of explanation have focused on the concept of feature attribution, a decomposition of the model's prediction into individual contributions corresponding to each input feature. In this work, we explore the problem of feature attribution in the context of Gaussian process regression (GPR). We take a principled approach to defining attributions under model uncertainty, extending the existing literature. We show that although GPR is a highly flexible and non-parametric approach, we can derive interpretable, closed-form expressions for the feature attributions. When using integrated gradients as an attribution method, we show that the attributions of a GPR model also follow a Gaussian process distribution, which quantifies the uncertainty in attribution arising from uncertainty in the model. We demonstrate, both through theory and experimentation, the versatility and robustness of this approach. We also show that, when applicable, the exact expressions for GPR attributions are both more accurate and less computationally expensive than the approximations currently used in practice. The source code for this project is freely available under MIT license at https://github.com/KurtButler/2024_attributions_paper.
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；Department of Computer Science（计算机科学系）；School of Information（信息学院）；University of Texas, Austin（得克萨斯大学奥斯汀分校）
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Embry-Riddle Aeronautical University（安柏瑞德航空大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Tennessee at Martin（田纳西大学马丁分校）；Bowling Green State University（博林格林州立大学）；Old Dominion University（奥多明尼昂大学）
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning
Süleyman Demirel University（苏莱曼·德米雷尔大学）；Burdur Mehmet Akif Ersoy University（布尔杜尔穆罕默德·阿基夫·埃尔索伊大学）；Karunya Institute of Technology & Sciences（卡鲁尼亚技术与科学学院）
An Explainable Decision Support System for Predictive Process Analytics
IBM（国际商业机器公司）；University of Padua（帕多瓦大学）
预测性流程分析正在成为组织的重要辅助工具，为其流程提供在线运营支持。然而，需要向流程涉众提供一个解释，解释为什么一个给定的流程执行被预测为以某种方式运行。否则，他们将不太可能信任的预测监测技术，因此，采用it.This本文提出了一个预测分析框架，也配备了解释能力的基础上博弈论的Shapley值。该框架已在IBM Process Mining套件中实现，并已商业化，可供业务用户使用。该框架已在真实事件数据上进行了测试，以评估预测和相应评价的质量。特别是，已经进行了用户评价，以了解系统提供的解释是否能为流程利益相关者所理解。
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
