Unifying local and global model explanations by functional decomposition of low dimensional structures
University of Copenhagen（哥本哈根大学）；Heidelberg University（海德堡大学）；Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所（BIPS））；University of Bremen（不来梅大学）
We consider a global representation of a regression or classification function by decomposing it into the sum of main and interaction components of arbitrary order. We propose a new identification constraint that allows for the extraction of interventional SHAP values and partial dependence plots, thereby unifying local and global explanations. With our proposed identification, a feature's partial dependence plot corresponds to the main effect term plus the intercept. The interventional SHAP value of feature $k$ is a weighted sum of the main component and all interaction components that include $k$, with the weights given by the reciprocal of the component's dimension. This brings a new perspective to local explanations such as SHAP values which were previously motivated by game theory only. We show that the decomposition can be used to reduce direct and indirect bias by removing all components that include a protected feature. Lastly, we motivate a new measure of feature importance. In principle, our proposed functional decomposition can be applied to any machine learning model, but exact calculation is only feasible for low-dimensional structures or ensembles of those. We provide an algorithm and efficient implementation for gradient-boosted trees (xgboost) and random planted forest. Conducted experiments suggest that our method provides meaningful explanations and reveals interactions of higher orders. The proposed methods are implemented in an R package, available at \url{https://github.com/PlantedML/glex}.
Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
Department of Informatics, USI Lugano（乌里大学信息学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Institute of Informatics, LMU Munich（慕尼黑大学信息研究所）
Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
Department of Statistics, University of California, Berkeley（加州大学伯克利分校统计学系）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计学与数据科学系）
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
Leibniz Institute for Prevention Research（预防研究与流行病学研究所）；Faculty of Mathematics（数学系）；Computer Science, University of Bremen（计算机科学，不莱梅大学）；University of Warsaw（华沙大学）；Centre for Credible AI, Warsaw University of Technology（可信人工智能中心，华沙技术大学）；Bielefeld University（比勒菲尔德大学）
Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
Berlin Institute for the Foundations of Learning（学习与数据基础研究院）；Machine Learning Group, Technical University of Berlin（柏林技术大学机器学习组）；Department of Artificial Intelligence, Korea University（韩国大学人工智能系）；Max Planck Institute for Informatics（信息研究所）；Department of Chemistry, Chemical Physics Theory Group, University of Toronto（多伦多大学化学系、化学物理理论组）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Acceleration Consortium, University of Toronto（多伦多大学加速联盟）
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；TU Graz（格拉茨工业大学）；BOKU University Vienna（维也纳农业大学）；TECNALIA（泰克纳利亚）；University of the Basque Country (EHU)（巴斯克大学）
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Berlin Institute for the Foundations of Learning（柏林学习与数据基础研究所）；Machine Learning Group, Technical University of Berlin（柏林技术大学机器学习组）；Department of Artificial Intelligence, Korea University（韩国大学人工智能系）；Department of Mathematics and Computer Science, Free University of Berlin（柏林自由大学数学与计算机科学系）
