Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more concentrated on the objects associated with the image's ground-truth class. We show that smooth decision boundaries play an important role in this enhanced interpretability, as the model's input gradients around data points will more closely align with boundaries' normal vectors when they are smooth. Thus, because robust models have smoother boundaries, the results of gradient-based attribution methods, like Integrated Gradients and DeepLift, will capture more accurate information about nearby decision boundaries. This understanding of robust interpretability leads to our second contribution: \emph{boundary attributions}, which aggregate information about the normal vectors of local decision boundaries to explain a classification outcome. We show that by leveraging the key factors underpinning robust interpretability, boundary attributions produce sharper, more concentrated visual explanations -- even on non-robust models. Any example implementation can be found at \url{https://github.com/zifanw/boundary}.
Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；Department of Mechanical Engineering, Imperial College London（伦敦帝国理工学院机械工程系）；Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区计算机科学与技术学院）
Less is More: Fewer Interpretable Region via Submodular Subset Selection
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
National University of Singapore（新加坡国立大学）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工学院研究与技术联盟）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；CSAIL, MIT（麻省理工学院计算机科学与人工智能实验室）
FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
Independent Researcher（独立研究者）；Indiana University at Bloomington（印第安纳大学布卢明顿分校）；National Yang Ming Chiao Tung University；National Taiwan University（国立台湾大学）；Boise State University（博伊州立大学）
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Cluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen（机器学习新视角科学卓越中心，图宾根大学）；Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg（计算机科学系，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Hertie Institute for Artificial Intelligence in Brain Health, University of Tübingen（脑健康人工智能研究所，图宾根大学）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern（糖尿病、内分泌学、营养医学和代谢学系，伯恩大学医院，伯恩大学）；Faculty of Health Sciences and Medicine, University of Lucerne（健康科学与医学学院，卢塞恩大学）
EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis
Dept. of Machine Learning Moffitt Cancer Center（机器学习系莫菲特癌症中心）；Dept. of Cancer Epidemiology Moffitt Cancer Center（癌症流行病学系莫菲特癌症中心）；Dept. of Electrical Engineering University of South Florida（电气工程系佛罗里达州立大学）
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
Faculty of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Center for Biomedical Image Computing and Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
Lahore University of Management Sciences, Pakistan（拉合尔管理科学大学，巴基斯坦）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Johannes Kepler University Linz（约翰·开普勒林茨大学）；Novo Nordisk A/S（诺和诺德公司）；University Hospital Basel（巴塞尔大学医院）；Imprint Labs, LLC.（Imprint Labs有限责任公司）
CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Jinesis AI Lab（Jinesis人工智能实验室）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；CMU（卡内基梅隆大学）；EuroSafeAI；ELLIS Institute Tübingen（图宾根ELLIS研究所）
Explanation-Guided Adversarial Training for Robust and Interpretable Models
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Fuzhou University（福州大学）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（神经网络与模式识别实验室、机器学习与智能系统研究所、自动化研究所，中国科学院）
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）
Fractal Flow: Hierarchical and Interpretable Normalizing Flow via Topic Modeling and Recursive Strategy
School of Mathematics Harbin Institute of Technology（哈尔滨工业大学数学学院）；School of Mathematics and Institute for Artificial Intelligence Harbin Instiute of Technology（哈尔滨工业大学数学学院和人工智能研究院）；School of Earth and Space Sciences and Institute for Artificial Intelligence Peking University（北京大学地球和空间科学学院和人工智能研究院）
