Learning an explainable classifier often results in low accuracy model or ends up with a huge rule set, while learning a deep model is usually more capable of handling noisy data at scale, but with the cost of hard to explain the result and weak at generalization. To mitigate this gap, we propose an end-to-end deep explainable learning approach that combines the advantage of deep model in noise handling and expert rule-based interpretability. Specifically, we propose to learn a deep data assessing model which models the data as a graph to represent the correlations among different observations, whose output will be used to extract key data features. The key features are then fed into a rule network constructed following predefined noisy expert rules with trainable parameters. As these models are correlated, we propose an end-to-end training framework, utilizing the rule classification loss to optimize the rule learning model and data assessing model at the same time. As the rule-based computation is none-differentiable, we propose a gradient linking search module to carry the gradient information from the rule learning model to the data assessing model. The proposed method is tested in an industry production system, showing comparable prediction accuracy, much higher generalization stability and better interpretability when compared with a decent deep ensemble baseline, and shows much better fitting power than pure rule-based approach.
Towards Explainable Neural-Symbolic Visual Reasoning
Segula Technologies（赛古拉技术公司）；Institut des Systèmes Intelligents et de Robotique, Sorbonne Universite（索邦大学智能系统与机器人研究所）；INRIA（法国国家信息与自动化研究所）；ENSTA Paris（巴黎高等先进技术学院）
许多高性能模型都缺乏可解释性。有越来越多的工作涌入可解释的人工智能（XAI），以解开什么是XAI的意思和期望。然而，对于如何产生和判断解释，并没有普遍的共识。在本文中，我们讨论了为什么整合连接主义和符号范式的技术是为非技术用户提供解释的最有效解决方案，并且我们提出了一个基于Doran et al. [2017]（arXiv：1710.00794）定义的推理模型来解释神经网络的决策。我们使用这种解释来纠正网络决策原理中的偏差。我们根据Burns等人[2018]（arXiv：1803.09797）中的图像描述方法，为该模型提供了一个潜在用途的示例。
Bridging Efficiency and Transparency: Explainable CoT Compression in Multimodal Large Reasoning Models
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of Computer Network and Information Integration (SEU), Ministry of Education, China（计算机网络与信息集成重点实验室（SEU））
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
Institute of Informatics, LMU Munich（慕尼黑大学信息学研究所）；Corporate Technology, Siemens AG（西门子股份公司中央研究院）；Department of Informatics, Technical University of Munich（慕尼黑工业大学信息学系）
Forward Composition Propagation for Explainable Neural Reasoning
Eindhoven University of Technology（埃因霍温理工大学）；Tilburg University（蒂尔堡大学）；Universidad de Granada（格拉纳达大学）；Universidad de Talca（塔尔卡大学）；Warsaw University of Technology（华沙理工大学）
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation
Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算机科学中心（国家济南超级计算中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；University of Technology Sydney（悉尼科技大学）
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
Modeling Endogenous Logic: Causal Neuro-Symbolic Reasoning Model for Explainable Multi-Behavior Recommendation
School of Computer Science and Engineering（计算机科学与工程学院）；Nanjing University of Science and Technology（南京理工大学）；School of Management（管理学院）；Hefei University of Technology（合肥工业大学）；School of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Nanjing University of Finance and Economics（南京财经大学）；Department of Finance and Accounting（金融与会计系）；Saint Petersburg State University（圣彼得堡国立大学）；School of Computing（计算机学院）；Macquarie University（麦考瑞大学）
ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
University of Stuttgart（斯图加特大学）；Bosch Center for AI（博世人工智能中心）；Cardiff University（卡迪夫大学）；University of Oxford（牛津大学）；University of Cambridge（剑桥大学）；Stanford University（斯坦福大学）；University of Oslo（奥斯陆大学）；University of Southampton（南安普顿大学）
Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
Department of Biomedical Engineering, Cullen College of Engineering（生物医学工程系，工程学院）；University of Houston（德克萨斯大学）；Department of Health Systems & Population Health Sciences, Tilman J. Fertitta Family College of Medicine（健康系统与人群健康科学系，蒂尔曼·J·费尔蒂塔家庭医学院）；University of Toronto（多伦多大学）；Virginia Tech（弗吉尼亚理工学院）
A Verifier-Guided Explainable Reasoning Framework with Gold-Anchored QLoRA, Task-Aware Mixture-of-Experts, and Group-Relative RLVR
University of Information Technology (UIT)（信息科技大学）；Ho Chi Minh City University of Technology (HCMUT)（胡志明市理工大学）；Vietnam National University, Ho Chi Minh City（越南国家大学胡志明市分校）；University of Economics Ho Chi Minh City (UEH)（胡志明市经济大学）
Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning
Shanghai Institute of AI Education, East China Normal University, Shanghai, 200241, China（上海人工智能教育研究院，华东师范大学，上海，200241，中国）；Shool of Computer Sicence and Technology, East China Normal University, Shanghai, 200241, China（计算机科学与技术学院，华东师范大学，上海，200241，中国）
Assessing the Real-World Utility of Explainable AI for Arousal Diagnostics: An Application-Grounded User Study
IT-Designers Gruppe Esslingen am Neckar（埃斯林根内卡尔IT设计组）；University of Tübingen（图宾根大学）；University of Giessen（吉森大学）；Klinikum Esslingen, Klinik für Kardiologie, Pneumologie und Angilologie（埃斯林根克林克医院，心内科、呼吸科和内科）；Technical University of Munich（慕尼黑技术大学）
EDVD-LLaMA: Explainable Deepfake Video Detection via Multimodal Large Language Model Reasoning
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电子与电气工程系，香港理工大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）；Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（智能工程学院，华南理工大学）
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
University of Cambridge（剑桥大学）；Mila - Quebec AI Institute（魁北克人工智能研究院）；McGill University（麦吉尔大学）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Stuttgart（斯图加特大学）；Amazon（亚马逊公司）；AITHYRA
Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning
Old Dominion University, Norfolk, VA, USA（老奥本大学）；Center for Wireless Communications, University of Oulu, Finland（无线通信中心，奥卢大学）；Florida International University, USA（佛罗里达国际大学）；Nanyang Technological University, Singapore（南洋理工大学）；University of Colombo, Sri Lanka（科伦坡大学）；Accenture Technology Labs, Arlington, VA, USA（Accenture技术实验室）
