Machine learning models are increasingly applied to biomedical data, yet their adoption in high stakes domains remains limited by poor robustness, limited interpretability, and instability of learned features under realistic data perturbations, such as missingness. In particular, models that achieve high predictive performance may still fail to inspire trust if their key features fluctuate when data completeness changes, undermining reproducibility and downstream decision-making. Here, we present CACTUS (Comprehensive Abstraction and Classification Tool for Uncovering Structures), an explainable machine learning framework explicitly designed to address these challenges in small, heterogeneous, and incomplete clinical datasets. CACTUS integrates feature abstraction, interpretable classification, and systematic feature stability analysis to quantify how consistently informative features are preserved as data quality degrades. Using a real-world haematuria cohort comprising 568 patients evaluated for bladder cancer, we benchmark CACTUS against widely used machine learning approaches, including random forests and gradient boosting methods, under controlled levels of randomly introduced missing data. We demonstrate that CACTUS achieves competitive or superior predictive performance while maintaining markedly higher stability of top-ranked features as missingness increases, including in sex-stratified analyses. Our results show that feature stability provides information complementary to conventional performance metrics and is essential for assessing the trustworthiness of machine learning models applied to biomedical data. By explicitly quantifying robustness to missing data and prioritising interpretable, stable features, CACTUS offers a generalizable framework for trustworthy data-driven decision support.
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence
Institute of Electronics, Information Engineering and Telecommunications - National Research Council of Italy (CNR-IEIIT)（电子信息与电信研究所 - 意大利国家科研理事会（CNR-IEIIT））；Politecnico di Torino - Department of Control and Computer Engineering (DAUIN)（托尼诺理工学院 - 控制与计算机工程系（DAUIN））；Rulex Innovation Labs（Rulex创新实验室）
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
From objective discovery to prediction of global ocean eco-provinces: A pathway for trustworthy learning
University of Washington（华盛顿大学）；NOAA Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局地球物理流体动力学实验室）；Massachusetts Institute of Technology（麻省理工学院）；University of California Davis（加州大学戴维斯分校）
Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems
Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）；Institute for Energy Efficiency in Production EEP, University of Stuttgart（生产能效研究所，斯图加特大学）；Institute of Industrial Manufacturing and Management IFF, University of Stuttgart（工业制造与管理研究所，斯图加特大学）
BACON: A fully explainable AI model with graded logic for decision making problems
Department of Information Systems University of Maryland, Baltimore County (UMBC)（信息系统系大学马里兰大学巴尔的摩县（UMBC））；Department of Computer Science San Francisco State University（计算机科学系旧金山州立大学）；Department of Information Systems UMBC（信息系统系大学马里兰大学巴尔的摩县（UMBC））
EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification
Dept. of Electrical and Electronic Engineering（电子与电气工程系）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Dept. of Electronics & Communication Engineering（电子与通信工程系）；Khulna University of Engineering and Technology（库尔纳工程与技术大学）
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics
Imperial College London（伦敦帝国理工学院）；Princeton University（普林斯顿大学）；NOAA/Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局/地球物理流体动力学实验室）；University of Washington（华盛顿大学）；Institut Pierre-Simon Laplace（皮埃尔-西蒙·拉普拉斯研究所）；IRD（法国发展研究院）；Sorbonne Université（索邦大学）；LOCEAN-IPSL（皮埃尔-西蒙·拉普拉斯环境研究所海洋学与气候实验室）；CNRS（法国国家科学研究中心）；Laboratoire des Sciences du Climat et de l’Environnement（气候与环境科学实验室）；CEA Saclay（法国原子能和替代能源委员会萨克雷中心）
Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency
Sun Yat-sen University（中山大学）；EURECOM（欧洲通信大学（EURECOM））；Zhejiang University（浙江大学）；University of Illinois Urbana-Champaign Institute（伊利诺伊大学厄巴纳-香槟分校研究院）；Singapore University of Technology and Design（新加坡科技设计大学）
Institute of Logic, Language, and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Pacmed（Pacmed公司）；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）
Geometry-based Schrödinger Bridges for Trustworthy Multimodal Fusion
Department of Computer Science（计算机科学系）；Techonology, Huaqiao University（技术学系，华侨大学）；Xiamen Key Laboratory of Computer Vision（厦门计算机视觉实验室）；Pattern Recognition, Huaqiao University（模式识别，华侨大学）；Tongji University（同济大学）；School of Cyber Science（网络科学学院）；Engineering, Wuhan University（工程学院，武汉大学）
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
Context-Aware Concept Distillation for Trustworthy Flood Prediction
School of Computer Science and Engineering, The Hebrew University of Jerusalem（耶路撒冷希伯来大学计算机科学与工程学院）；Institute of Earth Sciences, The Hebrew University of Jerusalem（耶路撒冷希伯来大学地球科学研究所）；Hebrew University Business School, The Hebrew University of Jerusalem（耶路撒冷希伯来大学商学院）
ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
organization= Faculty of Information Technology；Electrical Engineering, University of Oulu , addressline= , city= Oulu , postcode= 90570 , state= , country= Finland
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence
Andalusian Institute of Data Science and Computational Intelligence (DASCI)（安达卢西亚数据科学与计算智能研究所）；University of Granada（格拉纳达大学）；TECNALIA（泰克纳利亚科技公司）；Basque Research & Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
