Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

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Is Attention Interpretable?

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What do we need to build explainable AI systems for the medical domain?

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A Survey of Methods for Explaining Black Box Models

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Metrics for explainable AI: challenges and prospects

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Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

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A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations

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Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects

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Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

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AI Trustworthiness in Manufacturing: Challenges, Toolkits, and the Path to Industry 5.0

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The Role of Causality in Explainable Artificial Intelligence

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Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy

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Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges

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Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

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Explainable AI and Causal Understanding: Counterfactual Approaches Considered

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How AI tools can—and cannot—help organizations become more ethical

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Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery

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Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

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Explainable quantum clustering method to model medical data

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Explainable Artificial Intelligence: Counterfactual Explanations for Risk-Based Decision-Making in Construction

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Stable and actionable explanations of black-box models through factual and counterfactual rules

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The Role of Human Knowledge in Explainable AI

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Reduce System Redundancy and Optimize Sensor Disposition for EMG–IMU Multimodal Fusion Human–Machine Interfaces With XAI

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Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study

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Interpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review

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Improving Trustworthiness of AI Solutions: A Qualitative Approach to Support Ethically-Grounded AI Design

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Intelligent systems in healthcare: A systematic survey of explainable user interfaces

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A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations

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Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives

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Chromatics in Urban Landscapes: Integrating Interactive Genetic Algorithms for Sustainable Color Design in Marine Cities

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MDF-Net for abnormality detection by fusing X-rays with clinical data

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A nascent design theory for explainable intelligent systems

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DiCE4EL: Interpreting Process Predictions using a Milestone-Aware Counterfactual Approach

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Enabling causality learning in smart factories with hierarchical digital twins

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A Systematic Survey on Security in Anonymity Networks: Vulnerabilities, Attacks, Defenses, and Formalization

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Explainable AI for Predictive Analytics on Employee Attrition

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Enabling Explainable AI in Cybersecurity Solutions

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Real Estate Automated Valuation Model with Explainable Artificial Intelligence Based on Shapley Values

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Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation

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Explainable AI methods for drug discovery: A survey of interpretability, metrics and mechanistic insight

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Explainable AI (XAI) for AI-Acceptability: The Coming Age of Digital Management 5.0

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SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems

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Explainable Transfer Learning for Modeling and Assessing Risks in Tunnel Construction

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The AI-DEC: A Card-based Design Method for User-centered AI Explanations

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Causal Artificial Intelligence for High-Stakes Decisions: The Design and Development of a Causal Machine Learning Model

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A Survey of AI to AMD and Quantitative Analysis of AMD Pathology Based on Medical Images

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CUBE: Causal Intervention-Based Counterfactual Explanation for Prediction Models

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