Explainable artificial intelligence: an analytical review

Deep Residual Learning for Image Recognition

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Random Forests

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Deep learning

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Going deeper with convolutions

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Human-level control through deep reinforcement learning

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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A Unified Approach to Interpreting Model Predictions

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Generative Adversarial Nets

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A fast learning algorithm for deep belief nets

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Learning Deep Features for Discriminative Localization

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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Pattern Recognition and Machine Learning

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Support vector machines

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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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Backpropagation through time: what it does and how to do it

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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

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Some Studies in Machine Learning Using the Game of Checkers

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Learning Deconvolution Network for Semantic Segmentation

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The Black Box Society: The Secret Algorithms That Control Money and Information

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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks

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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

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Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

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An empirical study of the naive Bayes classifier

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Explaining Explanations: An Overview of Interpretability of Machine Learning

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K-nearest neighbor

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Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

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European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”

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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

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Deep blue

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The accuracy, fairness, and limits of predicting recidivism

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A Survey on the Explainability of Supervised Machine Learning

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Principles of artificial intelligence

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

Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636 · see citing works

Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint

Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000249929 · Cited by 634 · see citing works

Decision trees and decision-making

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Learning decision tree classifiers

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Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

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Towards Automatic Concept-based Explanations

Ghorbani, A., Wexler, J., Zou, J., & Kim, B. (2019) · arXiv · doi:10.48550/arxiv.1902.03129 · Cited by 378 · see citing works

Towards explainable deep neural networks (xDNN)

Angelov, P., & Soares, E. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.010 · Cited by 292 · see citing works

Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning

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Connectionist AI, symbolic AI, and the brain

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Prototype selection for interpretable classification

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Meaningful Explanations of Black Box AI Decision Systems

Pedreschi, D., Giannotti, F., Guidotti, R., Monreale, A., Ruggieri, S., & Turini, F. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019780 · Cited by 233 · see citing works

A Shared Vision for Machine Learning in Neuroscience

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ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

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Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

Fellous, J., Sapiro, G., Rossi, A. F., Mayberg, H., & Ferrante, M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01346 · Cited by 156 · see citing works

Explainable Artificial Intelligence Applications in NLP, Biomedical, and Malware Classification: A Literature Review

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Why model why? Assessing the strengths and limitations of LIMEPreprint

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Explaining Models by Propagating Shapley Values of Local Components

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Four Principles of Explainable Artificial Intelligence

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Artificial cognition: How experimental psychology can help generate explainable artificial intelligence

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Building Explainable Artificial Intelligence Systems

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Global Explanations of Neural Networks

Ibrahim, M., Louie, M., Modarres, C., & Paisley, J. W. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314230 · Cited by 126 · see citing works

Software transparency as a key requirement for self-driving cars

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Explaining Deep Learning Models Through Rule-Based Approximation and Visualization

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Towards Interpretability of Segmentation Networks by Analyzing DeepDreams

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Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data

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Toward Anthropomorphic Machine Learning

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Alan Turing’s unorganized machines and artificial neural networks: his remarkable early work and future possibilities

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An Examination of the Algorithmic Accountability Act of 2019Preprint

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Shallow Unorganized Neural Networks Using Smart Neuron Model for Visual Perception

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Who Killed Elaine Herzberg?

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Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios

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Neutron holography makes its debut

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System for quantitative diagnosis of COVID-19 associated Pneumonia based on Superpixels with deep learning and chest CT

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Towards an Explainable Threat Detection Tool.

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Fair-by-design explainable models for prediction of recidivism

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System for Quantitative Diagnosis of COVID-19-Associated Pneumonia Based on Superpixels With Deep Learning and Chest CTPreprint

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Next Generation Trustworthy Fraud Detection

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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

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A Review of Explainable Artificial Intelligence in Healthcare

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Explainable AI for Healthcare 5.0: Opportunities and Challenges

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A systematic review of explainable artificial intelligence models and applications: recent developments and future trends

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The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education

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Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation

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A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

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Explainable Artificial Intelligence in CyberSecurity: A Survey

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Human-centric artificial intelligence architecture for industry 5.0 applications

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Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning

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Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain

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Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

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Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction

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Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

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Emotion recognition in EEG signals using deep learning methods: A review

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Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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A literature review on applications of explainable artificial intelligence (XAI)

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

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Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

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Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction

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A survey on explainable artificial intelligence for cybersecurity

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The impact of large language models on higher education: exploring the connection between AI and Education 4.0

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Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 148 · see citing works

Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review

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Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review

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Smart Health

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XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process

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Short-term electricity load forecasting—A systematic approach from system level to secondary substations

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Explainable AI approaches in deep learning: Advancements, applications and challenges

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Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence

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Machine Learning and Artificial Intelligence in the Multi-Omics Approach to Gut Microbiota

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Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications

Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100 · see citing works

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Theodorakopoulos, L., Theodoropoulou, A., & Stamatiou, Y. C. (2024) · Eng—Advances in Engineering · doi:10.3390/eng5030068 · Cited by 101 · see citing works

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AI Security for Geoscience and Remote Sensing: Challenges and future trends

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Explainable Deep Learning Models With Gradient-Weighted Class Activation Mapping for Smart Agriculture

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Explainable hybrid vision transformers and convolutional network for multimodal glioma segmentation in brain MRI

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Numerical Grad-Cam Based Explainable Convolutional Neural Network for Brain Tumor Diagnosis

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Härtung, T. (2023) · ALTEX · doi:10.14573/altex.2309191 · Cited by 88 · see citing works

Hybrid modelling of water resource recovery facilities: status and opportunities

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SignExplainer: An Explainable AI-Enabled Framework for Sign Language Recognition With Ensemble Learning

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Harnessing LLMs for multi-dimensional writing assessment: Reliability and alignment with human judgments

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The Integration of AI and Metaverse in Education: A Systematic Literature Review

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Explainable Artificial Intelligence (XAI) for Deep Learning Based Medical Imaging Classification

Ghnemat, R., Alodibat, S., & Al-Haija, Q. S. A. (2023) · Journal of Imaging · doi:10.3390/jimaging9090177 · Cited by 70 · see citing works

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An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors

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Artificial intelligence in knowledge management: Identifying and addressing the key implementation challenges

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A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications

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Explainable Artificial Intelligence (XAI): A Systematic Literature Review on Taxonomies and Applications in Finance

Martins, T., de Almeida, A., Cardoso, E., & Nunes, L. (2023) · IEEE Access · doi:10.1109/access.2023.3347028 · Cited by 70 · see citing works

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Zdražil, L., Cadranel, A., Medveď, M., Otyepka, M., Zbořil, R., & Guldi, D. M. (2024) · Chem · doi:10.1016/j.chempr.2024.07.018 · Cited by 56 · see citing works

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Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2022) · ACM Computing Surveys · doi:10.1145/3527152 · Cited by 54 · see citing works

Explainable automated recognition of emotional states from canine facial expressions: the case of positive anticipation and frustration

Boneh-Shitrit, T., Feighelstein, M., Bremhorst, A., Amir, S., Distelfeld, T., Dassa, Y., Yaroshetsky, S., Riemer, S., Shimshoni, I., Mills, D. S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-27079-w · Cited by 54 · see citing works

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Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

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Sheik, A. G., Kumar, A., Srungavarapu, C. S., Azari, M., Ambati, S. R., Bux, F., & Patan, A. K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110132 · Cited by 47 · see citing works

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Cação, J., Santos, J., & Antunes, M. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100905 · Cited by 58 · see citing works

A Naturalistic Investigation of Trust, AI, and Intelligence Work

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Fraud detection in healthcare claims using machine learning: A systematic review

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Large language models are proficient in solving and creating emotional intelligence tests

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ExHyptNet: An explainable diagnosis of hypertension using EfficientNet with PPG signals

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Explainable artificial intelligence to improve the resilience of perishable product supply chains by leveraging customer characteristics

Jauhar, S. K., Harinath, S., Krishnaswamy, V., & Paul, S. K. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06348-z · Cited by 45 · see citing works

The performance-interpretability trade-off: a comparative study of machine learning models

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Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI

Annuzzi, G., Apicella, A., Arpaïa, P., Bozzetto, L., Criscuolo, S., Benedetto, E. D., Pesola, M., & Prevete, R. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3348334 · Cited by 48 · see citing works

Bridging the Transparency Gap: What Can Explainable AI Learn from the AI Act?

Gyevnár, B., Ferguson, N., & Schäfer, B. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230367 · Cited by 43 · see citing works

The AI Future of Emergency Medicine

Petrella, R. J. (2024) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2024.01.031 · Cited by 43 · see citing works

Explainable Artificial Intelligence: A Comprehensive Review of Methods, Applications, Challenges, and Future Research Directions

Valusa, S., Guntaka, S. B., & Lingam, V. P. (2025) · International Journal of Computer Technology and Electronics Communication · doi:10.15680/ijctece.2025.0802016

Explainable Artificial Intelligence: a Systematic Review

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Untitled work

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Survey on Explainable AI: Techniques, challenges and open issues

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Explaining Explanations: An Overview of Interpretability of Machine Learning

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