mlsanityData to 7 Oct 2026

Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction

arXiv 2409.00265 ↗First posted 30 Aug 2024cs.AI cs.CL cs.CYcited by 46

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Abstract (arXiv)

Artificial intelligence models encounter significant challenges due to their black-box nature, particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles. Explainable Artificial Intelligence (XAI) addresses these challenges by providing explanations for how these models make decisions and predictions, ensuring transparency, accountability, and fairness. Existing studies have examined the fundamental concepts of XAI, its general principles, and the scope of XAI techniques. However, there remains a gap in the literature as there are no comprehensive reviews that delve into the detailed mathematical representations, design methodologies of XAI models, and other associated aspects. This paper provides a comprehensive literature review encompassing common terminologies and definitions, the need for XAI, beneficiaries of XAI, a taxonomy of XAI methods, and the application of XAI methods in different application areas. The survey is aimed at XAI researchers, XAI practitioners, AI model developers, and XAI beneficiaries who are interested in enhancing the trustworthiness, transparency, accountability, and fairness of their AI models.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 101 of its 221 references that are in the map. First posted on arXiv 30 Aug 2024.

Builds on

101 of 221 references.

References · 101

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  7. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  8. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  9. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  10. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  11. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  12. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
38 more
  1. 2016Concrete Problems in AI Safetycited by 1,582
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  4. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  5. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  6. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  7. 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
  8. 2019Attention is not Explanationcited by 916
  9. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 844
  10. 2020Quantifying Attention Flow in Transformerscited by 759
  11. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  12. 2020Transformer Interpretability Beyond Attention Visualizationcited by 620
  13. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  14. 2023NExT-GPT: Any-to-Any Multimodal LLMcited by 610
  15. 2019ERASER: A Benchmark to Evaluate Rationalized NLP Modelscited by 523
  16. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  17. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  18. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  19. 2023Larger language models do in-context learning differentlycited by 386
  20. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 358
  21. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  22. 2021Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformerscited by 316
  23. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  24. 2015WIDER FACE: A Face Detection Benchmarkcited by 297
  25. 2020A Survey on Neural Network Interpretabilitycited by 291
  26. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  27. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  28. 2015Explaining NonLinear Classification Decisions with Deep Taylor Decompositioncited by 248
  29. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matterscited by 221
  30. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  31. 2018Deep Learning: A Critical Appraisalcited by 214
  32. 2017Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Modelscited by 202
  33. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  34. 2020Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Reviewcited by 174
  35. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  36. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 165
  37. 2017Interpretable Convolutional Neural Networkscited by 161
  38. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160

50 most cited of 101 All 101 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 21; 2026: 20.

46 papers.

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  1. 2025Out-of-Distribution Generalization in Time Series: A Surveycited by 15
  2. 2024Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithmscited by 8
  3. 2024Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AIcited by 8
  4. 2025Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Modelscited by 7
  5. 2025A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMscited by 7
  6. 2025Explainable AI: XAI-Guided Context-Aware Data Augmentationcited by 5
  7. 2024Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanationscited by 4
  8. 2024Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speechcited by 4
  9. 2025ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inferencecited by 4
  10. 2026Instance-Aligned Captions for Explainable Video Anomaly Detectioncited by 4
  11. 2024Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classificationcited by 3
  12. 2025PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claimscited by 3
34 more
  1. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  2. 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
  3. 2025Improving the Perturbation-Based Explanation of Deepfake Detectors Through the Use of Adversarially-Generated Samplescited by 2
  4. 2025Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Surveycited by 2
  5. 2025Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstormingcited by 2
  6. 2026Priority-Aware Shapley Valuecited by 2
  7. 2026Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAIcited by 2
  8. 2025Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Reviewcited by 1
  9. 2025Recent Advances and Future Directions in Literature-Based Discoverycited by 1
  10. 2025Counterfactual Visual Explanation via Causally-Guided Adversarial Steeringcited by 1
  11. 2025Bilingual Word Level Language Identification for Omotic Languagescited by 1
  12. 2026Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Modelscited by 1
  13. 2026XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoningcited by 1
  14. 2026A Two-Stage LLM Framework for Accessible and Verified XAI Explanationscited by 1
  15. 2026AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probecited by 1
  16. 2026NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Trainingcited by 1
  17. 2025Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contextscited by 0
  18. 2025A Concept-based approach to Voice Disorder Detectioncited by 0
  19. 2025Charting the Future of Scholarly Knowledge with AI: A Community Perspectivecited by 0
  20. 2025Feature Attribution in 5G Intrusion Detection: A Statistical vs. Logic-Based Comparisoncited by 0
  21. 2025Random Feature Spiking Neural Networkscited by 0
  22. 2025Toward Explaining Large Language Models in Software Engineering Taskscited by 0
  23. 2026Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricismcited by 0
  24. 2026Grandes Modelos de Linguagem Multimodais (MLLMs): Da Teoria `a Pr'aticacited by 0
  25. 2026Rules or Weights? Comparing User Understanding of Explainable AI Techniques with the Cognitive XAI-Adaptive Modelcited by 0
  26. 2026The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learningcited by 0
  27. 2026Behavior Learning (BL): Learning Hierarchical Optimization Structures from Datacited by 0
  28. 2026cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Contextcited by 0
  29. 2026Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Resultscited by 0
  30. 2026Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanationscited by 0
  31. 2026Explaining RhythmFormer: A Systematic XAI Analysis of Periodic Sparse Attention for Remote Photoplethysmographycited by 0
  32. 2026Beyond Shapley: Efficient Computation of Asymmetric Shapley Valuescited by 0
  33. 2026Explainable Reinforcement Learning via Physics-Aware Policy Distillationcited by 0
  34. 2026From objective discovery to prediction of global ocean eco-provinces: A pathway for trustworthy learningcited by 0