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

arXiv: https://arxiv.org/abs/2409.00265 · HTML: https://mlsanity.com/paper/2409.00265/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 101 of its 221 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 30 of 101)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [NExT-GPT: Any-to-Any Multimodal LLM](https://mlsanity.com/paper/2309.05519/) (2023)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)

## Built on by (most cited first, 30 of 46)

- [Out-of-Distribution Generalization in Time Series: A Survey](https://mlsanity.com/paper/2503.13868/) (2025)
- [Semantic-Driven Topic Modeling Using Transformer-Based Embeddings and Clustering Algorithms](https://mlsanity.com/paper/2410.00134/) (2024)
- [Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI](https://mlsanity.com/paper/2410.02609/) (2024)
- [Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models](https://mlsanity.com/paper/2501.15374/) (2025)
- [A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs](https://mlsanity.com/paper/2503.05050/) (2025)
- [Explainable AI: XAI-Guided Context-Aware Data Augmentation](https://mlsanity.com/paper/2506.03484/) (2025)
- [Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations](https://mlsanity.com/paper/2410.15374/) (2024)
- [Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech](https://mlsanity.com/paper/2411.08013/) (2024)
- [ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference](https://mlsanity.com/paper/2508.16703/) (2025)
- [Instance-Aligned Captions for Explainable Video Anomaly Detection](https://mlsanity.com/paper/2601.08155/) (2026)
- [Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification](https://mlsanity.com/paper/2501.04009/) (2024)
- [PEDANTIC: A Dataset for the Automatic Examination of Definiteness in Patent Claims](https://mlsanity.com/paper/2505.21342/) (2025)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)
- [Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions](https://mlsanity.com/paper/2509.25539/) (2025)
- [Improving the Perturbation-Based Explanation of Deepfake Detectors Through the Use of Adversarially-Generated Samples](https://mlsanity.com/paper/2502.03957/) (2025)
- [Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A Phase-specific Survey](https://mlsanity.com/paper/2505.07058/) (2025)
- [Semantic-Driven Topic Modeling for Analyzing Creativity in Virtual Brainstorming](https://mlsanity.com/paper/2509.16835/) (2025)
- [Priority-Aware Shapley Value](https://mlsanity.com/paper/2602.09326/) (2026)
- [Concerning Uncertainty -- A Systematic Survey of Uncertainty-Aware XAI](https://mlsanity.com/paper/2603.26838/) (2026)
- [Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review](https://mlsanity.com/paper/2504.10527/) (2025)
- [Recent Advances and Future Directions in Literature-Based Discovery](https://mlsanity.com/paper/2506.12385/) (2025)
- [Counterfactual Visual Explanation via Causally-Guided Adversarial Steering](https://mlsanity.com/paper/2507.09881/) (2025)
- [Bilingual Word Level Language Identification for Omotic Languages](https://mlsanity.com/paper/2509.07998/) (2025)
- [Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models](https://mlsanity.com/paper/2602.16608/) (2026)
- [XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning](https://mlsanity.com/paper/2603.01212/) (2026)
- [Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results](https://mlsanity.com/paper/2603.02948/) (2026)
- [A Two-Stage LLM Framework for Accessible and Verified XAI Explanations](https://mlsanity.com/paper/2604.12543/) (2026)
- [AttentionBender: Manipulating Cross-Attention in Video Diffusion Transformers as a Creative Probe](https://mlsanity.com/paper/2604.20936/) (2026)
- [NeuroViz: Real-time Interactive Visualization of Forward and Backward Passes in Neural Network Training](https://mlsanity.com/paper/2605.02044/) (2026)
- [Enhancing Trust Through Standards: A Comparative Risk-Impact Framework for Aligning ISO AI Standards with Global Ethical and Regulatory Contexts](https://mlsanity.com/paper/2504.16139/) (2025)

## 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.