# Explainable Artificial Intelligence Approaches: A Survey

arXiv: https://arxiv.org/abs/2101.09429 · HTML: https://mlsanity.com/paper/2101.09429/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 61 references that are in the map.
- First posted on arXiv 23 Jan 2021.

## Builds on (most cited first, 29 of 29)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Learning to Explain: An Information-Theoretic Perspective on Model Interpretation](https://mlsanity.com/paper/1802.07814/) (2018)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Visual Interpretability for Deep Learning: a Survey](https://mlsanity.com/paper/1802.00614/) (2018)
- [Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation](https://mlsanity.com/paper/2003.03934/) (2020)
- [Scalable Bayesian Rule Lists](https://mlsanity.com/paper/1602.08610/) (2016)
- [Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI](https://mlsanity.com/paper/1902.01876/) (2019)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [Assessing the Local Interpretability of Machine Learning Models](https://mlsanity.com/paper/1902.03501/) (2019)
- [An Adversarial Approach for Explainable AI in Intrusion Detection Systems](https://mlsanity.com/paper/1811.11705/) (2018)
- [Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability](https://mlsanity.com/paper/1904.03867/) (2019)
- [Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning](https://mlsanity.com/paper/1912.00512/) (2019)
- [An unexpected unity among methods for interpreting model predictions](https://mlsanity.com/paper/1611.07478/) (2016)
- [Explanations of model predictions with live and breakDown packages](https://mlsanity.com/paper/1804.01955/) (2018)
- [Towards Quantification of Explainability in Explainable Artificial Intelligence Methods](https://mlsanity.com/paper/1911.10104/) (2019)
- [Instance-Level Explanations for Fraud Detection: A Case Study](https://mlsanity.com/paper/1806.07129/) (2018)
- [Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response](https://mlsanity.com/paper/1911.09853/) (2019)
- [Explainability and Adversarial Robustness for RNNs](https://mlsanity.com/paper/1912.09855/) (2019)
- [Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction](https://mlsanity.com/paper/1905.11474/) (2019)
- [An Information-Theoretic Approach to Personalized Explainable Machine Learning](https://mlsanity.com/paper/2003.00484/) (2020)
- [TIP: Typifying the Interpretability of Procedures](https://mlsanity.com/paper/1706.02952/) (2017)

## Built on by (most cited first, 30 of 40)

- [Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities](https://mlsanity.com/paper/2111.06420/) (2021)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2207.06236/) (2022)
- [Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations](https://mlsanity.com/paper/2302.10671/) (2023)
- [Diagnosing AI Explanation Methods with Folk Concepts of Behavior](https://mlsanity.com/paper/2201.11239/) (2022)
- [Exploring the Robustness of In-Context Learning with Noisy Labels](https://mlsanity.com/paper/2404.18191/) (2024)
- [medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space](https://mlsanity.com/paper/2204.05376/) (2022)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022)
- [On the Robustness of Explanations of Deep Neural Network Models: A Survey](https://mlsanity.com/paper/2211.04780/) (2022)
- [Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2409.13723/) (2024)
- [Trustworthy AI Must Account for Interactions](https://mlsanity.com/paper/2504.07170/) (2025)
- [CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs](https://mlsanity.com/paper/2304.05207/) (2023)
- [Distribution Guided Active Feature Acquisition](https://mlsanity.com/paper/2410.03915/) (2024)
- [Towards Transparent AI: A Survey on Explainable Language Models](https://mlsanity.com/paper/2509.21631/) (2025)
- [Automatic Infectious Disease Classification Analysis with Concept Discovery](https://mlsanity.com/paper/2209.02415/) (2022)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Multi-Layer Attention-Based Explainability via Transformers for Tabular Data](https://mlsanity.com/paper/2302.14278/) (2023)
- [The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges](https://mlsanity.com/paper/2303.02411/) (2023)
- [On the stability, correctness and plausibility of visual explanation methods based on feature importance](https://mlsanity.com/paper/2311.12860/) (2023)
- [Hybrid Convolutional Neural Networks with Reliability Guarantee](https://mlsanity.com/paper/2405.05146/) (2024)
- [Towards Explainable Artificial Intelligence in Banking and Financial Services](https://mlsanity.com/paper/2112.08441/) (2021)
- [Explainability for identification of vulnerable groups in machine learning models](https://mlsanity.com/paper/2203.00317/) (2022)
- [EnTri: Ensemble Learning with Tri-level Representations for Explainable Scene Recognition](https://mlsanity.com/paper/2307.12442/) (2023)
- [Utilizing Explainability Techniques for Reinforcement Learning Model Assurance](https://mlsanity.com/paper/2311.15838/) (2023)
- [Review of Explainable Graph-Based Recommender Systems](https://mlsanity.com/paper/2408.00166/) (2024)
- [Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection](https://mlsanity.com/paper/2412.02084/) (2024)
- [Performance Evaluation of Sentiment Analysis on Text and Emoji Data Using End-to-End, Transfer Learning, Distributed and Explainable AI Models](https://mlsanity.com/paper/2502.13278/) (2025)
- [Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks](https://mlsanity.com/paper/2609.12305/) (2026)

## Abstract (arXiv)

The lack of explainability of a decision from an Artificial Intelligence (AI) based "black box" system/model, despite its superiority in many real-world applications, is a key stumbling block for adopting AI in many high stakes applications of different domain or industry. While many popular Explainable Artificial Intelligence (XAI) methods or approaches are available to facilitate a human-friendly explanation of the decision, each has its own merits and demerits, with a plethora of open challenges. We demonstrate popular XAI methods with a mutual case study/task (i.e., credit default prediction), analyze for competitive advantages from multiple perspectives (e.g., local, global), provide meaningful insight on quantifying explainability, and recommend paths towards responsible or human-centered AI using XAI as a medium. Practitioners can use this work as a catalog to understand, compare, and correlate competitive advantages of popular XAI methods. In addition, this survey elicits future research directions towards responsible or human-centric AI systems, which is crucial to adopt AI in high stakes applications.