mlsanityData to 7 Oct 2026

Explainable AI: current status and future directions

arXiv 2107.07045 ↗First posted 12 Jul 2021cs.LG cs.AIcited by 32

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Abstract (arXiv)

Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 37 of its 81 references that are in the map. First posted on arXiv 12 Jul 2021.

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37 of 81 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  6. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  7. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  9. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
  10. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  11. 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
  12. 2019Is Attention Interpretable?cited by 387
25 more
  1. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
  2. 2017A Structured Self-attentive Sentence Embeddingcited by 318
  3. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
  4. 2018Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negativescited by 261
  5. 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
  6. 2015Explaining NonLinear Classification Decisions with Deep Taylor Decompositioncited by 248
  7. 2015Visualizing and Understanding Neural Models in NLPcited by 199
  8. 2020Explainable deep learning models in medical image analysiscited by 111
  9. 2020Principles and Practice of Explainable Machine Learningcited by 99
  10. 2017Explaining Recurrent Neural Network Predictions in Sentiment Analysiscited by 87
  11. 2018Spread of hate speech in online social mediacited by 59
  12. 2018An Interpretable Reasoning Network for Multi-Relation Question Answeringcited by 47
  13. 2018A Unified Deep Learning Architecture for Abuse Detectioncited by 37
  14. 2017Mean Birds: Detecting Aggression and Bullying on Twittercited by 29
  15. 2019Human-grounded Evaluations of Explanation Methods for Text Classificationcited by 28
  16. 2019Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classificationcited by 25
  17. 2017An Interpretable Knowledge Transfer Model for Knowledge Base Completioncited by 17
  18. 2019CNM: An Interpretable Complex-valued Network for Matchingcited by 13
  19. 2020Explaining Motion Relevance for Activity Recognition in Video Deep Learning Modelscited by 7
  20. 2021An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scanscited by 7
  21. 2018Iterative Recursive Attention Model for Interpretable Sequence Classificationcited by 3
  22. 2018Analyzing and Interpreting Convolutional Neural Networks in NLPcited by 2
  23. 2018Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robotscited by 1
  24. 2019Train, Sort, Explain: Learning to Diagnose Translation Modelscited by 1
  25. 2020The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learningcited by 1

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Citing papers per year: 2022: 5; 2023: 9; 2024: 10; 2025: 8.

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  1. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  2. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  3. 2025FLEX: Continuous Agent Evolution via Forward Learning from Experiencecited by 51
  4. 2024ESCoT: Towards Interpretable Emotional Support Dialogue Systemscited by 46
  5. 2024AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learningcited by 40
  6. 2023Tell Me a Story! Narrative-Driven XAI with Large Language Modelscited by 20
  7. 2025Representation Engineering for Large-Language Models: Survey and Research Challengescited by 18
  8. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  9. 2024View-based Explanations for Graph Neural Networkscited by 8
  10. 2023Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Modelscited by 7
  11. 2024Towards a Framework for Evaluating Explanations in Automated Fact Verificationcited by 7
  12. 2024Word Importance Explains How Prompts Affect Language Model Outputscited by 6
20 more
  1. 2023Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Designcited by 5
  2. 2024Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challengescited by 5
  3. 2022Explanation-by-Example Based on Item Response Theorycited by 4
  4. 2022LUCID: Exposing Algorithmic Bias through Inverse Designcited by 2
  5. 2023A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopycited by 2
  6. 2025FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigationscited by 2
  7. 2023Explaining Search Result Stances to Opinionated Peoplecited by 1
  8. 2023Unveiling The Factors of Aesthetic Preferences with Explainable AIcited by 1
  9. 2023SAIBench: A Structural Interpretation of AI for Science Through Benchmarkscited by 1
  10. 2024An Early-Stage Workflow Proposal for the Generation of Safe and Dependable AI Classifierscited by 1
  11. 2025SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Managementcited by 1
  12. 2022Human-centered XAI for Burn Depth Characterizationcited by 0
  13. 2022Implicit Mixture of Interpretable Experts for Global and Local Interpretabilitycited by 0
  14. 2024Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligencecited by 0
  15. 2024The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Reviewcited by 0
  16. 2024Trustworthy XAI and Applicationcited by 0
  17. 2025AI Enabled User-Specific Cyberbullying Severity Detection with Explainabilitycited by 0
  18. 2025AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to Howcited by 0
  19. 2025Interpretability-Aware Pruning for Efficient Medical Image Analysiscited by 0
  20. 2025Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Promptscited by 0