mlsanityData to 13 Oct 2026

Explainability in Human-Agent Systems

arXiv 1904.08123 ↗First posted 17 Apr 2019cs.AIcited by 34

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Abstract (arXiv)

This paper presents a taxonomy of explainability in Human-Agent Systems. We consider fundamental questions about the Why, Who, What, When and How of explainability. First, we define explainability, and its relationship to the related terms of interpretability, transparency, explicitness, and faithfulness. These definitions allow us to answer why explainability is needed in the system, whom it is geared to and what explanations can be generated to meet this need. We then consider when the user should be presented with this information. Last, we consider how objective and subjective measures can be used to evaluate the entire system. This last question is the most encompassing as it will need to evaluate all other issues regarding explainability.

Key facts. Cited by 34 papers in the map (data through 13 Oct 2026). First posted on arXiv 17 Apr 2019.

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Citing papers per year: 2021: 10; 2022: 5; 2023: 5; 2024: 8; 2025: 5; 2026: 1.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  3. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  4. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  5. 2021Argumentative XAI: A Surveycited by 66
  6. 2025TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systemscited by 59
  7. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  8. 2021Question-Driven Design Process for Explainable AI User Experiencescited by 31
  9. 2024Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectivescited by 30
  10. 2022Explainable Intrusion Detection Systems (X-IDS): A Survey of Current Methods, Challenges, and Opportunitiescited by 25
  11. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  12. 2021Explainable Autonomous Robots: A Survey and Perspectivecited by 15
22 more
  1. 2021Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligencecited by 13
  2. 2023Ethical Framework for Harnessing the Power of AI in Healthcare and Beyondcited by 6
  3. 2024What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blockscited by 6
  4. 2023A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)cited by 5
  5. 2022Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfactioncited by 4
  6. 2021A Hybrid Approach for an Interpretable and Explainable Intrusion Detection Systemcited by 3
  7. 2024Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Educationcited by 2
  8. 2024Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Modelscited by 2
  9. 2024Explainable Interface for Human-Autonomy Teaming: A Surveycited by 2
  10. 2025Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedbackcited by 2
  11. 2025Not All Explanations are Created Equal: Investigating the Pitfalls of Current XAI Evaluationcited by 2
  12. 2022An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Modelcited by 1
  13. 2022Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collectioncited by 1
  14. 2024Structure and Reduction of MCTS for Explainable-AIcited by 1
  15. 2024Semifactual Explanations for Reinforcement Learningcited by 1
  16. 2025Towards LLM-generated explanations for Component-based Knowledge Graph Question Answering Systemscited by 1
  17. 2021Local Explanations for Clinical Search Engine resultscited by 0
  18. 2022Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarioscited by 0
  19. 2023Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learningcited by 0
  20. 2024Explaining Explainingcited by 0
  21. 2025Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanationscited by 0
  22. 2026Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?cited by 0