mlsanityData to 7 Oct 2026

Metrics for Explainable AI: Challenges and Prospects

arXiv 1812.04608 ↗First posted 11 Dec 2018cs.AIcited by 257

Not on the map. Why?

Abstract (arXiv)

The question addressed in this paper is: If we present to a user an AI system that explains how it works, how do we know whether the explanation works and the user has achieved a pragmatic understanding of the AI? In other words, how do we know that an explanainable AI system (XAI) is any good? Our focus is on the key concepts of measurement. We discuss specific methods for evaluating: (1) the goodness of explanations, (2) whether users are satisfied by explanations, (3) how well users understand the AI systems, (4) how curiosity motivates the search for explanations, (5) whether the user's trust and reliance on the AI are appropriate, and finally, (6) how the human-XAI work system performs. The recommendations we present derive from our integration of extensive research literatures and our own psychometric evaluations.

Key facts. Cited by 257 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Dec 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 51; 2023: 58; 2024: 52; 2025: 44; 2026: 18.

257 papers.

Sort:

By area:

  1. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  2. 2021Trustworthy AI: From Principles to Practicescited by 206
  3. 2022Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyondcited by 171
  4. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  5. 2023Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directionscited by 123
  6. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  7. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 84
  8. 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
  9. 2021Explanations in Autonomous Driving: A Surveycited by 80
  10. 2023Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanationscited by 73
  11. 2023Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulationcited by 60
  12. 2022Designing for Responsible Trust in AI Systems: A Communication Perspectivecited by 48
38 more
  1. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  2. 2023Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systemscited by 40
  3. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  4. 2021AutoDS: Towards Human-Centered Automation of Data Sciencecited by 32
  5. 2022Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Makingcited by 28
  6. 2021Principles of Explanation in Human-AI Systemscited by 25
  7. 2022Scalable Interpretability via Polynomialscited by 25
  8. 2023Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Sciencecited by 25
  9. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  10. 2022Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainabilitycited by 21
  11. 2021How Much Automation Does a Data Scientist Want?cited by 20
  12. 2021CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Modelscited by 20
  13. 2022Machine Explanations and Human Understandingcited by 20
  14. 2023Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Lifecited by 19
  15. 2023Automated Cyber Defence: A Reviewcited by 19
  16. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  17. 2022On the Influence of Explainable AI on Automation Biascited by 18
  18. 2023Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorationscited by 18
  19. 2024From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?cited by 18
  20. 2023GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanationscited by 17
  21. 2023XAIR: A Framework of Explainable AI in Augmented Realitycited by 17
  22. 2021Contrastive Explanations of Plans Through Model Restrictionscited by 16
  23. 2022Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAIcited by 16
  24. 2022Attribution-based XAI Methods in Computer Vision: A Reviewcited by 16
  25. 2023Calibrated Explanations: with Uncertainty Information and Counterfactualscited by 16
  26. 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 15
  27. 2023The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifierscited by 15
  28. 2021Explainable Activity Recognition for Smart Home Systemscited by 14
  29. 2024EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurationscited by 14
  30. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  31. 2025Because we have LLMs, we Can and Should Pursue Agentic Interpretabilitycited by 14
  32. 2023Explaining Reinforcement Learning Agents Through Counterfactual Action Outcomescited by 13
  33. 2021Resisting Out-of-Distribution Data Problem in Perturbation of XAIcited by 12
  34. 2022Towards Involving End-users in Interactive Human-in-the-loop AI Fairnesscited by 12
  35. 2022Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differcited by 12
  36. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  37. 2021Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Programcited by 11
  38. 2022Toward Policy Explanations for Multi-Agent Reinforcement Learningcited by 11

50 most cited of 257 All 257 (JSON)