mlsanityData to 7 Oct 2026

TED: Teaching AI to Explain its Decisions

arXiv 1811.04896 ↗First posted 12 Nov 2018cs.AIcited by 33

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Abstract (arXiv)

Artificial intelligence systems are being increasingly deployed due to their potential to increase the efficiency, scale, consistency, fairness, and accuracy of decisions. However, as many of these systems are opaque in their operation, there is a growing demand for such systems to provide explanations for their decisions. Conventional approaches to this problem attempt to expose or discover the inner workings of a machine learning model with the hope that the resulting explanations will be meaningful to the consumer. In contrast, this paper suggests a new approach to this problem. It introduces a simple, practical framework, called Teaching Explanations for Decisions (TED), that provides meaningful explanations that match the mental model of the consumer. We illustrate the generality and effectiveness of this approach with two different examples, resulting in highly accurate explanations with no loss of prediction accuracy for these two examples.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Nov 2018.

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Citing papers per year: 2021: 13; 2022: 6; 2023: 6; 2024: 4; 2025: 3; 2026: 1.

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  1. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  2. 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
  3. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  4. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  5. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  6. 2021A Review on Explainability in Multimodal Deep Neural Netscited by 46
  7. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  8. 2022Towards Prototype-Based Self-Explainable Graph Neural Networkcited by 14
  9. 2023Are Data-driven Explanations Robust against Out-of-distribution Data?cited by 14
  10. 2024DEAL: Disentangle and Localize Concept-level Explanations for VLMscited by 14
  11. 2023Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Frameworkcited by 11
  12. 2021AI Explainability 360: Impact and Designcited by 8
21 more
  1. 2021Learning to Predict with Supporting Evidence: Applications to Clinical Risk Predictioncited by 7
  2. 2022Study of Feature Importance for Quantum Machine Learning Modelscited by 7
  3. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  4. 2023Strategies to exploit XAI to improve classification systemscited by 7
  5. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  6. 2022Explanatory machine learning for sequential human teachingcited by 5
  7. 2021Image Classification with Consistent Supporting Evidencecited by 3
  8. 2022Explanatory Learning: Beyond Empiricism in Neural Networkscited by 3
  9. 2022Socially Intelligent Genetic Agents for the Emergence of Explicit Normscited by 3
  10. 2025CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractionscited by 3
  11. 2021Designer-User Communication for XAI: An epistemological approach to discuss XAI designcited by 2
  12. 2021Explainability-aided Domain Generalization for Image Classificationcited by 1
  13. 2023ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Modelscited by 1
  14. 2024Challenging the Human-in-the-loop in Algorithmic Decision-makingcited by 1
  15. 2024Value-Based Rationales Improve Social Experience: A Multiagent Simulation Studycited by 1
  16. 2025Explainable AI the Latest Advancements and New Trendscited by 1
  17. 2021Harnessing value from data science in business: ensuring explainability and fairness of solutionscited by 0
  18. 2023Impact of Feature Encoding on Malware Classification Explainabilitycited by 0
  19. 2024Explainable AI: Definition and attributes of a good explanation for health AIcited by 0
  20. 2025Augmenting Intelligence: A Hybrid Framework for Scalable and Stable Explanationscited by 0
  21. 2026Knowledge Cards: Structured Knowledge for AI Systemscited by 0