# TED: Teaching AI to Explain its Decisions

arXiv: https://arxiv.org/abs/1811.04896 · HTML: https://mlsanity.com/paper/1811.04896/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Human-Centered Explainable AI (XAI): From Algorithms to User Experiences](https://mlsanity.com/paper/2110.10790/) (2021)
- [What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research](https://mlsanity.com/paper/2102.07817/) (2021)
- [Towards Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2108.12055/) (2021)
- [A Review on Explainability in Multimodal Deep Neural Nets](https://mlsanity.com/paper/2105.07878/) (2021)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [Towards Prototype-Based Self-Explainable Graph Neural Network](https://mlsanity.com/paper/2210.01974/) (2022)
- [Are Data-driven Explanations Robust against Out-of-distribution Data?](https://mlsanity.com/paper/2303.16390/) (2023)
- [DEAL: Disentangle and Localize Concept-level Explanations for VLMs](https://mlsanity.com/paper/2407.14412/) (2024)
- [Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework](https://mlsanity.com/paper/2311.11055/) (2023)
- [AI Explainability 360: Impact and Design](https://mlsanity.com/paper/2109.12151/) (2021)
- [Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction](https://mlsanity.com/paper/2103.02768/) (2021)
- [Study of Feature Importance for Quantum Machine Learning Models](https://mlsanity.com/paper/2202.11204/) (2022)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Strategies to exploit XAI to improve classification systems](https://mlsanity.com/paper/2306.05801/) (2023)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [Explanatory machine learning for sequential human teaching](https://mlsanity.com/paper/2205.10250/) (2022)
- [Image Classification with Consistent Supporting Evidence](https://mlsanity.com/paper/2111.07048/) (2021)
- [Explanatory Learning: Beyond Empiricism in Neural Networks](https://mlsanity.com/paper/2201.10222/) (2022)
- [Socially Intelligent Genetic Agents for the Emergence of Explicit Norms](https://mlsanity.com/paper/2208.03789/) (2022)
- [CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions](https://mlsanity.com/paper/2506.05586/) (2025)
- [Designer-User Communication for XAI: An epistemological approach to discuss XAI design](https://mlsanity.com/paper/2105.07804/) (2021)
- [Explainability-aided Domain Generalization for Image Classification](https://mlsanity.com/paper/2104.01742/) (2021)
- [ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Models](https://mlsanity.com/paper/2307.13815/) (2023)
- [Challenging the Human-in-the-loop in Algorithmic Decision-making](https://mlsanity.com/paper/2405.10706/) (2024)
- [Value-Based Rationales Improve Social Experience: A Multiagent Simulation Study](https://mlsanity.com/paper/2408.02117/) (2024)
- [Explainable AI the Latest Advancements and New Trends](https://mlsanity.com/paper/2505.07005/) (2025)
- [Harnessing value from data science in business: ensuring explainability and fairness of solutions](https://mlsanity.com/paper/2108.07714/) (2021)
- [Impact of Feature Encoding on Malware Classification Explainability](https://mlsanity.com/paper/2307.05614/) (2023)

## 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.