# How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins

arXiv: https://arxiv.org/abs/1905.07186 · HTML: https://mlsanity.com/paper/1905.07186/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 20 of 20)

- [Explaining Latent Representations with a Corpus of Examples](https://mlsanity.com/paper/2110.15355/) (2021)
- [XAIR: A Framework of Explainable AI in Augmented Reality](https://mlsanity.com/paper/2303.16292/) (2023)
- [Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ](https://mlsanity.com/paper/2212.08733/) (2022)
- [Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching](https://mlsanity.com/paper/2102.03919/) (2021)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples](https://mlsanity.com/paper/2310.07747/) (2023)
- [Robust Text Classification: Analyzing Prototype-Based Networks](https://mlsanity.com/paper/2311.06647/) (2023)
- [Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences](https://mlsanity.com/paper/2410.19856/) (2024)
- [CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models](https://mlsanity.com/paper/2404.14830/) (2024)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- [Case-Based Inverse Reinforcement Learning Using Temporal Coherence](https://mlsanity.com/paper/2206.05827/) (2022)
- [Wrapper Boxes: Faithful Attribution of Model Predictions to Training Data](https://mlsanity.com/paper/2311.08644/) (2023)
- [Explainable deep learning improves human mental models of self-driving cars](https://mlsanity.com/paper/2411.18714/) (2024)
- [Informed Machine Learning for Improved Similarity Assessment in Process-Oriented Case-Based Reasoning](https://mlsanity.com/paper/2106.15931/) (2021)
- [Interpretable ML for Imbalanced Data](https://mlsanity.com/paper/2212.07743/) (2022)
- [What's meant by explainable model: A Scoping Review](https://mlsanity.com/paper/2307.09673/) (2023)
- [Model-agnostic Body Part Relevance Assessment for Pedestrian Detection](https://mlsanity.com/paper/2311.15679/) (2023)
- [Using Part-based Representations for Explainable Deep Reinforcement Learning](https://mlsanity.com/paper/2408.11455/) (2024)
- [Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification](https://mlsanity.com/paper/2507.09826/) (2025)
- [Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark](https://mlsanity.com/paper/2609.35226/) (2026)

## Abstract (arXiv)

This paper surveys an approach to the XAI problem, using post-hoc explanation by example, that hinges on twinning Artificial Neural Networks (ANNs) with Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins. A systematic survey of 1100+ papers was carried out to identify the fragmented literature on this topic and to trace it influence through to more recent work involving Deep Neural Networks (DNNs). The paper argues that this twin-system approach, especially using ANN-CBR twins, presents one possible coherent, generic solution to the XAI problem (and, indeed, XCBR problem). The paper concludes by road-mapping some future directions for this XAI solution involving (i) further tests of feature-weighting techniques, (iii) explorations of how explanatory cases might best be deployed (e.g., in counterfactuals, near-miss cases, a fortori cases), and (iii) the raising of the unwelcome and, much ignored, issue of human user evaluation.