# Towards Analogy-Based Explanations in Machine Learning

arXiv: https://arxiv.org/abs/2005.12800 · HTML: https://mlsanity.com/paper/2005.12800/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2020.

## Built on by (most cited first, 7 of 7)

- [BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?](https://mlsanity.com/paper/2105.04949/) (2021)
- [Galois theory for analogical classifiers](https://mlsanity.com/paper/2205.04593/) (2022)
- [Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learners](https://mlsanity.com/paper/2202.01153/) (2022)
- [Any four real numbers are on all fours with analogy](https://mlsanity.com/paper/2407.18770/) (2024)
- [Generalizing Analogical Inference from Boolean to Continuous Domains](https://mlsanity.com/paper/2511.10416/) (2025)
- [When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making](https://mlsanity.com/paper/2602.04003/) (2026)
- [Some recent advances in reasoning based on analogical proportions](https://mlsanity.com/paper/2212.11717/) (2022)

## Abstract (arXiv)

Principles of analogical reasoning have recently been applied in the context of machine learning, for example to develop new methods for classification and preference learning. In this paper, we argue that, while analogical reasoning is certainly useful for constructing new learning algorithms with high predictive accuracy, is is arguably not less interesting from an interpretability and explainability point of view. More specifically, we take the view that an analogy-based approach is a viable alternative to existing approaches in the realm of explainable AI and interpretable machine learning, and that analogy-based explanations of the predictions produced by a machine learning algorithm can complement similarity-based explanations in a meaningful way. To corroborate these claims, we outline the basic idea of an analogy-based explanation and illustrate its potential usefulness by means of some examples.