# Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach

arXiv: https://arxiv.org/abs/2106.08064 · HTML: https://mlsanity.com/paper/2106.08064/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives](https://mlsanity.com/paper/1802.07623/) (2018)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations](https://mlsanity.com/paper/1702.07826/) (2017)
- [Beneficial and Harmful Explanatory Machine Learning](https://mlsanity.com/paper/2009.06410/) (2020)
- [RelNN: A Deep Neural Model for Relational Learning](https://mlsanity.com/paper/1712.02831/) (2017)

## Built on by (most cited first, 8 of 8)

- [Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI](https://mlsanity.com/paper/2301.11970/) (2023)
- [CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations](https://mlsanity.com/paper/2406.01649/) (2024)
- [An Interactive Explanatory AI System for Industrial Quality Control](https://mlsanity.com/paper/2203.09181/) (2022)
- [Explaining Hate Speech Classification with Model Agnostic Methods](https://mlsanity.com/paper/2306.00021/) (2023)
- [Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust](https://mlsanity.com/paper/2308.14163/) (2023)
- [When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX](https://mlsanity.com/paper/2405.01661/) (2024)
- [Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance](https://mlsanity.com/paper/2506.23975/) (2025)
- [The Dual Role of Abstracting over the Irrelevant in Symbolic Explanations: Cognitive Effort vs. Understanding](https://mlsanity.com/paper/2602.03467/) (2026)

## Abstract (arXiv)

In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or visualizations. However, as shown in cognitive science as well as in early AI research, concept understanding can also be improved by the alignment of a given instance for a concept with a similar counterexample. Contrasting a given instance with a structurally similar example which does not belong to the concept highlights what characteristics are necessary for concept membership. Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains. We introduce an explanation generation algorithm for relational concepts learned with Inductive Logic Programming (textscGeNME). The algorithm identifies near miss examples from a given set of instances and ranks these examples by their degree of closeness to a specific positive instance. A modified rule which covers the near miss but not the original instance is given as an explanation. We illustrate textscGeNME with the well known family domain consisting of kinship relations, the visual relational Winston arches domain and a real-world domain dealing with file management. We also present a psychological experiment comparing human preferences of rule-based, example-based, and near miss explanations in the family and the arches domains.