# On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

arXiv: https://arxiv.org/abs/2009.06399 · HTML: https://mlsanity.com/paper/2009.06399/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 30 of 60)

- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms](https://mlsanity.com/paper/2108.00783/) (2021)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Diffusion Models for Counterfactual Explanations](https://mlsanity.com/paper/2203.15636/) (2022)
- [Learning Support and Trivial Prototypes for Interpretable Image Classification](https://mlsanity.com/paper/2301.04011/) (2023)
- [Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis](https://mlsanity.com/paper/2106.15231/) (2021)
- [A Rationale-Centric Framework for Human-in-the-loop Machine Learning](https://mlsanity.com/paper/2203.12918/) (2022)
- [Formalising the Robustness of Counterfactual Explanations for Neural Networks](https://mlsanity.com/paper/2208.14878/) (2022)
- [Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI](https://mlsanity.com/paper/2301.11970/) (2023)
- [Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions](https://mlsanity.com/paper/2107.09734/) (2021)
- [Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI](https://mlsanity.com/paper/2204.10152/) (2022)
- [Natural Example-Based Explainability: a Survey](https://mlsanity.com/paper/2309.03234/) (2023)
- [Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ](https://mlsanity.com/paper/2212.08733/) (2022)
- ["Even if ..." -- Diverse Semifactual Explanations of Reject](https://mlsanity.com/paper/2207.01898/) (2022)
- [Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition](https://mlsanity.com/paper/2312.00092/) (2023)
- [Identifying Spurious Correlations and Correcting them with an Explanation-based Learning](https://mlsanity.com/paper/2211.08285/) (2022)
- [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://mlsanity.com/paper/2302.14165/) (2023)
- [Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals](https://mlsanity.com/paper/2303.09297/) (2023)
- [Interpreting Language Reward Models via Contrastive Explanations](https://mlsanity.com/paper/2411.16502/) (2024)
- [This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy](https://mlsanity.com/paper/2212.02506/) (2022)
- [The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes](https://mlsanity.com/paper/2310.18937/) (2023)
- [Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems](https://mlsanity.com/paper/2207.09374/) (2022)
- [DeDUCE: Generating Counterfactual Explanations Efficiently](https://mlsanity.com/paper/2111.15639/) (2021)
- [Interpretable Regional Descriptors: Hyperbox-Based Local Explanations](https://mlsanity.com/paper/2305.02780/) (2023)
- [Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found Using Counterfactuals As Guides?](https://mlsanity.com/paper/2403.00980/) (2024)
- [A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers](https://mlsanity.com/paper/2411.02643/) (2024)
- [Even-if Explanations: Formal Foundations, Priorities and Complexity](https://mlsanity.com/paper/2401.10938/) (2024)
- [Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation](https://mlsanity.com/paper/2104.14461/) (2021)
- [Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers](https://mlsanity.com/paper/2405.05295/) (2024)
- [Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features](https://mlsanity.com/paper/2107.07095/) (2021)

## Abstract (arXiv)

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.