# A Novel Multi-Objective Evolutionary Algorithm for Counterfactual Generation

arXiv: https://arxiv.org/abs/2502.10418 · HTML: https://mlsanity.com/paper/2502.10418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Actionable Recourse in Linear Classification](https://mlsanity.com/paper/1809.06514/) (2018)
- [Multi-Objective Counterfactual Explanations](https://mlsanity.com/paper/2004.11165/) (2020)
- [Efficient Search for Diverse Coherent Explanations](https://mlsanity.com/paper/1901.04909/) (2019)
- [GeCo: Quality Counterfactual Explanations in Real Time](https://mlsanity.com/paper/2101.01292/) (2021)
- [Monotonic classification: an overview on algorithms, performance measures and data sets](https://mlsanity.com/paper/1811.07155/) (2018)

## Abstract (arXiv)

Machine learning algorithms that learn black-box predictive models (which cannot be directly interpreted) are increasingly used to make predictions affecting the lives of people. It is important that users understand the predictions of such models, particularly when the model outputs a negative prediction for the user (e.g. denying a loan). Counterfactual explanations provide users with guidance on how to change some of their characteristics to receive a different, positive classification by a predictive model. For example, if a predictive model rejected a loan application from a user, a counterfactual explanation might state: If your salary was pounds50,000 (rather than your current pounds35,000), then your loan would be approved. This paper proposes two novel contributions: (a) a novel multi-objective Evolutionary Algorithm (EA) for counterfactual generation based on lexicographic optimisation, rather than the more popular Pareto dominance approach; and (b) an extension to the definition of the objective of validity for a counterfactual, based on measuring the resilience of a counterfactual to violations of monotonicity constraints which are intuitively expected by users; e.g., intuitively, the probability of a loan application to be approved would monotonically increase with an increase in the salary of the applicant. Experiments involving 15 experimental settings (3 types of black box models times 5 datasets) have shown that the proposed lexicographic optimisation-based EA is very competitive with an existing Pareto dominance-based EA; and the proposed extension of the validity objective has led to a substantial increase in the validity of the counterfactuals generated by the proposed EA.