DiCoFlex: Model-Agnostic Diverse Counterfactuals with Flexible Control

Oleksii Furman, Ulvi Movsum-zada, Patryk Marszałek, Maciej Zieba, Marek Śmieja

Keywords: Explainable AI, Counterfactual Explanations, Tabular Data, Normalizing Flows

TL;DR: DiCoFlex generates diverse, customizable counterfactual explanations in a single forward pass without requiring model access, enabling real-time constraint adjustments

Abstract: Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learning model decisions. Despite their significance, existing methods for generating counterfactuals often require constant access to the predictive model, involve computationally intensive optimization for each instance, and lack the flexibility to adapt to new user-defined constraints without retraining. In this paper, we propose DiCoFlex, a novel model-agnostic, conditional generative framework that produces multiple diverse counterfactuals in a single forward pass. Leveraging conditional normalizing flows trained solely on labeled data, DiCoFlex addresses key limitations by enabling real-time, user-driven customization of constraints such as sparsity and actionability at inference time. Extensive experiments on standard benchmark datasets show that DiCoFlex outperforms existing methods in terms of validity, diversity, proximity, and constraint adherence, making it a practical and scalable solution for counterfactual generation in sensitive decision-making domains.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 12804