UGCE: User-Guided Incremental Counterfactual Exploration

-- Counterfactual explanations (CFEs) are a popular approach for interpreting machine learning predictions by identifying minimal feature changes that alter model outputs. However, in real-world settings, users often refine feasibility constraints over time, requiring counterfactual generation to adapt dynamically. Existing methods fail to support such iterative updates, instead recomputing explanations from scratch with each change, an inefficient and rigid approach. We propose User-Guided Incremental Counterfactual Exploration (UGCE), a genetic algorithm-based framework that incrementally updates counterfactuals in response to evolving user constraints. Experimental results across five benchmark datasets demonstrate that UGCE significantly improves computational efficiency while maintaining high-quality solutions compared to a static, non-incremental approach. Our evaluation further shows that UGCE supports stable performance under varying constraint sequences, benefits from an efficient warm-start strategy, and reveals how different constraint types may affect search behavior . I NTRODUCTION Machine learning (ML) models are increasingly deployed in high-stakes decision-making domains, including lending, college admissions, and hiring, where their predictions influence critical life outcomes [1]-[3]. However, these models often function as black boxes, making it difficult for stakeholders to understand the rationale behind predictions, particularly when an unfavorable decision is made. This lack of transparency has driven the need for explanation techniques that help users interpret and contest automated decisions [4], [5]. As a result, research on explainability has gained significant traction, leading to a wide array of methodologies aimed at making ML models more transparent and interpretable [5]-[13].