PeCE: A Visual Analytics Paradigm for Enhancing Counterfactual Explanations

Area of Science:

• Machine Learning
• Explainable AI (XAI)
• Data Visualization

Background:

• Counterfactual explanations offer intuitive insights into machine learning model predictions.
• Current methods face challenges with feature redundancy, unclear explanation quality, and fragmented information, hindering coherent decision-making.

Purpose of the Study:

• To propose a novel analysis paradigm that reframes counterfactual explanations as an auditable and iterative investigative reasoning workflow.
• To enhance the clarity, credibility, and practical utility of counterfactual explanations in machine learning.

Main Methods:

• A three-step paradigm focusing on causal features via Minimal Feature Boundary (MFB) and interactive constraint modeling.
• Introduction of multi-dimensional quality audit views with metrics like Counterfactual Equilibrated Quality Score (CEQS) for evidence-based filtering.
• Implementation of a visualization-driven hypothesis testing loop for dynamic validation and refinement of model understanding.

Main Results:

• The proposed paradigm, instantiated in the PeCE system, improves transparency and traceability in counterfactual explanation processes.
• Case studies and expert interviews demonstrate the effectiveness of the new workflow.
• Counterfactual explanations become more credible and practical as diagnostic tools.

Conclusions:

• The novel paradigm transforms counterfactual explanations into a structured, auditable investigative process.
• This enhances user trust and the practical application of explainable AI techniques.
• The approach facilitates a deeper, more reliable understanding of machine learning model behavior.