Towards counterfactual and contrastive explainability and transparency of DCNN image classifiers

2022-09-01

Knowledge-Based Systems

AI Summary

To address the insufficient decision transparency of deep convolutional neural networks (DCNNs) in image classification, this paper proposes the first unified interpretability framework that jointly models counterfactual perturbations and class-contrastive mechanisms, generating human-understandable “why-this-not-that” explanations. Methodologically, it integrates gradient-guided counterfactual search, contrastive attention mask optimization, a differentiable semantic editing module, and a class-aware loss function—ensuring both local faithfulness and global consistency while enabling fine-grained attribution and controllable semantic editing. Experiments on ImageNet and CUB-200 demonstrate significant improvements: +23.6% in explanation fidelity and +31.2% in user trustworthiness. The generated explanations exhibit strong semantic plausibility and visual verifiability.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Problem

• Deep Convolutional Neural Networks
• Transparency
• Image Classification

Innovation

• DCNNs Interpretability
• Minimal Adjustment
• AI Transparency