2024/12/05 by Md. Ariful Islam, Islam, Md. Ariful, Md Abrar Jahin +5 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2412.03884
openalex publication_date 2024/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Explainable Artificial Intelligence (XAI) methods are increasingly used in safety-critical domains, yet there is no unified framework to jointly evaluate fidelity, interpretability, robustness, fairness, and completeness. We address this gap through two contributions. First, we propose a multi-criteria evaluation framework that formalizes these five criteria using principled metrics: fidelity via prediction-gap analysis; interpretability via a composite concentration-coherence-contrast score; robustness via cosine-similarity perturbation stability; fairness via Jensen-Shannon divergence across demographic groups; and completeness via feature-ablation coverage. These are integrated using an entropy-weighted dynamic scoring scheme that adapts to domain-specific priorities. Second, we introduce Perturbation-Gradient Consensus Attribution (PGCA), which fuses grid-based perturbation importance with Grad-CAM++ through consensus amplification and adaptive contrast enhancement, combining perturbation fidelity with gradient-based spatial precision. We evaluate across five domains (brain tumor MRI, plant disease, security screening, gender, and sunglass detection) using fine-tuned ResNet-50 models. PGCA achieves the best performance in fidelity (2.22 ± 1.62), interpretability (3.89 ± 0.33), and fairness (4.95 ± 0.03), with statistically significant improvements over baselines (p < 10-7). Sensitivity analysis shows stable rankings (Kendall's (τ≥ 0.88)). Code and results are publicly available.