A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution

[2412.03884] A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2412.03884 (cs) [Submitted on 5 Dec 2024 ( v1 ), last revised 8 Apr 2026 (this version, v3)] Title: A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution Authors: Md. Ariful Islam , Md Abrar Jahin , M. F. Mridha , Nilanjan Dey View a PDF of the paper titled A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution, by Md. Ariful Islam and 3 other authors View PDF HTML (experimental) Abstract: 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 $(2.22 \pm 1.62)$, interpretability $(3.89 \pm 0.33)$, and fairness $(4.95 \pm 0.03)$, with statistically significant improvements over baselines $(p < 10^{-7})$. Sensitivity analysis shows stable rankings (Kendall's $(\tau \geq 0.88)$). Code and results are publicly available. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2412.03884 [cs.AI] (or arXiv:2412.03884v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2412.03884 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Md Abrar Jahin [ view email ] [v1] Thu, 5 Dec 2024 05:30:10 UTC (6,627 KB) [v2] Tue, 15 Jul 2025 17:10:45 UTC (6,828 KB) [v3] Wed, 8 Apr 2026 00:22:07 UTC (4,196 KB) Full-text links: Access Paper: View a PDF of the paper titled A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution, by Md. Ariful Islam and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2024-12 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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