(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Clothing-change person re-identification (CC-ReID) poses a formidable challenge for surveillance systems, as individuals deliberately or incidentally alter their appearance, rendering traditional appearance-based matching unreliable. While deep learning models have achieved considerable accuracy in controlled benchmarks, their opacity undermines trust, accountability, and operational safety in high-stakes deployments such as law enforcement and border control. This paper presents a system-level investigation into explainable artificial intelligence (XAI) for CC-ReID through the lens of semantic feature attribution. We argue that the path toward trustworthy CC-ReID lies not in isolated algorithmic post-hoc explanation but in architecting inherently interpretable attribution pipelines that expose how models associate identity across clothing boundaries. The paper dissects the structural trade-offs among global and local explanation modalities, the integration of attention and part-based feature decoupling as attribution sources, and the infrastructure requirements for real-time explainability at scale. A comprehensive analysis is provided of how semantic attribution surfaces robustness vulnerabilities, enables fairness audits across demographic subgroups, and informs governance frameworks for biometric surveillance. We further examine the deployment continuum from centralized cloud analytics to privacy-preserving edge inference, discussing energy sustainability and the tension between model complexity and explanatory fidelity. By synthesizing perspectives from computer vision, systems engineering, and regulatory policy, this work offers a blueprint for next-generation CC-ReID platforms where explanation is not an add-on but a fundamental design constraint. The paper concludes with an agenda for open benchmarking, standardized attribution validation, and cross-disciplinary collaboration to align technical capability with societal values.