Adaptive Human-Centric Representation Learning for Robust and Fair Identity Systems under Distribution Shift

Human-centric identity systems deployed in real-world environments continuously encounter non-stationary conditions, such as changing illumination, background noise, device transitions, and physiological aging. These environmental and temporal variations introduce distribution shift, degrading system performance, elevating false match rates, and disproportionately amplifying demographic bias. Existing biometric architectures typically treat drift, demographic fairness, explainability, and multimodal fusion as isolated problems, lacking real-time, unified recalibration mechanisms. This paper presents a novel, real-time trustworthy artificial intelligence (AI) framework designed for multimodal human-centric identity systems. The architecture integrates four core operational layers: (1) a real-time multimodal distribution shift detection mechanism combining online change-point detection with latent embedding divergence metrics; (2) a dynamic fairness monitoring and recalibration module triggered by shift signals; (3) a temporal explainability framework introducing the Temporal Explanation Stability Index (TESI) to evaluate interpretability robustness over time; and (4) a drift-aware reliability-weighted multimodal fusion engine. This unified framework provides continuous monitoring, automated bias mitigation, and stable interpretability, establishing a principled pipeline for trustworthy identity verification in dynamic real-world environments.