ADAPTIVE EVIDENCE-DRIVEN DATA INTELLIGENCE: A SELF-VALIDATING FRAMEWORK FOR RELIABLE AI DECISION-MAKING UNDER CONTINUOUS DATA DRIFT

Abstract

Machine learning systems are trained on a snapshot of the world but deployed into a world that keeps changing. When the data that a model sees in production departs from its training distribution, predictions can degrade silently, and decisions that rely on them become unreliable. This paper proposes an adaptive, evidence-driven and self-validating framework for maintaining reliable AI decision-making under continuous data drift. The framework combines a multimodal representation layer, a statistical evidence layer that quantifies drift and uncertainty, a validation gate that converts evidence into trust states, an adaptive response layer that recalibrates, retrains or defers to human judgement, and an audit ledger that records why each action was taken. Using three illustrative figures, we show how multimodal contrastive alignment feeds anomaly detection, compliance validation and risk scoring; how the statistical significance of a drift test depends jointly on shift magnitude and sample size; and how the same synthetic shift can improve performance at one hospital while degrading it at another, with large class-level variation hidden behind stable aggregate metrics. We derive design rules for monitoring, response policy and governance, and we discuss limitations and research directions.