A worker with cancer is dismissed after algorithmic dashboards misread her treatment fatigue as cognitive decline. A stroke prediction model achieves 95 percent accuracy while missing every single stroke case. These are not edge cases – they are structural failures, produced when algorithmic distortions intersect with the cognitive variability of human judgment in the absence of feedback. This article calls that convergence double noise. Drawing on Shannon–Weaver’s communication model, Kahneman and colleagues’ concept of noise, and narratological theory – unreliable narration, focalization, paralepsis, emplotment, and situatedness – the article argues that predictive failures are not only statistical but narrative: computational systems operating under epistemic constraint produce false stories that resist correction. In both cases, the absence of meaningful feedback channels turns local distortions into entrenched misjudgments: the first through algorithmic dashboards that freeze discontinuous behavioral signals into an irrevocable story of cognitive decline; the second through routine design decisions – class balancing, metric selection, and threshold setting – that normalize the erasure of clinically decisive false negatives. By integrating narratological theory with computational methods and epistemic critique, the article positions double noise as a central challenge for clinical AI and advances feedback ethics as a normative orientation, calling for systems that preserve ambiguity, enable contestation, and institutionalize shared judgment in high-risk environments.