Misaligning Stories: Narrative Unreliability, Double Noise, and Feedback Ethics in Clinical AI

A worker with cancer is dismissed after algorithmic dashboards misread her treatment fatigue as cognitive decline.A stroke prediction model achieves 95 per cent 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, 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. Plain Language SummaryWhen a cancer patient is dismissed because an algorithm misread her fatigue as cognitive decline, the problem is not only technical-it is communicative.This article examines how AI systems in medicine and work can misinterpret human behavior, and why feedback is the missing safeguard.This article introduces the idea of double noise: when human judgment, which is variable, and algorithmic judgment, which is often simplified, overlap and reinforce each other.The result can be unfair or unsafe outcomes.Drawing on narrative theory, the article also shows how these systems do not merely misread people-they actively construct false stories by imposing coherence on incomplete signals, stories that then resist correction because no feedback channel exists to revise them.The analysis draws on two case studies.The first is a fictional story about Leo, a worker whose illness led to short-term changes in her digital activity.Corporate dashboards misread these temporary signals as lasting decline, and her employer dismissed her without asking for context.Though fictional, this scenario is increasingly plausible in workplaces governed by algorithmic monitoring.The second examines a synthetic dataset on stroke prediction, designed for teaching.It illustrates how routine design choices-such as class balancing or metric selection-can obscure critical errors, especially false negatives.Both cases show how, without feedback, small distortions harden into entrenched misjudgments.The article argues for systems designed to keep interpretive channels open: where professionals can question algorithmic outputs and individuals can challenge decisions that affect their health, work, or dignity.The framework developed here is intended as a transferable analytical tool-applicable wherever AI systems configure heterogeneous signals into consequential stories, suppressing the feedback that would allow those stories to be told differently.