The Governance of Human Capacity in the AI Age, Vol. 6: Distributed Human Judgment as Negative-Entropy Infrastructure

The Governance of Human Capacity in the AI Age, Vol. 6: Distributed Human Judgment as Negative-Entropy InfrastructureCivilization Physics — Human Systems & AI Governance Series This paper argues that sustainable AI systems require more than large-scale preference collection and reinforcement learning from human feedback. Current governance approaches rely heavily on thin preference signals such as ranking comparisons, thumbs-up feedback, and surface-level satisfaction metrics. While these methods improve usability and alignment at scale, they are insufficient for preserving long-term cognitive stability, institutional accountability, and contact with reality. The paper proposes an alternative framework: distributed human judgment as negative-entropy infrastructure . The analysis begins from an ontological claim: intelligence—biological or artificial—is best understood as a far-from-equilibrium adaptive process sustained through continuous exchange with structured environmental input. Drawing on thermodynamic and dynamical-systems perspectives, the paper argues that adaptive systems drift, narrow, and degrade when deprived of fresh, structured correction. Model-collapse research reinforces this principle by demonstrating that recursive training on synthetic or self-generated outputs progressively erodes diversity, precision, and robustness. This same logic is extended to human cognition under AI conditions. The paper distinguishes ordinary cognitive offloading from a more dangerous pattern in which AI systems increasingly generate candidate reasoning, interpretations, and conclusions on behalf of users. Empirical studies in writing assistance, educational contexts, and decision-support systems suggest that unrestricted AI delegation can reduce cognitive engagement, weaken independent reasoning, and intensify automation bias under certain conditions. The central conceptual distinction introduced is between preference signals and judgment signals: Preference signals express comparative liking or surface-level acceptability. Judgment signals encode structured reasoning, criteria, contextual evaluation, and responsibility-bearing assessment. Preference optimization can improve fluency, tone, and perceived helpfulness. Judgment signals are required for truthfulness, safety, edge-case robustness, professional validity, and institutional accountability. The paper argues that current alignment paradigms overemphasize preference while underinvesting in structured judgment infrastructure. To formalize this, the paper develops a layered taxonomy of human input: Preference signals — lightweight comparative preferences and surface reactions. Ordinary judgment signals — contextual corrections, local relevance assessments, edge-case observations, and grounded user feedback. Professional verification signals — domain-qualified approval, rejection, escalation, and responsibility-bearing review. Structural frame-building signals — constitutions, rubrics, benchmarks, interface defaults, and escalation architectures that determine how all lower-level signals are interpreted. The paper argues that sustainable AI ecosystems require all four layers simultaneously rather than collapsing governance into aggregated preference metrics. A key theoretical contribution is the concept of Frame Gravity, defined as the tendency for AI outputs, user interactions, and resulting feedback data to bend toward the framing conditions established by users and interfaces. Because LLMs strongly adapt to conversational framing and user style, weakly framed users may unknowingly reinforce sycophancy, affirmation bias, and worldview capture. Thin preference pipelines can therefore launder framing distortions into future training distributions. This leads to the proposal of a three-tier negentropy infrastructure: Ordinary judgment infrastructure — broad, diverse user corrections and grounded real-world feedback. Professional verification infrastructure — responsibility-bearing domain oversight for high-consequence outputs. Structural frame-building infrastructure — governance systems that define what counts as valid judgment in the first place. Together, these layers preserve diversity, anomaly detection, institutional correction, and normative constraint across the AI ecosystem. The paper then turns to governance and design implications. “AI literacy” is criticized as an insufficient policy goal if it merely means familiarity with AI systems. Sustainable AI instead requires AI competence: the ability to frame tasks, maintain independent judgment, evaluate outputs against external criteria, escalate uncertainty appropriately, and resist inappropriate delegation. Several design principles follow: Separate preference capture from judgment capture. Route feedback according to risk and expertise level. Weight signals by demonstrated reliability rather than raw volume. Preserve structured expert disagreement rather than flattening it into consensus. Use cognitive-forcing interfaces, Socratic guidance, and staged assistance in learning contexts. Explicitly assign responsibility nodes with override authority. The paper also emphasizes the importance of preserving human cognition itself as a governance objective. AI systems that replace effortful judgment rather than scaffolding it risk degrading both the models and the humans interacting with them. Sustainable systems therefore require architectures that preserve human framing, testing, interpretation, and contestation capacities rather than optimizing solely for convenience and satisfaction. The paper concludes that distributed human judgment should be treated as essential infrastructure rather than auxiliary feedback. Within the Civilization Physics framework, this work establishes a broader principle: sustainable intelligence systems remain viable only when continuously supplied with structured, reality-linked negative entropy through diverse human judgment layers. Preference alone cannot preserve contact with reality. Long-term AI stability depends on maintaining humans not merely as consumers of outputs, but as active generators of judgment, correction, and accountable structure. Keywords: Distributed Human Judgment · Negative Entropy · AI Governance · Preference Signals · Judgment Signals · Frame Gravity · Automation Bias · Cognitive Offloading · Human Oversight · Civilization Physics