Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks
Summary
This paper demonstrates that content moderation systems can cause disproportionate harm to bridge users connecting separate communities, even when aggregate accuracy metrics appear satisfactory, with governance loss increasing under false-positive-heavy conditions.
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Paper page - Selective Control under Noisy Perception: Governance Failures Hidden by Aggregate Metrics in Modular Networks
Source: https://huggingface.co/papers/2606.14819
Abstract
Content moderation systems can cause disproportionate harm to bridge users connecting separate communities, even when overall accuracy metrics appear satisfactory, with governance loss increasing significantly under false-positive-heavy conditions.
A content-moderation system can score well on every standard accuracy metric and still cause real harm, if its mistakes fall on the few users who connect otherwise separate communities. We show this in anagent-based modelwhere N=240 learning agents on acommunity-structured networkeach post harmless, productive, or dangerous content, and aregulatorremoves or penalizes whatever anoisy classifierflags. Overall usefulness barely moves as the noise changes (one-way ANOVA, p=0.96): by aggregate measures, nothing looks wrong. The damage instead concentrates on thesebridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. Agovernance loss(L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles underfalse-positive-heavy noise. Aggregate accuracy hides who is harmed, and the cheap quantity to audit is how many connections a user has (degree), a near-perfect proxy for thebetweennessthat defines a bridge (r=0.96).
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