噪声感知下的选择性控制:模块化网络中聚合指标隐藏的治理失败
阅读原文· arxiv.org基于240个学习智能体的社区网络模拟显示,内容审核在噪声分类器下标准准确率几乎不变(p=0.96),但伤害集中在桥梁用户:有用帖子被错误抑制、危险帖子被错误放过。将两类错误与执法成本分开计算的治理损失L_gov在假阳性偏高噪声下翻倍。聚合准确率无法揭示受损对象,而用户度(连接数)近乎完美代理中介中心性(r=0.96),可作为低成本审计指标。
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 an agent-based model where N=240 learning agents on a community-structured network each post harmless, productive, or dangerous content, and a regulator removes or penalizes whatever a noisy classifier flags. 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 these bridge users, whose useful posts are wrongly suppressed and whose dangerous posts are wrongly spared. A governance loss (L_gov) that prices these two mistakes separately from the cost of enforcement more than doubles under false-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 the betweenness that defines a bridge (r=0.96).