越南具有法律意识的人工智能节制:一个符合网络安全法的平台治理框架

Luong Vu Bui
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引用次数: 0

摘要

数字平台越来越多地调解欺诈、非法贸易、错误信息和煽动行为,通常需要严格的法定时限,需要快速和可审计的回应。传统的审核管道仍然调整为分类器的准确性,使它们与法律类别、正当过程保障和威慑目标不一致。这就产生了代价高昂的假阳性结果,阻碍了合法的表达;产生了假阴性结果,造成了社会危害;我们通过引入一个具有法律意识的人工智能调节框架来解决这一差距,该框架将法律→政策→模型标签→证据最小化→行动层→上诉路径整合到一个单一的、可审计的管道中,并具有人在环审查和强制性理由。该框架通过对犯罪的预期效用进行建模,并展示风险阈值、分级制裁和上诉质量如何在内部化执法外部性的同时改变激励机制,从而嵌入经济犯罪学。我们开发了一个包含50,000个多信号项目的决策分析模拟,用于在伤害成本边界上校准治理选择。结果表明,在匹配的阈值下,与二元移除相比,分级行动菜单在保持威慑和降低上诉推翻率的同时,每单位执法成本减少2.51 × 。这一证据表明,适度选择在犯罪市场中起着价格信号的作用:提高确定性和比例严重性,降低罪犯的预期回报,同时通过可逆性和透明度保护合法性。通过量化威慑、外部性减少和执行成本效率,该框架将适度重新定义为治理经济学问题,而不仅仅是技术准确性问题。在经济犯罪学中,它通过成本调整的伤害减少边界实现威慑(确定性×严重性)、理性选择激励和有能力的监护。通过开放的研究对象和管辖权可移植性协议,它为平台治理提供了一个可复制和可审计的蓝图,该蓝图是兼容的、经济上合理的,并且可以跨部门和司法管辖区转移。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Legally-aware AI moderation in Vietnam: A cybersecurity law-compliant framework for platform governance
Digital platforms increasingly mediate fraud, illicit trade, misinformation, and incitement, often under stringent statutory timelines that demand rapid and auditable responses. Conventional moderation pipelines remain tuned to classifier accuracy, leaving them misaligned with legal categories, due-process safeguards, and deterrence goals. This produces costly false positives that chill lawful expression, false negatives that enable social harm, and thin audit trails that weaken accountability. We address this gap by introducing a legally-aware AI moderation framework that integrates law → policy → model label → evidence minima → action tier → appeal path into a single, auditable pipeline with human-in-the-loop review and mandatory rationales. The framework embeds economic criminology by modeling the expected utility of offending and demonstrating how risk thresholds, graduated sanctions, and appeal quality shift incentives while internalizing enforcement externalities. We develop a decision-analytic simulation of 50,000 multi-signal items that calibrates governance choices on a harm–cost frontier. Results show that at matched thresholds, a graduated action menu achieves 2.51 × greater harm reduction per unit enforcement cost compared to binary removal, while sustaining deterrence and lowering appeal overturn rates. This evidence demonstrates that moderation choices function as price signals in a crime market: raising certainty and proportional severity reduces offenders’ expected payoffs while protecting legitimacy through reversibility and transparency. By quantifying deterrence, externality reduction, and enforcement-cost efficiency, the framework reframes moderation as a problem of governance economics, not just technical accuracy. Situated within economic criminology, it operationalizes deterrence (certainty × severity), rational-choice incentives, and capable guardianship via a cost-adjusted harm-reduction frontier. Through open research objects and a jurisdictional portability protocol, it offers a replicable and auditable blueprint for platform governance that is compliant, economically rational, and transferable across sectors and jurisdictions.
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