AI 中文总结
针对军事AI的伦理挑战，提出包含问责角色、对抗性审计和分级部署的治理框架，强调制度基础设施对保持人类判断的关键作用。
AI 中文摘要
深度学习系统如今在军事决策中调解使用武力的行为，然而其内部逻辑难以审查，其评估实践易受操纵，且其部署将责任分散于众多利益相关者之间。这些系统所带来的伦理挑战本质上是认识论层面的：不仅涉及自主武器是否应被允许杀人，还涉及当关键功能被委托给不透明算法时，负责任的人类判断所需的条件能否得以维持。我们表明，这种认识论条件产生了具体的责任缺口：责任在设计师、操作者和政策制定者之间分散，而国际人道法预设了当前人工智能系统所缺乏的判断能力。为解决这一缺口，我们提出一个治理框架，通过明确的问责角色、使用未公开基准的对抗性审计、分级部署阈值以及拟议的北约评估标准，将伦理约束程序化。对八个已记录案例（1988-2025年）的反事实分析表明，每种治理机制都能解决一类已记录的失败，但没有任何单一保障措施能独立奏效：有效的军事人工智能治理不仅需要技术约束，还需要使人类判断保持意义的制度基础设施。
英文摘要
Deep learning systems now mediate military decisions to use force, yet their internal logic resists inspection, their evaluation practices are gameable, and their deployment fractures accountability across dispersed stakeholders. The ethical challenge posed by these systems is fundamentally epistemic: not just whether autonomous weapons should be permitted to kill, but whether the conditions for responsible human judgment can survive when critical functions are delegated to opaque algorithms. We show that this epistemic condition produces a concrete accountability gap: responsibility diffuses across designers, operators, and policymakers while International Humanitarian Law presupposes capacities for judgment that current AI systems lack. To address this gap, we propose a governance framework that proceduralizes ethical constraints through named accountability roles, adversarial auditing with undisclosed benchmarks, tiered deployment thresholds, and a proposed NATO evaluation standard. Counterfactual analysis of eight documented cases (1988-2025) shows that each governance mechanism addresses a documented class of failure, but no single safeguard suffices in isolation: effective governance of military AI requires not only technical constraints but the institutional infrastructure to keep human judgment meaningful.
