[Purpose/Significance]As anti-terrorism steps into the intelligent era, anti-terrorism prediction algorithm, as an application al-gorithm of machine learning, has a remarkable effect on predicting terrorist organizations and terrorists. However, the " confidentiality black box" and the "technology black box" problems, known as the"double black boxes" problems, which appear in intelligent anti-ter-rorism are the key of this research. [Method/Process]Taking the US military war on terror as a case, this research empirically analyzes the causes of "double black boxes", such as data deviation, algorithm discrimination, over-reliance, insufficient transparency and ineffective supervision and accountability and normatively analyzes the policy and legal exploration of the US government. [ Result/Conclusion] In conclusion, ways to solve the "double black boxes" problems of anti-terrorism prediction algorithm involve ensuring the transparency of interpretability, accountability and participation.%目的/意义 智能反恐时代,反恐预测算法作为机器学习的应用算法,在预测恐怖活动组织和恐怖分子方面,效果显著.然而,应用中出现了"保密黑箱"叠加"技术黑箱"的"双黑箱"问题,对之研究具有重要的现实价值.[方法/过程 以美军反恐战争为研究样本,实证分析了造成"双黑箱"的原因,如数据偏差、算法歧视、过度依赖、透明不足和问责不力等;规范分析了美国政府破解"双黑箱"的政策和法律探索.[结果/结论 研究认为,确保可参与性、可诠释性和可问责性的透明化路径是解决反恐预测算法"双黑箱"的积极面向.
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