生成式人工智能视域下情报分析算法风险多重治理机制研究

Research on Multi-layer Governance Mechanism for Algorithmic Risk in Intelligence Analysis under Generative Artificial Intelligence

随着进入生成式人工智能时代,算法的快速发展和应用颠覆了传统的情报分析模式,成为情报信息采集和分析重要的工具。但随着算法在军事情报等重要领域的广泛应用,不仅引发了一系列挑战和严重的安全风险,也引起全球范围的担忧。文章聚焦生成式人工智能视域下情报分析算法,在系统阐释算法风险产生的内外部因素的基础上,通过有机融合协同治理、敏捷治理以及精准治理理论,构建情报分析算法风险的多重治理机制框架。内容包括:事前治理:情报分析算法风险的源头治理;事中治理:情报分析算法风险的敏捷治理;事后治理:情报分析算法风险的精准治理。以期为有效防范和应对生成式人工智能视域下情报分析算法风险提供参考,使情报分析能够更好地服务于国家战略需要。

With the emergence of generative artificial intelligence,the rapid advancement and application of algorithms have fundamentally transformed traditional models of intelligence analysis,establishing themselves as crucial tools for collecting and analyzing intelligence information.However,with the increasing application of algorithms in critical domains such as military intelligence,they have engendered a plethora of challenges and great security risks,thereby eliciting global apprehensions.This paper focuses on algorithmic risks in the context of intelligence analysis facilitated by generative artificial intelligence,systematically elucidating both internal and external factors that contribute to such risks.By seamlessly integrating theories on collaborative governance,agile governance,and precise governance into a cohesive framework,it establishes a multi-layered mechanism for governing algorithmic risk in intelligence analysis.This framework encompasses:Pre-event governance:source control over algorithmic risk in intelligence analysis;Mid-event governance:agile management of algorithmic risk in intelligence analysis;Post-event governance:precise handling of algorithmic risk in intelligence analysis.The objective is to provide guidance for effectively preventing and addressing algorithmic risks associated with intelligent analysis within the realm of generative artificial intelligence so that it may better serve national strategic requirements.