What would the inputs be to a machine whose output is the destabilization of a robust democracy, or whose emanations could disrupt the political power of nations? In the recent essay "The Coming AI Hackers," Schneier (2021) proposed a future application of artificial intelligences to discover, manipulate, and exploit vulnerabilities of social, economic, and political systems at speeds far greater than humans' ability to recognize and respond to such threats. This work advances the concept by applying to it theory from machine learning, hypothesizing some possible "featurization" (input specification and transformation) frameworks for AI hacking. Focusing on the political domain, we develop graph and sequence data representations that would enable the application of a range of deep learning models to predict attributes and outcomes of political, particularly legislative, systems. We explore possible data models, datasets, predictive tasks, and actionable applications associated with each framework. We speculate about the likely practical impact and feasibility of such models, and conclude by discussing their ethical implications.

翻译：如果一台机器的输出是破坏稳健民主，或其辐射可能扰乱国家政治权力，那么它的输入会是什么？在最近的文章《即将到来的人工智能黑客》中，Schneier（2021）提出了人工智能的未来应用，即发现、操控和利用社会、经济及政治系统的漏洞，其速度远超人类识别和应对此类威胁的能力。本研究通过应用机器学习理论推进这一概念，为人工智能黑客假设了一些可能的“特征化”（输入规范与转换）框架。聚焦政治领域，我们开发了图和序列数据表示方法，使得各类深度学习模型能够应用于预测政治系统（尤其是立法系统）的属性与结果。我们探索了与每个框架相关的可能数据模型、数据集、预测任务及可操作应用。我们推测了这些模型的实际影响和可行性，并最后讨论了其伦理意义。