Machine Learning Featurizations for AI Hacking of Political Systems

• URL: http://arxiv.org/abs/2110.09231v1
• Date: Fri, 8 Oct 2021 16:51:31 GMT
• Title: Machine Learning Featurizations for AI Hacking of Political Systems
• Authors: Nathan E Sanders, Bruce Schneier
• Abstract summary: In the recent essay "The Coming AI Hackers," Schneier proposed a future application of artificial intelligences to discover, manipulate, and exploit vulnerabilities of social, economic, and political systems. This work advances the concept by applying to it theory from machine learning, hypothesizing some possible "featurization" frameworks for AI hacking. 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 systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 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.

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