Machine Learning Featurizations for AI Hacking of Political Systems

TL;DR

This paper explores how machine learning can be used to model and potentially exploit political systems, proposing data representations and discussing ethical concerns of AI-driven political manipulation.

Contribution

It introduces new featurization frameworks for applying deep learning to political data, advancing the understanding of AI hacking in political contexts.

Findings

Developed graph and sequence data representations for political systems

Analyzed potential predictive tasks and applications

Discussed ethical implications of AI hacking in politics

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, 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.