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# Toward Resisting AI-Enabled Authoritarianism

Isaac Friend University of Oxford

Fazl Barez University of Oxford Whitebox

Keir Reid Independent Researcher

Igor Krawczuk Independent Researcher

Vincent Wang University of Oxford

Jakob Mökander Tony Blair Institute

Philip Torr University of Oxford

Julia Morse University of Oxford

Robert Trager University of Oxford

# Abstract

Artificial-intelligence systems built with statistical machine learning have become the operating system of contemporary surveillance and information control, spanning both physical and online spaces. City-scale face-recognition grids, real-time social-media takedown engines and predictive “pre-crime” dashboards share four politically relevant technical features: massive data ingestion, black-box inference, automated decision-making, and no human in the loop. These features now amplify authoritarian power and erode liberal-democratic norms across many political regimes. Yet mainstream machine learning research still devotes only limited attention to technical safeguards such as differential privacy, federated-learning security and large-model interpretability, or adversarial methods that can help the public resist AI-enhanced domination. We identify four resulting gaps: evidence (little empirical measurement of safeguard deployment), capability (open problems such as billion-parameter privacy–utility trade-offs, causal explanations for multimodal models and Byzantine-resilient federated learning), deployment (public-sector AI systems almost never ship with safeguards enabled by default) and asymmetry (authoritarian actors already enjoy a “power surplus,” so even incremental defensive advances matter). We propose re-directing the field toward a triad of safeguards—privacy preservation, formal interpretability and adversarial user tooling—and outline concrete research directions that fit within standard ML practice. Shifting community priorities toward Explainable-by-Design, Privacy-by-Default is a pre-condition for any durable defense of liberal democracy.

# 1 Introduction

The development of statistical learning-driven AI systems enables new degrees and new forms of social control. Contemporary AI systems differ from previous technologies in four ways relevant to political power: their ability to process unprecedented volumes of data in real time, their expanded capacity for automated decision-making without human intervention, their predictive modeling, and their black-box nature. Meanwhile, the global social context in which technology must be considered is characterized in part by an increase in authoritarian politics, enough that it makes sense to speak of an international trend. 1 In this paper, we do not assert (nor do we deny) a central causal role for ML technology in this major international political change, but establish by example that development and deployment of machine learning technology can, via the four technical properties discussed above, enhance 

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