AI-Generated Disinformation in the UK: Risk of Harm, Context, and Classification

2026-10-05

AI Summary

This study addresses the threats posed by AI-generated disinformation to social trust and public safety by presenting the first systematic quantification of its multidimensional harm landscape within UK society. Through the analysis of 112 high-exposure instances of AI-generated false content, this work employs an empirical data-driven taxonomy and risk assessment methodology to identify substantive risks across eight major domains. The findings demonstrate that AI-generated disinformation significantly exacerbates the risks of social unrest, health-related harms, and financial fraud, while broadly eroding public trust in information ecosystems. By mapping these vulnerabilities, this research provides critical empirical evidence to inform effective AI governance frameworks and targeted mitigation strategies against the escalating societal impacts of synthetic media.

Abstract

More people across the UK are today exposed to more AI-generated and AI-altered disinformation and misinformation than ever before - exposing individuals and society to a growing range of harmful consequences and potential consequences. We analyse evidence from a dataset of 112 pieces of AI-generated or AI-altered disinformation or misinformation seen tens of millions of times across the UK between 1 January 2025 and 31 March 2026. The dataset of examples studied does not, of course, provide an exhaustive sample of all AI-generated false information in circulation in the period, but rather a snapshot of examples showing some, but not all, of the potential effects. Our analysis of this sample found a substantive risk of causing or contributing to harm to individuals and society in eight distinct fields, from contributing to incidents of serious social unrest and vigilante violence to causing direct harms to health and causing the sort of serious financial loss that can be caused by online scams and fraud. Other fields of risk included: abuse serious enough to affect individuals'health and behaviour; public engagement with the police and justice systems; susceptibility to false conspiracy theories with the potential to cause direct harms; and broader changes to social and political attitudes with potential to affect political, social events over the longer term. More than four in five pieces of content we assessed added to reasons for the public to distrust information as not merely inaccurate but substantively false or misleading: a broad disinformation effect with potential for significant effects for society.

Problem

• AI-generated disinformation
• misinformation
• risk of harm
• public distrust
• UK

Innovation

• AI-generated disinformation
• risk classification
• harm analysis
• misinformation dataset
• public trust