Large Language Models as Instruments of Power: New Regimes of Autonomous Manipulation and Control
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Abstract (arXiv)
Large language models (LLMs) can reproduce a wide variety of rhetorical styles and generate text that expresses a broad spectrum of sentiments. This capacity, now available at low cost, makes them powerful tools for manipulation and control. In this paper, we consider a set of underestimated societal harms made possible by the rapid and largely unregulated adoption of LLMs. Rather than consider LLMs as isolated digital artefacts used to displace this or that area of work, we focus on the large-scale computational infrastructure upon which they are instrumentalised across domains. We begin with discussion on how LLMs may be used to both pollute and uniformize information environments and how these modalities may be leveraged as mechanisms of control. We then draw attention to several areas of emerging research, each of which compounds the capabilities of LLMs as instruments of power. These include (i) persuasion through the real-time design of choice architectures in conversational interfaces (e.g., via "AI personas"), (ii) the use of LLM-agents as computational models of human agents (e.g., "silicon subjects"), (iii) the use of LLM-agents as computational models of human agent populations (e.g., "silicon societies") and finally, (iv) the combination of LLMs with reinforcement learning to produce controllable and steerable strategic dialogue models. We draw these strands together to discuss how these areas may be combined to build LLM-based systems that serve as powerful instruments of individual, social and political control via the simulation and disingenuous "prediction" of human behaviour, intent, and action.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 May 2024.
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Citing papers per year: 2024: 2; 2025: 7; 2026: 1.
10 papers.
- 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agentscited by 76
- 2024Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Modelscited by 14
- 2025Simulating Society Requires Simulating Thoughtcited by 12
- 2025Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generationcited by 7
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Modelscited by 6
- 2026AI Agents Alone Are Not (Yet) Sufficient for Social Simulationcited by 3
- 2025An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Casecited by 0
- 2025How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experimentscited by 0
- 2025Building Resilient Information Ecosystems: Large LLM-Generated Dataset of Persuasion Attackscited by 0
- 2025Beyond Real versus Fake Towards Intent-Aware Video Analysiscited by 0
- 2026AI Agents Alone Are Not (Yet) Sufficient for Social Simulation19 Feb 2026
- 2025Beyond Real versus Fake Towards Intent-Aware Video Analysis27 Nov 2025
- 2025Building Resilient Information Ecosystems: Large LLM-Generated Dataset of Persuasion Attacks23 Nov 2025
- 2025Beyond Data Privacy: New Privacy Risks for Large Language Models16 Sep 2025
- 2025Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation8 Sep 2025
- 2025How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments13 Aug 2025
- 2025An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case25 Jul 2025
- 2025Simulating Society Requires Simulating Thought8 Jun 2025
- 2024Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models22 Dec 2024
- 2024From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents4 Dec 2024
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- 2024Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions
- 2026From Tokens To Agents: A Researcher's Guide To Understanding Large Language Models
- 2024Systematic Biases in LLM Simulations of Debates
- 2025Computational Basis of LLM's Decision Making in Social Simulation
- 2025Can (A)I Change Your Mind?
- 2026When Agents Persuade: Rhetoric Generation and Mitigation in LLMs
- 2025Stop saying LLM: Large Discourse Models (LDM) and Artificial Discursive Agent (ADA)?
- 2025From the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation