Escalation Risks from Language Models in Military and Diplomatic Decision-Making

• URL: http://arxiv.org/abs/2401.03408v1
• Date: Sun, 7 Jan 2024 07:59:10 GMT
• Title: Escalation Risks from Language Models in Military and Diplomatic Decision-Making
• Authors: Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel, Max Lamparth, Chandler Smith, Jacquelyn Schneider
• Abstract summary: This work aims to scrutinize the behavior of multiple AI agents in simulated wargames. We design a novel wargame simulation and scoring framework to assess the risks of the escalation of actions taken by these agents. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models' reported reasonings for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.

Related papers

• Mind the Gap: Foundation Models and the Covert Proliferation of Military Intelligence, Surveillance, and Targeting [0.0]
  We show that the inability to prevent personally identifiable information from contributing to ISTAR capabilities may lead to the use and proliferation of military AI technologies by adversaries. We conclude that in order to secure military systems and limit the proliferation of AI armaments, it may be necessary to insulate military AI systems and personal data from commercial foundation models.
  arXiv  Detail & Related papers  (2024-10-18T19:04:30Z)
• The GPT Dilemma: Foundation Models and the Shadow of Dual-Use [0.0]
  This paper examines the dual-use challenges of foundation models and the risks they pose for international security. The paper analyzes four critical factors in the development cycle of foundation models: model inputs, capabilities, system use cases, and system deployment. Using the Intermediate-Range Nuclear Forces (INF) Treaty as a case study, this paper proposes several strategies to mitigate the associated risks.
  arXiv  Detail & Related papers  (2024-07-29T22:36:27Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Human vs. Machine: Behavioral Differences Between Expert Humans and Language Models in Wargame Simulations [1.6108153271585284]
  We show that large language models (LLMs) behave differently compared to humans in high-stakes military decision-making scenarios. Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.
  arXiv  Detail & Related papers  (2024-03-06T02:23:32Z)
• DPP-Based Adversarial Prompt Searching for Lanugage Models [56.73828162194457]
  Auto-regressive Selective Replacement Ascent (ASRA) is a discrete optimization algorithm that selects prompts based on both quality and similarity with determinantal point process (DPP) Experimental results on six different pre-trained language models demonstrate the efficacy of ASRA for eliciting toxic content.
  arXiv  Detail & Related papers  (2024-03-01T05:28:06Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Towards more Practical Threat Models in Artificial Intelligence Security [66.67624011455423]
  Recent works have identified a gap between research and practice in artificial intelligence security. We revisit the threat models of the six most studied attacks in AI security research and match them to AI usage in practice.
  arXiv  Detail & Related papers  (2023-11-16T16:09:44Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Confidence-Building Measures for Artificial Intelligence: Workshop Proceedings [3.090253451409658]
  Foundation models could eventually introduce several pathways for undermining state security. The Confidence-Building Measures for Artificial Intelligence workshop brought together a multistakeholder group to think through the tools and strategies to mitigate the risks. The flexibility of CBMs make them a key instrument for navigating the rapid changes in the foundation model landscape.
  arXiv  Detail & Related papers  (2023-08-01T22:20:11Z)
• The Role of Large Language Models in the Recognition of Territorial Sovereignty: An Analysis of the Construction of Legitimacy [67.44950222243865]
  We argue that technology tools like Google Maps and Large Language Models (LLM) are often perceived as impartial and objective. We highlight the case of three controversial territories: Crimea, West Bank and Transnitria, by comparing the responses of ChatGPT against Wikipedia information and United Nations resolutions.
  arXiv  Detail & Related papers  (2023-03-17T08:46:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.