Beyond Accidents and Misuse: Decoding the Structural Risk Dynamics of Artificial Intelligence

• URL: http://arxiv.org/abs/2406.14873v2
• Date: Fri, 16 Aug 2024 04:28:44 GMT
• Title: Beyond Accidents and Misuse: Decoding the Structural Risk Dynamics of Artificial Intelligence
• Authors: Kyle A Kilian,
• Abstract summary: This paper explores the concept of structural risks associated with the rapid integration of advanced AI systems across social, economic, and political systems. By analyzing the interactions between technological advancements and social dynamics, this study isolates three primary categories of structural risk. We present a comprehensive framework to understand the causal chains that drive these risks, highlighting the interdependence between structural forces and the more proximate risks of misuse and system failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The integration of artificial intelligence (AI) across contemporary industries is not just a technological upgrade but a transformation with profound structural implications. This paper explores the concept of structural risks associated with the rapid integration of advanced AI systems across social, economic, and political systems. This framework challenges the conventional perspectives that primarily focus on direct AI threats such as accidents and misuse and suggests that these more proximate risks are interconnected and influenced by a larger sociotechnical system. By analyzing the interactions between technological advancements and social dynamics, this study isolates three primary categories of structural risk: antecedent structural causes, antecedent system causes, and deleterious feedback loops. We present a comprehensive framework to understand the causal chains that drive these risks, highlighting the interdependence between structural forces and the more proximate risks of misuse and system failures. The paper articulates how unchecked AI advancement can reshape power dynamics, trust, and incentive structures, leading to profound and often unpredictable shifts. We introduce a methodological research agenda for mapping, simulating, and gaming these dynamics aimed at preparing policymakers and national security officials for the challenges posed by next-generation AI technologies. The paper concludes with policy recommendations.

Related papers

• Multi-Agent Risks from Advanced AI [90.74347101431474]
  Multi-agent systems of advanced AI pose novel and under-explored risks. We identify three key failure modes based on agents' incentives, as well as seven key risk factors. We highlight several important instances of each risk, as well as promising directions to help mitigate them.
  arXiv  Detail & Related papers  (2025-02-19T23:03:21Z)
• Safety is Essential for Responsible Open-Ended Systems [47.172735322186]
  Open-Endedness is the ability of AI systems to continuously and autonomously generate novel and diverse artifacts or solutions. This position paper argues that the inherently dynamic and self-propagating nature of Open-Ended AI introduces significant, underexplored risks.
  arXiv  Detail & Related papers  (2025-02-06T21:32:07Z)
• Lessons from complexity theory for AI governance [1.6122472145662998]
  Complexity theory can help illuminate features of AI that pose central challenges for policymakers. We examine how efforts to govern AI are marked by deep uncertainty. We propose a set of complexity-compatible principles concerning the timing and structure of AI governance.
  arXiv  Detail & Related papers  (2025-01-07T07:56:40Z)
• Considerations Influencing Offense-Defense Dynamics From Artificial Intelligence [0.0]
  AI can enhance defensive capabilities but also presents avenues for malicious exploitation and large-scale societal harm. This paper proposes a taxonomy to map and examine the key factors that influence whether AI systems predominantly pose threats or offer protective benefits to society.
  arXiv  Detail & Related papers  (2024-12-05T10:05:53Z)
• A Taxonomy of Systemic Risks from General-Purpose AI [2.5956465292067867]
  We consider systemic risks as large-scale threats that can affect entire societies or economies. Key sources of systemic risk emerge from knowledge gaps, challenges in recognizing harm, and the unpredictable trajectory of AI development. This paper contributes to AI safety research by providing a structured groundwork for understanding and addressing the potential large-scale negative societal impacts of general-purpose AI.
  arXiv  Detail & Related papers  (2024-11-24T22:16:18Z)
• Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
  This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems. Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
  arXiv  Detail & Related papers  (2024-11-05T17:17:45Z)
• Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI [52.138044013005]
  generative AI, particularly large language models (LLMs), become increasingly integrated into production applications. New attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weaknesses in these systems, while blue-teaming works to protect against such adversarial attacks. This work aims to bridge the gap between academic insights and practical security measures for the protection of generative AI systems.
  arXiv  Detail & Related papers  (2024-09-23T10:18:10Z)
• Quantifying AI Vulnerabilities: A Synthesis of Complexity, Dynamical Systems, and Game Theory [0.0]
  We propose a novel approach that introduces three metrics: System Complexity Index (SCI), Lyapunov Exponent for AI Stability (LEAIS), and Nash Equilibrium Robustness (NER) SCI quantifies the inherent complexity of an AI system, LEAIS captures its stability and sensitivity to perturbations, and NER evaluates its strategic robustness against adversarial manipulation.
  arXiv  Detail & Related papers  (2024-04-07T07:05:59Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing [7.1795069620810805]
  This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm.
  arXiv  Detail & Related papers  (2023-10-15T07:55:59Z)

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.