Towards Risk Analysis of the Impact of AI on the Deliberate Biological Threat Landscape

• URL: http://arxiv.org/abs/2401.12755v3
• Date: Tue, 11 Jun 2024 13:45:58 GMT
• Title: Towards Risk Analysis of the Impact of AI on the Deliberate Biological Threat Landscape
• Authors: Matthew E. Walsh,
• Abstract summary: The perception that the convergence of biological engineering and artificial intelligence could enable increased biorisk has drawn attention to the governance of biotechnology and artificial intelligence. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how artificial intelligence can increase biorisk. The perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and artificial intelligence. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how artificial intelligence can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

Related papers

• Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
  We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context. We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
  arXiv  Detail & Related papers  (2024-07-31T17:59:24Z)
• Evaluating AI Evaluation: Perils and Prospects [8.086002368038658]
  This paper contends that the prevalent evaluation methods for these systems are fundamentally inadequate. I argue that a reformation is required in the way we evaluate AI systems and that we should look towards cognitive sciences for inspiration.
  arXiv  Detail & Related papers  (2024-07-12T12:37:13Z)
• Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models [0.0]
  We argue that AI evaluations model should prioritize addressing high-consequence risks. These risks could cause large-scale harm to the public, such as pandemics. Scientists' experience with identifying and mitigating dual-use biological risks can help inform new approaches to evaluating biological AI models.
  arXiv  Detail & Related papers  (2024-05-25T16:29:17Z)
• 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)
• The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks [0.0]
  AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab will constantly need to evolve. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations.
  arXiv  Detail & Related papers  (2024-02-01T03:53:13Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• 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)
• AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity [18.324118502535775]
  We argue that human-AI teaming is worthwhile in cybersecurity. We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
  arXiv  Detail & Related papers  (2023-09-28T01:20:44Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

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.