The Reality of AI and Biorisk

• URL: http://arxiv.org/abs/2412.01946v3
• Date: Thu, 02 Jan 2025 11:04:46 GMT
• Title: The Reality of AI and Biorisk
• Authors: Aidan Peppin, Anka Reuel, Stephen Casper, Elliot Jones, Andrew Strait, Usman Anwar, Anurag Agrawal, Sayash Kapoor, Sanmi Koyejo, Marie Pellat, Rishi Bommasani, Nick Frosst, Sara Hooker,
• Abstract summary: It is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model. This paper provides an analysis of existing available research surrounding two AI and biorisk threat models.
• Score: 24.945718952309157
• License:
• Abstract: To accurately and confidently answer the question 'could an AI model or system increase biorisk', it is necessary to have both a sound theoretical threat model for how AI models or systems could increase biorisk and a robust method for testing that threat model. This paper provides an analysis of existing available research surrounding two AI and biorisk threat models: 1) access to information and planning via large language models (LLMs), and 2) the use of AI-enabled biological tools (BTs) in synthesizing novel biological artifacts. We find that existing studies around AI-related biorisk are nascent, often speculative in nature, or limited in terms of their methodological maturity and transparency. The available literature suggests that current LLMs and BTs do not pose an immediate risk, and more work is needed to develop rigorous approaches to understanding how future models could increase biorisks. We end with recommendations about how empirical work can be expanded to more precisely target biorisk and ensure rigor and validity of findings.

Related papers

• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI. We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts. We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments [112.25067497985447]
  We introduce BioDiscoveryAgent, an agent that designs new experiments, reasons about their outcomes, and efficiently navigates the hypothesis space to reach desired solutions. BioDiscoveryAgent can uniquely design new experiments without the need to train a machine learning model. It achieves an average of 21% improvement in predicting relevant genetic perturbations across six datasets.
  arXiv  Detail & Related papers  (2024-05-27T19:57:17Z)
• 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)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• 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)
• Towards Risk Analysis of the Impact of AI on the Deliberate Biological Threat Landscape [0.0]
  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.
  arXiv  Detail & Related papers  (2024-01-23T13:35:16Z)
• 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)
• XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications [5.91274133032321]
  Explainable AI (XAI) has emerged as a promising solution to enhance the transparency and interpretability of AI models in bioinformatics. This review provides a comprehensive analysis of various XAI techniques and their applications across various bioinformatics domains.
  arXiv  Detail & Related papers  (2023-12-11T03:08:18Z)
• Unmasking Biases and Navigating Pitfalls in the Ophthalmic Artificial Intelligence Lifecycle: A Review [3.1929071422400446]
  This review article breaks down the AI lifecycle into seven steps. Data collection; defining the model task; data pre-processing and labeling; model development; model evaluation and validation; deployment. Finally, post-deployment evaluation, monitoring, and system recalibration and delves into the risks for harm at each step and strategies for mitigating them.
  arXiv  Detail & Related papers  (2023-10-08T03:49:42Z)
• 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)

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.