Securing Dual-Use Pathogen Data of Concern

• URL: http://arxiv.org/abs/2602.08061v1
• Date: Sun, 08 Feb 2026 17:11:19 GMT
• Title: Securing Dual-Use Pathogen Data of Concern
• Authors: Doni Bloomfield, Allison Berke, Moritz S. Hanke, Aaron Maiwald, James R. M. Black, Toby Webster, Tina Hernandez-Boussard, Oliver M. Crook, Jassi Pannu,
• Abstract summary: Training data is an essential input into creating competent artificial intelligence (AI) models.<n>Data controls may be among the most high-leverage interventions available to reduce the proliferation of concerning biological AI capabilities.
• Score: 4.518583284698333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training data is an essential input into creating competent artificial intelligence (AI) models. AI models for biology are trained on large volumes of data, including data related to biological sequences, structures, images, and functions. The type of data used to train a model is intimately tied to the capabilities it ultimately possesses--including those of biosecurity concern. For this reason, an international group of more than 100 researchers at the recent 50th anniversary Asilomar Conference endorsed data controls to prevent the use of AI for harmful applications such as bioweapons development. To help design such controls, we introduce a five-tier Biosecurity Data Level (BDL) framework for categorizing pathogen data. Each level contains specific data types, based on their expected ability to contribute to capabilities of concern when used to train AI models. For each BDL tier, we propose technical restrictions appropriate to its level of risk. Finally, we outline a novel governance framework for newly created dual-use pathogen data. In a world with widely accessible computational and coding resources, data controls may be among the most high-leverage interventions available to reduce the proliferation of concerning biological AI capabilities.

Related papers

• What's the next frontier for Data-centric AI? Data Savvy Agents [71.76058707995398]
  We argue that data-savvy capabilities should be a top priority in the design of agentic systems.<n>We propose four key capabilities to realize this vision: Proactive data acquisition, Sophisticated data processing, Interactive test data synthesis, and Continual adaptation.
  arXiv  Detail & Related papers  (2025-11-02T17:09:29Z)
• A Survey of Data Agents: Emerging Paradigm or Overstated Hype? [66.1526688475023]
  "Data agent" currently suffers from terminological ambiguity and inconsistent adoption.<n>This survey introduces the first systematic hierarchical taxonomy for data agents.<n>We conclude with a forward-looking roadmap, envisioning the advent of proactive, generative data agents.
  arXiv  Detail & Related papers  (2025-10-27T17:54:07Z)
• Standards in the Preparation of Biomedical Research Metadata: A Bridge2AI Perspective [1.0389904886733017]
  Bridge2AI has defined the criteria a dataset may possess to render it AI-ready.<n>These criteria include FAIRness, provenance, degree of characterization, explainability, sustainability, and computability.<n>This report assesses the state of metadata creation and standardization in the Bridge2AI Grand Challenges.
  arXiv  Detail & Related papers  (2025-09-12T17:38:46Z)
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [138.07763415496288]
  We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• Leveraging Generative AI Through Prompt Engineering and Rigorous Validation to Create Comprehensive Synthetic Datasets for AI Training in Healthcare [0.0]
  The GPT-4 API was employed to generate high-quality synthetic datasets aimed at overcoming this limitation.<n>The generated data encompassed a comprehensive array of patient admission information, including healthcare provider details, hospital departments, wards, bed assignments, patient demographics, emergency contacts, vital signs, immunizations, allergies, medical histories, appointments, hospital visits, laboratory tests, diagnoses, treatment plans, medications, clinical notes, visit logs, discharge summaries, and referrals.
  arXiv  Detail & Related papers  (2025-04-29T16:37:34Z)
• A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI [70.06771291117965]
  We introduce Biomedica, an open-source dataset derived from the PubMed Central Open Access subset.<n>Biomedica contains over 6 million scientific articles and 24 million image-text pairs.<n>We provide scalable streaming and search APIs through a web server, facilitating seamless integration with AI systems.
  arXiv  Detail & Related papers  (2025-03-26T05:56:46Z)
• Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework [8.017827642932746]
  Generalized Attribute Utility and Detectability-Induced bias Testing (G-AUDIT) for datasets is a modality-agnostic dataset auditing framework.<n>Our method examines the relationship between task-level annotations and data properties including patient attributes.<n>G-AUDIT successfully identifies subtle biases commonly overlooked by traditional qualitative methods.
  arXiv  Detail & Related papers  (2025-03-13T02:16:48Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• Deep Learning and Handheld Augmented Reality Based System for Optimal Data Collection in Fault Diagnostics Domain [0.0]
  This paper presents a novel human-machine interaction framework to perform fault diagnostics with minimal data. Minimizing the required data will increase the practicability of data-driven models in diagnosing faults. The proposed framework has provided above 100% precision and recall on a novel dataset with only one instance of each fault condition.
  arXiv  Detail & Related papers  (2022-06-15T19:15:26Z)

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.