?asOf= parameter to see the current catalog state.Dual-Use Research Norms (DURC for AI)
dual-use-research-taxonomy · Frontier safety
A normative framework — adapted from biosecurity's Dual-Use Research of Concern (DURC) policies — for governing AI research and publication decisions when research outputs have both beneficial and harmful applications.
Definition & scope
Dual-use research norms in AI explicitly draw on the biosecurity precedent: the 1975 Asilomar conference on recombinant DNA, the 2004 US National Science Advisory Board for Biosecurity, and the 2014 US gain-of-function moratorium. The AI parallels are publication-control debates around GPT-2 (OpenAI's staged release, 2019), the deepfake-generation research community (FaceSwap-era, 2017-2020), CBRN-uplift research, and offensive cybersecurity capabilities (e.g., AutoAttack research). Field positions cluster: (a) full publication — Brundage et al. 2018 critique of selective release; (b) staged or structured access — Solaiman et al. 2019; (c) capability-thresholded redaction — Anthropic, OpenAI, DeepMind dual-use policies, 2023-2025. Governance instruments are catching up. US EO 14110 §4.2(a)(ii) explicitly required reporting on dual-use capabilities including CBRN, cyber, and autonomous-replication. EU AI Act Art. 5 prohibits certain dual-use applications (manipulation, social scoring) but does not regulate research-stage decisions. NIST AI RMF Map 1.1 includes 'risk of misuse' assessment but does not prescribe publication norms. The G7 Hiroshima Code §3 endorses 'responsible information sharing' without operationalising it. For AI safety researchers, dual-use research norms are the closest analogue to peer-review-style governance of which findings should be public — a research-community-internal governance layer that operates upstream of regulator-mandated controls.
Locus of dispute: Is the biosecurity DURC analogy applicable to AI? Information-spread dynamics differ fundamentally (Brundage 2023); the field has not converged on whether DURC-style governance translates.
Use in governance
How instruments operationalise this concept
| Instrument | Jurisdiction | Status |
|---|---|---|
| Executive Order 14110 on Safe, Secure, Trustworthy AI | US | partial |
| G7 Hiroshima AI Process Code of Conduct | G7 | in force |
| NIST AI Risk Management Framework | US | in force |
| Anthropic Responsible Scaling Policy (RSP) v2 | US | in force |
| OpenAI Preparedness Framework | US | in force |
| Google DeepMind Frontier Safety Framework | US | in force |
| Meta Frontier AI Framework | US | in force |
| White House Voluntary AI Commitments | US | in force |
Appears in topic articles
Editorial note
The biosecurity DURC analogy is contested: critics (Brundage 2023) argue that information-spread dynamics in AI are fundamentally different from biological materials. Pair citations of 'dual-use research norms in AI' with a note on the analogy's contested status.
See also
Further reading
Sources on the broader topics this concept relates to — complementing, not standing in for, the primary sources cited inline above. 71 academic & grey-literature sources; catalogued metadata with a primary link; one-line findings are ✦ AI-generated summaries, labeled as such (charter §7.9). Browse the full literature index.
- Open Foundation Models and TDM Exceptions to Copyright – Building Blocks for an AI Ecosystem Peer-reviewed✦ AIArgues Art. 3 CDSM Directive's scientific-research TDM exception 'does not grant rightsholders any control' and can be a 'safe harbor' for training openly released foundation models without licensing data.
- An interdisciplinary account of the terminological choices by EU policymakers ahead of the final agreement on the AI Act: AI system, general purpose AI system, foundation model, and generative AI Peer-reviewed✦ AITraces how the AI Act's legal text shifted across versions among the terms 'AI system, general purpose AI system, foundation model, and generative AI', exposing definitional instability in the regime.
- The EU model of AI governance: regulating artificial intelligence through law and policy Peer-reviewed✦ AIAnalyses how the AI Act's risk-based model handles general-purpose and foundation models whose 'autonomous content generation challenges legal categories of authorship, accountability, and control'.
- Generative AI and data protection Peer-reviewed✦ AIExamines friction between foundation-model training and the GDPR, noting models that 'memorize and leak pieces of training data' cannot be treated as anonymous.
- Copyright and AI in the UK: Opting-In or Opting-Out? Peer-reviewed✦ AIContends the UK opt-in/opt-out framing is a 'missed opportunity'; a broadened research exception plus market-entry transparency and creator remuneration would better serve both innovation and rightsholders.
- Technical Challenges of Rightsholders' Opt-out From Gen AI Training after Robert Kneschke v. LAION Peer-reviewed✦ AIExamines post-LAION practical obstacles to the EU TDM opt-out (robots.txt, machine-readability, memorisation): 'While the TDM exceptions may seem workable in theory, implementing them in practice presents a variety of practical…
- GPTs are GPTs: Labor market impact potential of LLMs Peer-reviewed✦ AIFinds around 80% of the U.S. workforce "could have at least 10% of their work tasks affected" by LLMs, which exhibit "traits of general-purpose technologies".
- Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity Peer-reviewed✦ AIExamines how the EU AI Act, liability regimes, GDPR, copyright and cybersecurity rules apply to generative AI, identifying gaps and proposing targeted regulatory refinements.
- A large-scale audit of dataset licensing and attribution in AI Peer-reviewed✦ AIAudit of 1,800+ AI training datasets finds "licence omission rates of more than 70% and error rates of more than 50%" on popular hosting sites.
- Evaluating Frontier Models for Dangerous Capabilities Preprint✦ AIPilots dangerous-capability evaluations (persuasion, cyber, self-proliferation) on frontier models, finding 'early warning signs' but no strong present danger — grounding evaluation-based gating.
- The Right to Transparency in Public Governance: Freedom of Information and the Use of Artificial Intelligence by Public Agencies Peer-reviewed✦ AIFinds freedom-of-information regimes "generally only grant access to existing documents" and that with "no mature standard for documenting AI models," public-sector AI transparency is limited.
- On the Quest for Effectiveness in Human Oversight: Interdisciplinary Perspectives Peer-reviewed✦ AISynthesises interdisciplinary evidence to argue that legally mandated human oversight of AI is often ineffective ('rubber-stamp') unless effectiveness conditions are explicitly designed for.
+ 59 more across this concept's topics — see the literature index.
References
The primary instrument sources behind the article's classifications.
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