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 and 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.
Used by these instruments
- Executive Order 14110 on Safe, Secure, Trustworthy AI· US
- G7 Hiroshima AI Process Code of Conduct· G7
- NIST AI Risk Management Framework· US
- Anthropic Responsible Scaling Policy (RSP) v2· US
- OpenAI Preparedness Framework· US
- Google DeepMind Frontier Safety Framework· US
- Meta Frontier AI Framework· US
- White House Voluntary AI Commitments· US
Related concepts
- AI Alignment— The technical problem of designing AI systems whose objectives, behaviour, and emergent goals reliab
- Capability Elicitation— Techniques designed to reveal the upper bounds of an AI model's capabilities, rather than measuring
- Red-Team Evaluation— Structured adversarial probing of an AI model's capabilities and behaviour before deployment, design
- AI Safety Level 3 (ASL-3)— A capability-based risk tier in Anthropic's Responsible Scaling Policy denoting models with the pote
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.
References
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