Dual-use Technology¶
Dual-use technologies are systems, tools, or capabilities designed and developed for legitimate, beneficial purposes that can also be repurposed or applied toward harmful ends. The concept, originating in security studies and weapons research, has become increasingly important in the AI and information security context as powerful machine learning systems and generative technologies become more widely available.
In the context of AI and disinformation, dual-use concerns arise because many AI capabilities—like natural language generation, image synthesis, and social network analysis—have clear beneficial applications (machine translation, medical imaging, public health) but can also enable malicious uses (automated disinformation, deepfakes, coordinated inauthentic behavior, influence operations).
The dual-use challenge for AI research is fundamentally different from traditional dual-use weapons research: AI knowledge and capabilities diffuse rapidly through academic publication, open-source software, and commercial availability. A single research advance can simultaneously enable both beneficial and harmful applications, making it difficult to prevent misuse without also suppressing beneficial uses.
Key papers¶
- The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation — Foundational framework analyzing dual-use nature of AI across digital, physical, and political security
- Release Strategies and the Social Impacts of Language Models — Examines responsible release strategies for powerful language models considering dual-use concerns
- On the Opportunities and Risks of Foundation Models — Discusses dual-use implications of large foundation models
Related topics¶
- AI Safety — Broader focus on ensuring AI systems behave safely and reliably
- Responsible AI — Approaches to developing AI with consideration for societal impacts
- AI-Generated Content — Content synthesis as a dual-use capability
- Disinformation — Potential harmful application of dual-use technologies