arXiv:2607.08077v1 Announce Type: new Abstract: AI developers face a dual-use dilemma. An AI capability that helps one user cure a disease can help another synthesize one. This dilemma could be resolved with access control, limiting dual-use AI capabilities to trusted deployments with a legitimate need. A gold standard for access control would be to serve separate models with different capabilities to different users. However, training and deploying multiple models is prohibitively expensive. To address this challenge, we propose gradient-routed auxiliary modules (GRAM), a pre-training method
The increasing sophistication and dual-use potential of AI models necessitate robust access control mechanisms to mitigate risks and ensure responsible deployment.
This development addresses the critical dual-use dilemma in AI, offering a technical solution to serve different capabilities to different users without prohibitive costs, thus enhancing security and ethical AI development.
AI developers can now more practically implement fine-grained access control for specialized AI capabilities, leading to more secure and segmented AI deployments.
- · AI developers
- · National security agencies
- · Regulators
- · Ethical AI initiatives
- · Malicious actors
- · Those seeking to exploit dual-use AI
- · Developers unable to implement such modular pre-training
Widespread adoption of modular pre-training methods could lead to more secure and controllable AI systems.
This could enable more permissive deployment of advanced AI in sensitive applications, fostering innovation while managing risk.
The ability to granularly control AI capabilities may lead to new models for AI-as-a-service, segmenting access based on security clearances or legal frameworks.
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Read at arXiv cs.LG