The Hidden AI Frontier
Argues that frontier AI models kept hidden inside corporate labs pose dual-use and security risks, and proposes transparency and oversight measures for these privately held systems.
Research
IAPS
Policy research on advanced AI security and governance. www.iaps.ai
Research Report (25)Compute governance (23)Frontier Security (22)International governance & China (18)Policy and Standards (18)Erich Grunewald (16)Jam Kraprayoon (14)Link Post (14)Oliver Guest (11)Linkpost (10)Joe O'Brien (9)Chris Covino (8)Issue Brief (8)Oscar Delaney (7)Bill Anderson-Samways (6)Responses to RFIs/RFCs (6)Matthew Mittelsteadt (5)Renan Araujo (5)Theo Bearman (5)Blog Post (4)Cara Labrador (4)Shaun Ee (4)Asher Brass (3)International Strategy (3)Onni Aarne (3)Brianna Rosen (2)Clarissa Koh (2)Hamish Low (2)Karson Elmgren (2)Sambhav Maheshwari (2)Sarah Godek (2)Article (1)Cassia King (1)Commentary (1)Dave Banerjee (1)Jan Wehner (1)Request for Information (RFI) Response (1)Sumaya Nur Adan (1)
Focus All
Topic Lab practice
Source IAPS · 4
Date Any time
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Responsible Reporting for Frontier AI Development
Proposes responsible reporting of safety-critical information from frontier AI developers to government, industry and civil society, with voluntary and regulatory mechanisms for implementing it.
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Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
Argues that external scrutiny, including third-party auditing, red-teaming and researcher access, is needed for public accountability over frontier LLM decisions, and sets out six requirements under the ASPIRE framework with policy recommendations.
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Open-Sourcing Highly Capable Foundation Models
Argues that open-sourcing some highly capable foundation models may pose extreme risks outweighing benefits, and surveys alternative model-sharing strategies with policy recommendations for developers, standards bodies and governments.