Research / Sources
Policy research on advanced AI security and governance. www.iaps.ai
Proposes a standing U.S.-China AI Risk and Incident Dialogue, modelled on the military MMCA channel, to define AI risks, share information, set incident notification procedures and review incidents.
Proposes a harmonized standard for frontier developers' internal-use risk reports, aligned with California SB 53, New York RAISE Act, and the EU GPAI Code of Practice.
Analyses the India AI Impact Summit 2026, covering its focus on demonstrable impact, US-China divergence on global AI governance, and competing visions of AI sovereignty.
IAPS report mapping nine strategic visions for navigating powerful AI, assessing how each addresses misaligned AI takeover, great-power war, and AI-enabled power concentration, and which policy objectives are robust across them.
A IAPS memo analyses the MAIM deterrence argument, in which great powers would sabotage rivals' ASI races, reformulating its claims and assessing the evidence for and against each.
A policy report arguing that competitive pressure could push AI developers away from human-readable chain-of-thought reasoning, undermining monitoring, and proposing governance options to preserve CoT monitorability.
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.
Describes the founding of the China AI Safety and Development Association (CnAISDA), a pivotal body for frontier AI governance in China, and how its domestic and geopolitical challenges may shape frontier AI risk discussions.
A blog post on an Oxford Martin AI Governance Initiative paper examining which actors, such as governments, labs or third parties, should develop which AI model evaluations.
Examines the design of the international AI Summit series, including hosting, secretariat, participant selection, agenda and frequency, and proposes six recommendations to formalise it and strengthen its impact on frontier AI safety governance.
Compares US and Chinese policy approaches to governing general-purpose AI, identifying divergences in regulation focus, principles, and international governance, and their implications for cooperation on AI safety and security.
Commentary on how the International Network of AI Safety Institutes should set priorities, structure membership and coordinate on safety standards, information sharing and evaluations ahead of the November 2024 San Francisco convening.
Identifies Chinese government-linked institutions that could serve as counterparts to the US and UK AI Safety Institutes, based on a systematic review of open sources.
A governance primer analysing the first wave of AI Safety Institutes (UK, US, Japan), comparing their shared characteristics, core functions of research, standards and cooperation, and challenges.
Argues that international scientific assessments of AI risks need a coordinated process among the UK-led report, the UN, the OECD and AI Safety Institutes, and discusses how these actors could work together.
Suggests agenda topics for US-China track II dialogues on advanced AI risks, such as developer best practices, national and international governance, that avoid leaking proliferation-sensitive information.
Argues that public-interest institutions should fund alignment work reducing accident and misuse risks, identifies four ways alignment efforts could backfire, and proposes mitigations.
A report offering best practices for international AI safety dialogues to reduce large-scale risks from advanced AI, including harmful outcomes to avoid and promising safety interventions to discuss.