Research / Sources
Research on governing advanced AI and its risks. www.governance.ai
Characterises how AI agents and LLMs could flood government services with demand, assesses which services are most exposed, and maps government responses and their trade-offs for equitable access.
Proposes agent-permissions.json, a robots.txt-style manifest letting websites specify allowed LLM web-agent interactions, to enable compliant agent access while respecting site owners' preferences.
Argues that EU AI Act Article 50 likely requires labeling AI agents' web requests and browser actions, with verifiable labels, to help humans distinguish AI activity from genuine human opinion.
Survey of local US policymakers in 2022 and 2023 finds mixed views on AI's impacts, majority support for government oversight and AI safety policies, and a partisan gap in regulatory support.
A GovAI white paper on how Global AI Majority countries can gain voice in and access to AI, weighing dual-use security concerns with proposals for interest alignment and safety assurance.
Assesses agent identifiers, real-time monitoring and activity logging as ways to increase visibility into AI agents, analysing their governance value, implementation trade-offs and privacy and power-concentration implications.
Argues that societies should reduce the negative impacts of advanced AI through adaptation, not only by restricting development and diffusion, and offers a framework, examples, and recommendations.
Conceptual paper distinguishing direct alignment (serving an operator's goals) from social alignment (externalities on society), arguing social alignment requires governance and new norms for AI systems.
A survey of 524 ML researchers finds strong support for prioritizing AI safety research and pre-publication harm review, with low trust in military and some companies, and strong opposition to lethal autonomous weapons.