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In November 2023, the UK and US announced the creation of their AI Safety Institutes (AISIs). Five other jurisdictions have followed in establishing AISIs or similar institutions, with more likely to follow. While there is considerable variation between these institutions, there are also key similarities worth identifying. This primer describes one cluster of similar AISIs, the "first wave," consisting of the Japan, UK, and US AISIs. First-wave AISIs have several fundamental characteristics in common: they are technical government institutions, have a clear mandate related to the safety of advanced AI systems, and lack regulatory powers. Safety evaluations are at the center of first-wave AISIs. These techniques test AI systems across tasks to understand their behavior and capabilities on relevant risks, such as cyber, chemical, and biological misuse. They also share three core functions: research, standards, and cooperation. These functions are critical to AISIs' work on safety evaluations but also support other activities such as scientific consensus-building and foundational AI safety research. Despite its growing popularity as an institutional model, the AISI model is not free from challenges and limitations. Some analysts have criticized the first wave of AISIs for specializing too much in a sub-area and for being potentially redundant with existing institutions, for example. Future developments may rapidly change this landscape, and particularities of individual AISIs may not be captured by our broad-strokes description. This policy brief aims to outline the core elements of first-wave AISIs as a way of encouraging and improving conversations on this novel institutional model, acknowledging this is just a simplified snapshot rather than a timeless prescription.

As AI systems become more advanced, concerns about large-scale risks from misuse or accidents have grown. This report analyzes the technical research into safe AI development being conducted by three leading AI companies: Anthropic, Google DeepMind, and OpenAI. We define safe AI development as developing AI systems that are unlikely to pose large-scale misuse or accident risks. This encompasses a range of technical approaches aimed at ensuring AI systems behave as intended and do not cause unintended harm, even as they are made more capable and autonomous. We analyzed all papers published by the three companies from January 2022 to July 2024 that were relevant to safe AI development, and categorized the 80 included papers into nine safety approaches. Additionally, we noted two categories representing nascent approaches explored by academia and civil society, but not currently represented in any research papers by these leading AI companies. Our analysis reveals where corporate attention is concentrated and where potential gaps lie. Some AI research may stay unpublished for good reasons, such as to not inform adversaries about the details of security techniques they would need to overcome to misuse AI systems. Therefore, we also considered the incentives that AI companies have to research each approach, regardless of how much work they have published on the topic. We identified three categories where there are currently no or few papers and where we do not expect AI companies to become much more incentivized to pursue this research in the future. These are model organisms of misalignment, multi-agent safety, and safety by design. Our findings provide an indication that these approaches may be slow to progress without funding or efforts from government, civil society, philanthropists, or academia., Comment: Sep-25 update adds papers that we missed in the original dataset, as well as more acknowledgements

AI alignment work is important from both a commercial and a safety lens. With this paper, we aim to help actors who support alignment efforts to make these efforts as effective as possible, and to avoid potential adverse effects. We begin by suggesting that institutions that are trying to act in the public interest (such as governments) should aim to support specifically alignment work that reduces accident or misuse risks. We then describe four problems which might cause alignment efforts to be counterproductive, increasing large-scale AI risks. We suggest mitigations for each problem. Finally, we make a broader recommendation that institutions trying to act in the public interest should think systematically about how to make their alignment efforts as effective, and as likely to be beneficial, as possible., Comment: Update Dec-15: Added a missing acknowledgement and fixed minor formatting errors