A map of the AI safety field's problems and agendas, and a request for your ratings

We built aisafetyagendas.com, an interactive map of AI safety research agendas and how they map to different problems in alignment. The rows are 12 core problems, the columns are research areas, and inside you can find 58 research agendas. Each cell is the intersection of a problem and an area: the number tells you how many agendas target that problem, the colour tells you how mature they are. We did a first pass ourselves, using our own judgement, but the first pass is not the point.

Figure 1: The Map at aisafetyagendas.com

The point is that every cell is a question aimed back at the community: is this rating right? You can rate the cells in your area, tell us how familiar you are with it, and read the map as best case, average, or worst case depending on how optimistic you feel. It was built as part of the Safe AI Germany Incubator.

Figure 2: From left to right best, average and worst case rating examples.

The allocation problem

The field cannot see its own resource allocation. Leech and Lynn put it as: "you can't optimise an allocation of resources if you don't know what the current one is". Wentworth goes further, arguing that the memetically successful strategy is to work on easy problems rather than "plausible bottlenecks to humanity's survival". The IAPS Expert Survey gets to a similar place from another direction, warning that funders and researchers concentrate on the most visible work and displace quieter research that matters more.

IAPS highlights as a cause the lack of comparative ranking of research directions because what actually exists are mainly lists that don’t build up: Anthropic's recommended directions, DeepMind's technical AGI safety approach, Redwood's project proposals, the UK AISI Alignment Project's £15M bet on areas it thinks the field underrates, and independent reviewers redoing the exercise every year. Written in April 2025, DeepMind placed itself closest to Anthropic's Core Views, by then two years old, with much more weight on robust training, monitoring and security, and set itself against OpenAI's focus on automating alignment research. How is this disagreement taken into account by the field? These different views aren’t lively contested in official channels.

Plenty of people are already pulling in this direction. The IAPS survey asks specialists wh...

Riemann's Pupil

Riemann's Pupil