The school is focused on teaching and exploring approaches and frameworks, less on presentation of the latest research results. The content of the school is mostly technical – it is assumed the attendees understand current ML approaches such as deep learning, reinforcement learning, and some of the underlying theoretical frameworks.

The intended audience of the school are researchers interested in learning more about the AI alignment topics, PhD students, researchers working in ML/AI outside academia, and talented students.
It is also recommended that the participants have a basic general understanding of the AI alignment problem and technical concepts such as specification gaming, instrumental goals, and interpretability.
This year we also encourage inter-disciplinary researchers interested in applying their domain knowledge in AI alignment to attend, e.g. from evolutionary biology, behavioral economics, or statistical mechanics.

The school consists of lectures and topical series, focused smaller-group workshops and discussions, expert panels, and opportunities for networking, project brainstorming and informal discussions.