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AI x Epistemics Research Sprint

Event artwork for AI x Epistemics Research Sprint
About this event

What to expect

Build practical AI benchmarks, trust infrastructure and decision-support products online from 13–15 November 2026 in Apart Research’s three-day sprint.

The AI x Epistemics Research Sprint examines how AI is changing what people and institutions believe, how they make decisions and how they coordinate. Organised by Apart Research, an independent AI safety research organisation, the event challenges participants to turn open research questions into tangible tools, studies and prototypes that support a healthier information environment.

What you’ll do

Working solo or in a team of up to five, you’ll choose an open problem, develop a project over one weekend and write up your findings. Possible outputs include a benchmark, verification signal, research study or user-facing product. Reading lists, datasets, design sketches and worked examples are provided as starting points, and several suggested projects require no machine-learning skills.

The sprint offers four tracks, including an open track for broader questions in the field. Named areas include:

  • Model epistemics and decision-making evaluations: test whether models understand the strength of their claims, express confidence accurately, oversell evidence or change their position under pressure. Projects may also investigate benchmark gaming and whether honesty scores predict better decisions.
  • Trust infrastructure: explore auditable source-reliability scoring, provenance, tamper-evident records and signals that help people and AI agents check claims and trace their origins. The wider goal is to make verification cheap, routine and difficult to manipulate.
  • Epistemic products: build and assess tools that improve real decisions, for example, evaluating advice users act upon, testing whether chatbots escalate disputes, creating briefings for consequential choices or surfacing disagreements and key cruxes within groups.
  • Open questions: tackle larger meta-level problems surrounding AI, knowledge, coordination and adoption.…

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