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Sprint projectNov 2, 2025Denmark, Ghana

EU ForecastHUB

Ewura Ama Sam, Balázs Laszlo · Team EU-ForecastHUB

Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: EU ForecastHUB

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EUForecast-Hub is a modular forecasting platform designed to advance transparent forecasting to benefit European policy decisions in the years ahead. It integrates Bayesian inference (dynamic Bayesian networks) and natural-language processing (LLaMA integration) to enable users (from researchers to policymakers) to construct effective forecasting models without coding experience. It is intended to act as a novel approach for mapping causal dependencies and quantifying uncertainty in policy and AI-related scenarios. Namely, users can simulate “what-if” forecasts across multiple domains, such as climate-driven migration and AI capability scaling—linking social, economic, and technological indicators in a probabilistic graph. Strategically, EUForecast-Hub’s features support informed decision-making on Europe’s path toward safe and equitable AI development.

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Does the project meaningfully advance AI timeline prediction and capability forecasting? Does it clearly connect to measurable indicators of AI progress (compute, benchmarks, economic impacts, automation milestones)? Does it build on or challenge existing forecasting frameworks like biological anchors, scaling laws, or scenario planning? Does it offer novel methodologies, data sources, or empirical insights that could improve forecast accuracy? Is it grounded in observable trends rather than pure speculation?

Does this project inform critical decisions about AI development and preparedness? Does it help identify key uncertainties, decision points, or early warning indicators? How well does the project connect technical metrics to real-world impacts and policy needs? Could the output guide resource allocation, safety research priorities, or regulatory timelines? Does it reduce uncertainty around transformative AI milestones or capability emergence?

Is the project methodologically rigorous, reproducible, and technically sound? Is the forecasting approach well-calibrated with appropriate uncertainty quantification? Are the data sources, assumptions, and limitations clearly documented? Does the project demonstrate sound statistical methodology and honest treatment of model uncertainties? Would the tool, model, or framework be useful for ongoing forecasting efforts, research planning, or policy analysis?

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Cite this project

@misc{sam2025eu,
  title = {{EU ForecastHUB}},
  author = {Ewura Ama Sam and Balázs Laszlo},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/eu-forecasthub-6wiz}},
  url = {https://apartresearch.com/sprints/projects/eu-forecasthub-6wiz}
}

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