Skip to content
Apart Research
Apart Research
Contact us
Sprint projectNov 2, 2025Dubai

AI Risk Hotspots: Early Warning System

Ismail Khan Mohammad · Team dubAI

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

Read the report

Report: AI Risk Hotspots: Early Warning System

Open in a new tab (opens in new tab)
Presentation

Presentation: AI Risk Hotspots: Early Warning System

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

AI Risk Hotspots Early Warning System, is a data-driven dashboard that provides situational awareness of AI risk. It empirically demonstrates the correlation between the growth in AI training compute and the rise of real-world harm. The system analyzes historical incident data to identify, track, and forecast the fastest-accelerating harm categories. It successfully identifies 'Malicious Use & Security' as the #1 current hotspot and provides a 3-year forecast for its trajectory. Based on this data, I propose concrete mitigation strategies like C2PA watermarking and stricter API access controls. This tool transforms the abstract discussion of AI risk into a tangible, measurable, and forecastable reality.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

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?

No public critique yet.

Cite this project

@misc{mohammad2025ai,
  title = {{AI Risk Hotspots: Early Warning System}},
  author = {Ismail Khan Mohammad},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/ai-risk-hotspots-early-warning-system-qtu1}},
  url = {https://apartresearch.com/sprints/projects/ai-risk-hotspots-early-warning-system-qtu1}
}

More from The AI Forecasting Hackathon

All 34 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026