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Ground-Level Governance

AI policy has quickly become a national issue, but how should local governments act? Spend a day building something that a local government could use. Mangrove can match you to peers who share your interests and availability.

  • August 28 to 30, 2026
  • 36 hours
  • Online
Hosted by:

Mangrove

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Mangrove

Finalists

$500 in prizes
  1. First placePaul GreenJulie Green

    Queensland Artificial Intelligence Risk Index (QAIRI)

Prizes

$500

Timeline

  1. Aug 27, 2:00 PM

    Registration closed

  2. Aug 28, 3:00 PM

    Kickoff

  3. Aug 30, 4:35 AM

    Submissions closed

  4. Sep 6, 3:00 PM

    Peer reviews closed

  5. Sep 7, 8:36 AM

    Results published

Tracks

  • Track A

    Prepare

    How should local governments prepare for increasingly capable AI and its impacts?

    Example projects

    • Model AI-driven labor or economic disruption in a specific city.
    • Stress-test critical local services against AI-enabled risks.
    • Build a preparedness playbook with indicators that trigger specific actions
  • Track B

    Measure

    What AI risks can local governments detect & how can they measure them reliably?

    Example projects

    • Build a benchmark for AI systems used in local public services.
    • Create a dataset or dashboard tracking local AI incidents and harms.
    • Develop early-warning indicators for emerging AI risks.
  • Track C

    Govern

    What can local governments meaningfully control, and where can local action fill gaps in broader AI policy?

    Example projects

    • Map local AI chokepoints: procurement, infrastructure, supply chains, public services, or major employers.
    • Evaluate how governments have already responded and what existing policy misses.
    • Design a practical, enforceable intervention that could be demonstrated or adopted now.

Use the arrows, or the left and right keys with the strip focused, to move one card at a time.

Judging

CriterionWeightWhat it means
  1. 30%

    Rigor and honesty

    Assumptions on the surface, methods a reader can check, limitations named

    30%

    Assumptions on the surface, methods a reader can check, limitations named

  2. 25%

    Question and scope

    A specific question, narrow enough to answer in two days

    25%

    A specific question, narrow enough to answer in two days

  3. 25%

    Usefulness

    Could a decisionmaker rule with this?

    25%

    Could a decisionmaker rule with this?

  4. 20%

    Communication

    Followable by a smart person outside the discipline

    20%

    Followable by a smart person outside the discipline

We want every submission, not just finalists, to get thoughtful feedback. Projects will first be reviewed by fellow participants, giving every team fresh eyes on their work. Final awards will be determined by our judges.

Judges

  • Blessing Oluwatosin Ajimoti

    Principal Consultant, Global Impact · Public Digital

    Blessing Oluwatosin Ajimoti is a digital transformation professional. She works with governments and organizations in Africa and the Americas to leverage digital and data for effective public service delivery. Blessing holds a Master of Public Policy from the University of Oxford, and an MBA from the Quantic School of Business and Technology.

  • Yulu (Niki) Pi, PhD

    Research Fellow · University of Warwick

    Dr Yulu Pi is a Research Fellow at the University of Warwick and University of Duisburg-Essen. Her research examines Human-AI interaction, AI governance, and algorithmic accountability. Her work explores AI-assisted decisions in domains such as finance and public services. She collaborates with policymakers, regulators, and civil society organisations to develop AI and AI governance.

  • Donghyun Suh

    Economist · Bank of Korea

    Donghyun Suh is an economist at the Bank of Korea. His research examines how artificial intelligence affects labor markets, with particular interests in organizational change, the meaning of work, and redistribution policy. He holds a PhD in Economics from the University of Virginia.

  • Krystal Pan, M.Sc.

    Founder · Increment AI Lab

    Krystal Pan is a co-founder of the Vancouver AI Safety Hub and founder of Increment AI Lab. Her work focuses on frontier AI safety ecosystem-building and building technical common ground across governance and adjacent fields. She holds an MSc in Computer Science from McGill University and Mila and was previously a Google DeepMind Scholar.

Use the arrows, or the left and right keys with the strip focused, to move one card at a time.

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September 25 to 27, 2026. Register by Sep 24.

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