AI for Good Lab Hackathon: United Arab Emirates

Problem Statement

The objective of the ITU AI for Good Lab hackathon is to harness crowdsourced inputs and actively involve vast communities, including students, researchers, and professionals, in building a collaborative and open knowledge base that enables cross-domain and cross-national AI agent development thanks to the leadership and coordination of the UAE. The use of AI agents is aimed at demonstrating the following:

1. Application domains: enabling domain specific application use cases,
2. AI strategies, policies and regulations: identifying the interplay between AI strategies, policies and regulations on one side and innovative applications on the other side. Arriving at the best possible domain-specific policy recommendations based on global learnings and best practices.
3. The need to study the behaviour and performance of Agentic solutions when faced with real life application and regulatory scenarios.

There is a growing need to understand how AI agentic solutions would function in various domains such as healthcare, agriculture and public services. This involves both technology and strategies and innovations. This hackathon challenges participants to build the foundational infrastructure — country-specific knowledge bases and AI agents — that will power realistic, policy-grounded applications.

This hackathon provides an opportunity for participants to collaborate globally in creating a shared AI knowledge infrastructure, contribute to a meaningful governance tool for the host country, and win exciting prizes.

This hackathon provides opportunities to countries around the world to contribute to the open knowledge base, creating a shared asset, under the leadership of UAE. The participants from various countries would be provided online mentoring towards this contribution. The outcomes from the hackathon would include publication of the open knowledge base in the ITU AI for Good Lab with acknowledgement to UAE.

Requirements for Participants

Participants are required to build and demonstrate an AI agent-based solution using a knowledge base created from use case related documentation and code in the domains such as healthcare, education, telecom, and public services, while adhering to country AI policies, standards and regulations, mapped to global best practices and policies.

Participants are invited to:

1. Create innovative AI solutions, demonstrating the value of AI integration and agentic solutions in the application domains such as healthcare, education, telecom, and public services,
2. Make sure that the solution is mapped to and adhere to the published and open documents provided by the country (including laws and regulations; national AI, digital and cybersecurity strategies; data protection and privacy legislation; institutional mandates and responsibilities; economic and industrial characteristics; labour-market conditions; intellectual property regimes; multilateral commitments and treaty obligations; energy and infrastructure constraints; and other relevant country-specific information)
3. ITU reference code to extract, structure, and ingest data into a vectorized, distributed knowledge base hosted in the ITU AI for Good Lab.
4. Use the cloud credits in the Sandbox to fine-tune AI models and create AI agents capable of one or more of the following roles:

• • Innovative AI solution – an agent which uses the knowledge base and demonstrates unique value, solving problems in the domains.
  • Policy and legal adviser — an agent that participants can query during a simulated error scenarios in applications, while adhering to applicable legislation, institutional responsibilities, emergency powers, international obligations, and sector-specific impacts. The recommendations for handling of scenarios could be based on global best practices and policy examples. Gaps and mappings to the global best practices may be described by the agent.
  • Institutional participant — an agent that assumes the role of a specific government actor (e.g., regulator, technical adviser, ministry) and contributes information or recommendations during the simulation.
  • Multi-agent coordination — multiple agents representing different institutional roles (e.g., domain specific application agent, cross domain responsibilities such as health care, agriculture, education, innovations, security, telecommunications regulator, data-protection authority), each receiving different information, operating under different mandates, and producing independent assessments. Inter-agent coordination protocols should orchestrate the interaction.

5. Design and administer interactive error scenarios with sequenced, chunked events released progressively (not all at once), reflecting realistic conditions where information is incomplete, unevenly distributed, and received at different times by different agents.

6. Implement audit logging and chain-of-thought tracing so that agent decisions, reasoning, and policy references are transparent and reviewable.

The AI solutions developed should be aligned with ITU-T Y.3172 and reflect the ITU AI Readiness Framework from (including but not limited to) the perspectives below:

• Open data and open-source management
• Generative AI content ecosystem
• Contextualization of AI solutions and regional development focus
• AI integration in domains and cross-domain analysis
• Human interaction and supervision in AI
• AI policy and implementation opportunity identification
• AI for social inclusion
• Digital infrastructure and energy considerations

Evaluation Criteria

Solutions will be evaluated on:

• Knowledge base quality — completeness, accuracy, and structure of ingested national material; ability to retrieve relevant policy context under query
• Agent capability — relevance and accuracy of agent responses during simulated error scenarios; ability to identify applicable solutions, while adhering to laws, mandates, and coordination mechanisms. Policy, strategy, standards gap identification — quality of insights surfaced: unclear responsibilities, standards, technology and legislative gaps, coordination problems, conflicts between objectives. In addition to the country level policies, the relevance of the gaps and accuracy of the global policy examples would be a critical part of the evaluation.

NOTE – Rather than analysing specific geographies or country specific recommendations, participants should focus on domain specific policy gaps and finding relevant global policy examples.

• Auditability — transparency of agent reasoning through logs, chain-of-thought traces, and source attribution
• Alignment with ITU standards — reflection of Y.3172 pipeline and AI Readiness Framework in the solution architecture

Submission Materials

1. Technical Report (Max 5 pages) (download the template here)
2. Github repository
3. Knowledge base containing policies, regulations, and strategies (Reference code for KB creation: GitHub – CrashingGuru/ITUAIReadiness: This repo contains code for training for ITU AI Readiness · GitHub)
4. Optional: Demo video (max 7 minutes)

Note: demo video will be required for shortlisted teams after first round evaluation

Benefits of Participation

• Access to online expert mentoring sessions
• Access to ITU AI for Good Lab and sandbox— for selected hackathon participants
• Cross-domain collaboration with experts in industry, academia, and policymaking
• Hands-on experience building AI agents for real-world AI applications
• Deep dive into local AI integration priorities and practical contextualization of AI solutions
• Contribution to a tool that will be used by national stakeholders, innovators, and senior officials in actual policy exercises
• Awards and certificates given by dignitaries from the host country
• Opportunity for winning solutions to be deployed in the 2027 Summit live multi-country error exercise
• Sponsorship for travel is provided for oversea selected teams.

Awards and Certificates

The following awards would be given:

• First grand prize — 1500 CHF
• Second prize — 1000 CHF
• Third prize — 600 CHF
• Special prize for Women’s Team — 600 CHF
• Judges’ prize for teams — 1000 CHF

The team should be a maximum of 4 people. For example: 4 participants or 3 participants + 1 internal mentor. Only teams with a woman leader are eligible for the Special Prize for Women’s Team. All participants who submit valid solutions will receive a certificate of participation. A maximum of one prize will be awarded per team.

Awards would be distributed by UAE dignitaries.

Judges’ prize is awarded based on the level of engagement, future potential in terms of research, development, innovation, or standards potential, as determined by the judges’ panel.