Purpose

Teams should identify a clearly defined, real-world problem where Artificial Intelligence or Machine Learning can provide a meaningful and practical solution.

Define a Real-World Problem

1. Identify a genuine problem faced by users, organizations, communities, industries, or public services.
2. Clearly describe who is affected, the existing challenge, and why the problem needs to be addressed.
3. Avoid vague, purely theoretical, or problem statements without a clear real-world use case.

Demonstrate Problem Understanding

1. Clearly define the problem, objectives, expected outcomes, and target users or stakeholders.
2. Explain the current process or existing methods and identify their limitations.
3. Use relevant observations, examples, or available evidence to support the problem definition.

Identify the AI Opportunity

1. Explain why AI/ML is relevant to the identified problem.
2. Identify where AI can improve prediction, classification, detection, recommendation, automation, optimization, or decision support.
3. The problem should require meaningful application of AI/ML rather than using AI only as an add-on.

Define Scope & Expected Outcome

1. Keep the problem sufficiently focused to address within the hackathon timeline.
2. Define measurable or demonstrable outcomes that can be validated through a prototype.
3. Prioritize the core problem and avoid unnecessary features that dilute the solution.

Consider Data & Practical Constraints

1. Identify potential data sources and whether suitable data can realistically be obtained or generated.
2. Consider data quality, availability, privacy, security, and ethical requirements.
3. Consider technical requirements, available resources, cost, operational practicality, and ease of deployment.

Focus on Impact & Scalability

1. Explain the potential benefit to intended users or stakeholders.
2. Consider whether the problem and proposed AI intervention can be expanded to additional users, locations, or use cases.
3. Consider long-term usefulness and sustainable implementation.

Problem Statement Checklist

1. Is the problem real and clearly defined?
2. Is the target user or stakeholder clearly identified?
3. Are the objectives and expected outcomes clear?
4. Is there a meaningful role for AI/ML?
5. Can the solution be demonstrated through a functional prototype?
6. Are data, feasibility, impact, and scalability reasonably considered?