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Challenge instructions

Workshop
Complete

To strengthen your AI/ML solution, you'll now work on refining your data and modelling approach, defining how success will be evaluated, and addressing governance, ethical, and practical considerations.

Download: Hackathon Resource Pack​

If you haven’t already, download the pack now. It contains descriptions of the scenarios,datasets, and thepresentation template.

Workshop 2 goals​

Refine your technical design, address governance and ethics, and prepare your final presentation.

Instructions illustration

Instructions​

1. Refine data and pre-processing​

Build on your initial thinking by refining:

  • Data access, quality, and readiness.
  • Pre-processing steps and feature engineering choices.
  • How these decisions affect feasibility and performance.

2. Refine modelling and evaluation​

Deepen your modelling and evaluation design:

  • Narrow down your preferred model approach.
  • Explain trade-offs (e.g. accuracy vs explainability).
  • Clarify how evaluation links back to the business success criteria.

3. Governance and ethical considerations​

Design how the solution would be responsible and defensible:

  • Data governance: access, storage, and compliance.
  • Ethical risks: bias, fairness, transparency, and misuse.
  • Potential negative impacts and mitigations.

4. Execution and communication​

Consider practical implementation:

  • Who needs to be involved (technical, business, governance).
  • How progress and outcomes would be communicated.
  • Immediate next steps after the hackathon.

Presentation preparation​

Using the template, prepare a clear and focused presentation that:

  • Walks through your proposed AI/ML solution end to end.
  • Explains key decisions, assumptions, and trade-offs.
  • Addresses governance, ethics, and stakeholder considerations.
  • Recommends realistic next steps.

Submit presentation on next page​

All presentations must be submitted before the deadline agreed with your instructor.