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Challenge instructions

Instruction and application
In Progress

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.

Hackathon Resource Pack​

If you haven't already, or if you need to re-download it, download the Hackathon Resource Pack now. It contains:

  • Descriptions of the two scenarios.
  • Datasets for each scenario.
  • Presentation template to capture your thinking throughout the hackathon. AIMLF Hackathon Resource Pack.zip

Once downloaded, open the files and keep them accessible for the rest of the hackathon.

### Instructions

Refine data and pre-processing decisions​

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.

Focus on justifying choices, not implementing them. You canreview the following learning content to support your work for this step:

Refine modelling and evaluation approach​

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.
  • Identify limitations or sources of uncertainty.

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 of incorrect predictions and mitigations.

Governance should be treated as a core design element, not an add-on. To support your work for this step, you canreview Module 3 content on the key ethical principles in AI and ethical frameworks for AI development.

Execution and communication​

Consider how the solution would be taken forward in practice:

  • Who needs to be involved (technical, business, governance).
  • How decisions, progress, and outcomes would be communicated.
  • What the immediate next steps would be after the hackathon.
  • What would be required to move toward a proof of concept.

To support your thinking for this step, you can review the Module 2 content on stakeholder management in ML projects and aligning stakeholders and communicating solutions.

Looking ahead to the wrap up workshop​

In the wrap-up workshop, you'll bring this work together by preparing and delivering your presentation, clearly communicating your solution, key decisions, and recommended next steps.

In progress