AI/ML Code Sprint for DRR and DRI

This Quest merges a short AI/ML code sprint with an advanced Work-Integrated Learning Path (WILP). Participants implement or enhance an AI/ML pipeline targeting a recognized DRR or DRI issue—such as climate-driven vector-borne disease spikes, population displacement predictions, or multi-hazard synergy forecasting. The emphasis is on building an ethically grounded solution with disclaimers around interpretability, data constraints, and local context.

Key Outputs

1. AI/ML Pipeline: A containerized or script-based solution addressing a targeted DRR or DRI challenge
2. Performance & RRI Metrics: Documenting how well the model handles real or test data, plus disclaimers for underrepresented areas
3. WILP Module Completion: Confirming participant’s advanced learning milestone, bridging theory and practice

11 Steps

• WILP Enrollment: Officially join or confirm your advanced AI track, awarding initial eCredits for commitment
• WILP Enrollment: Officially join or confirm your advanced AI track, awarding initial eCredits for commitment
• Data Collation: Gather relevant time-series or geospatial sets from recognized open data archives, ensuring advanced container-based ingestion is allowed
• Model Architecture Sketch: Outline your approach (temporal RNN, ensemble-based random forest, or advanced gradient boosting) and desired outcome metrics
• Initial Training: Use partial data subsets to refine hyperparameters, tracking results in a local or cloud environment
• Interpretability Tools: Apply partial post-hoc checks (like integrated Gradients, LIME) to highlight which features dominate predictions, awarding partial pCredits for advanced ML
• Local Stakeholder Feedback: If feasible, present an alpha model demonstration to relevant community, verifying practical acceptability and disclaimers
• Refinement: Incorporate feedback from both domain experts and WILP mentors, adjusting the pipeline or disclaimers
• Peer Validation: Post your final model code for cross-check in the platform forum, awarding partial validation credits if sufficiently improved
• WILP Reflection: Write a short concluding note describing lessons, model performance achievements, and RRI-based disclaimers
• Integration & Acknowledgment: Merge your code, disclaimers, and WILP reflections into the official DRR/DRI library, awarding final credits once fully approved