Predicting landslides requires data-driven modeling of geological, climatological, and topographical signals. This Quest entails an in-depth responsible and performance audit of an existing landslide AI pipeline—checking coverage for atypical slope profiles, interpretability tools, and disclaimers for data-limited or indigenous terrains. The design fosters a robust approach, from verifying the model’s geostatistical assumptions to ensuring locally relevant disclaimers.
Experts may incorporate advanced interpretability frameworks (like post-hoc Saliency Maps or Grad-CAM), multi-ensemble verification (where multiple models converge on a single risk classification), or spatiotemporal indexing. This merges domain-based geotechnical knowledge with a thorough RRI approach that acknowledges rural or indigenous constraints.
Key Outputs
- AI Audit Report: Summarizing performance metrics, interpretability checks, and coverage biases.
- Update Proposals: Code snippet or config adjustments to handle underrepresented slope categories.
- Ethical & Social Disclaimer: Short note capturing potential negative externalities (like false alarms or ignoring smaller slope events).
10 Steps
- Model Retrieval: Download the landslide AI pipeline from the repository
- Data Context: Examine training coverage, focusing on varied topography (steep vs. moderate hills, deforested vs. forested zones)
- Geostatistical Screening: Compare model labeling to known landslide archives, identifying mismatch patterns (earns initial eCredits)
- Interpretability Check: Use advanced interpretability methods (SHAP, LIME, or CAM-based overlays) to see which features drive predictions
- Bias & Gaps: Determine if certain subregions or geologic profiles are systematically undervalued or missing
- Peer Collaboration: Post partial results to a domain forum, awarding partial participation credits for group validation
- Refine Model: If feasible, tweak hyperparameters or add extra geospatial layers (soil composition, drainage density)
- RRI Overlay: Summarize disclaimers for local usage—like disclaimers if the model fails beyond a certain slope threshold or forest cover ratio
- Re-Test & Summarize: Generate updated performance metrics with disclaimers on borderline cases
- Integrate & Validate: Earn final partial or full validation credits once your revised pipeline or disclaimers pass a multi-review acceptance