Hans Korving - SHAP beyond the standard graphics: co-design of ML-models in earth sciences

Co-design with stakeholders is essential when developing ML models to ensure they are both accurate and trusted, especially in earth sciences applications

Standard SHAP visualizations, while useful for ML experts, often fail to resonate with stakeholders and domain experts who struggle to connect them with real-world context

Combining UMAP dimensionality reduction with SHAP values helps create more interpretable visualizations by projecting high-dimensional data into 2D space

HDBSCAN clustering on UMAP-projected SHAP values creates meaningful groups that stakeholders can better understand compared to clustering on raw feature values

Converting model insights into human-readable if/then rules based on raw feature values (rather than SHAP values) makes the results more relatable to stakeholders’ domain knowledge

Incorporating stakeholder feedback and domain constraints into model development leads to better trust and adoption, even if it means sacrificing some model performance

Visualizing results through maps and relating clusters to physical locations helps stakeholders validate model behavior against their real-world experience

Simple, interpretable models (like linear regression with interactions) are often preferable to complex black-box models that require extensive explanation

Iterative refinement and continuous stakeholder feedback throughout the modeling process helps bridge the gap between ML capabilities and practical applications

The approach was successfully applied to wildfire susceptibility prediction in Italy and ecological quality assessment of streams in the Netherlands