  Earthquake-induced liquefaction can cause substantial lateral spreading,
posing threats to infrastructure. Machine learning (ML) can improve lateral
spreading prediction models by capturing complex soil characteristics and site
conditions. However, the "black box" nature of ML models can hinder their
adoption in critical decision-making. This study addresses this limitation by
using SHapley Additive exPlanations (SHAP) to interpret an eXtreme Gradient
Boosting (XGB) model for lateral spreading prediction, trained on data from the
2011 Christchurch Earthquake. SHAP analysis reveals the factors driving the
model's predictions, enhancing transparency and allowing for comparison with
established engineering knowledge. The results demonstrate that the XGB model
successfully identifies the importance of soil characteristics derived from
Cone Penetration Test (CPT) data in predicting lateral spreading, validating
its alignment with domain understanding. This work highlights the value of
explainable machine learning for reliable and informed decision-making in
geotechnical engineering and hazard assessment.
