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Limitations of SHAP-based interpretations in environmental and membrane filtration applications
Summary
Researchers critically analyze the use of SHAP values — a machine learning interpretability method — in microplastic filtration studies, arguing that SHAP's dependence on model assumptions and inability to handle correlated variables can produce misleading conclusions about which process parameters matter most in complex environmental systems.
Maliwan et al. (2025) identified key parameters in microplastic ultrafiltration using interpretable machine learning (SHAP), attributing 57.6-70.6 % feature importance to factors like transmembrane pressure. This paper critically examines their methodological approach, highlighting significant concerns regarding SHAP's application. SHAP values are inherently model-dependent and lack ground truth for validating feature importance accuracy, leading to potentially biased and erroneous conclusions; high prediction accuracy does not ensure reliable insights. SHAP's underlying assumptions, particularly feature independence, rarely hold in complex environmental systems characterized by multicollinearity, potentially misattributing variable importance. We advocate for a more robust analytical framework incorporating unsupervised machine learning (e.g., feature agglomeration) and nonlinear nonparametric statistical methods (e.g., Spearman's correlation) to provide more reliable insights into variable relationships, moving beyond model-dependent interpretations.