Harnessing interpretable machine learning: SHapley additive exPlanations (SHAP)-driven insights, transformative impact, and controversies in adsorption-based environmental remediation

Key Points

• Interpretable machine learning techniques enhance insights into adsorption effectiveness, related to environmental remediation.
• The focus on SHAP highlights its role in improving our understanding of machine learning models applied to remediation processes.
• Assessment includes controversies surrounding the use of machine learning in environmental contexts and its implications for best practices.
• Understanding SHAP's contributions may enable better decision-making in adopting machine learning for environmental solutions.