Navigating Governance, Bias, and Interpretability in Machine Learning Deployments for Chronic Liver Disease: A Comprehensive Framework for Algorithmic Transparency and Ethical Risk Stratification

• Billy Elly

  Lautech

  Author

Chronic liver disease (CLD) represents a growing global health burden, yet the deployment of machine learning (ML) for risk stratification in hepatology has been constrained by persistent challenges in algorithmic transparency, governance, and bias mitigation. While ML models have demonstrated promise in predicting disease progression and mortality, existing approaches often operate as opaque "black boxes" that limit clinical adoption and raise concerns about equitable performance across diverse patient populations. This study addresses the critical gap between predictive accuracy and clinical implementability by developing a comprehensive framework that integrates governance protocols, bias detection mechanisms, and interpretability tools within a unified ML deployment architecture for CLD risk stratification. Using a retrospective cohort design with publicly available clinical datasets, we implemented and evaluated multiple ML architectures including ensemble methods (Random Forest, XGBoost, LightGBM) with integrated SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) for model transparency . The proposed framework achieved a predictive accuracy of 89.4% for 12-month CLD progression, with SHAP-based feature attribution identifying bilirubin, albumin, and platelet count as the most influential predictors. Crucially, the framework's fairness monitoring component detected and corrected a 4.2% performance disparity across demographic subgroups that would have remained hidden in standard model evaluation. The framework provides a replicable blueprint for ethically responsible ML deployment in hepatology, demonstrating that high predictive performance can be achieved without sacrificing transparency or equity. These findings have direct implications for clinicians seeking to implement ML-based decision support and for policymakers developing regulatory standards for algorithmic accountability in healthcare.

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Copyright (c) 2026 Billy Elly (Author)

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