AI-Driven Frameworks for Ethical and Efficient Management of Surrogacy in India

Abstract

Based on artificial intelligence, the paper proposes a framework for ethically and effectively controlling the process of Surrogacy in India, which includes statistical analysis, machine learning, fairness assessment, and explainable AI techniques. Data were processed using descriptive and inferential statistics in a synthetic data set of socio-economic, clinical and procedural variables, and the approval time, maternal health, age and education level were found to be significant predictors of ethical risk. Machine learning (Random Forest and XGBoost) models achieved good predictive performance (ROC-AUC > 0.88), and feature contributions were also given by explainability (SHAP), which helped to make the contribution of features to the model's performance more transparent for policy makers and clinicians. The fairness analysis has showed little bias in relation to income groups, and this has led to an equitable risk analysis and compliance to regulations. The framework provides practical suggestions on refining the approval process, focus on high-risk cases, and improving ethical surrogacy management. Limited availability of data and the fact that regional diversity is not considered represent weaknesses, with future research that should continue to use real-world datasets, federated learning and IoT-based monitoring as risk assessment methods on an ongoing basis. The research adds to AI-assisted governance, policy-making, and ethical decision-making of reproductive healthcare.