Evaluating Algorithmic Fairness of Machine Learning Models in Predicting Underweight, Overweight, and Adiposity Across Socioeconomic and Caste Groups in India: Evidence from the Longitudinal Ageing Study in India

This study evaluates the algorithmic fairness of machine learning models used to predict underweight, overweight, and adiposity outcomes across socioeconomic and caste groups in India, using data from the Longitudinal Ageing Study in India. The authors assess disparities in predictive performance across demographic subgroups and examine whether algorithmic models exacerbate or mitigate existing inequalities. The findings highlight the importance of fairness-aware evaluation frameworks in digital health applications to ensure equitable deployment of machine learning tools in diverse populations.