Ensuring Fairness in Machine Learning to Advance Health Equity

Health domain has witnessed an increase in use of machine learning (ML) technology which follows the saying “garbage in garbage out”. Factors such as human and structural biases at various stages of data collection, model design and prediction interpretation have all accounted for widening the gap of health disparity among protective groups (such as African-American.)