Fairness-by-Design Machine Learning Workflow for Responsible AI Decision-Making

AI Ethics, Fairness in Machine Learning, Responsible AI, Explainable AI, Machine ethics

My research focuses on developing a fairness-by-design machine learning workflow for responsible AI decision-making, with particular attention to educational and social data systems. Many machine learning models are evaluated mainly through predictive accuracy and utility, but this alone does not ensure that the model treats different groups equitably, remains interpretable, or can be responsibly used in institutional decision-making. I investigate how fairness metrics, model performance, interpretability, and governance constraints can be integrated into a single practical ML workflow. This work contributes to AI ethics by connecting normative concerns about fairness and accountability with operational machine learning practices that researchers, developers, and institutions can apply in practice.