Master thesis project: Evaluating Fairness Interventions in Machine Learning

Research goal As machine learning systems are increasingly used to support decision-making, ensuring fair outcomes has become an important challenge. This thesis will investigate and compare different approaches for mitigating unfairness across the machine learning lifecycle, including data preparation, model development, and prediction adjustment stages. The research will assess how various fairness interventions affect both model performance and fairness metrics, using representative datasets and practical use cases. The objective is to provide insights into the strengths, limitations, and trade-offs of different techniques, and to develop recommendations for the responsible design of machine learning systems.…