Exploring the impact of fairness-aware criteria in AutoML

ArXi:2604.10224v1 Announce Type: cross Machine Learning (ML) systems are increasingly used to decision-making processes that affect individuals. However, these systems often rely on biased data, which can lead to unfair outcomes against specific groups. With the growing adoption of Automated Machine Learning (AutoML), the risk of intensifying discriminatory behaviours increases, as most frameworks primarily focus on model selection to maximise predictive performance.