As part of the second phase of the TOPML project (Trading Off Non-Functional Properties of Machine Learning), dispositional and situational factors are being investigated in collaboration with Computer Science (Dr. Mattia Cerrato) in the FAIR – COM project, which influence how users understand and evaluate different strategies for bias mitigation – i.e. for reducing biases in AI systems. Empirical user studies will be used to identify psychological and contextual factors that shape the understanding and acceptance of fairness measures. These findings will be incorporated into the development of explainability approaches (Explainable AI, XAI) and the design of user interfaces to communicate fairness-related properties of AI systems in a transparent and understandable way.