Model Discrimination

Model discrimination focuses on identifying and mitigating biases in predictive models, ensuring fair and accurate predictions across different subgroups. Current research emphasizes data-driven approaches, including optimization algorithms and causal inference techniques, to detect and correct for biases arising from various sources like label selection or heterogeneous data distributions. This work is crucial for improving the reliability and fairness of machine learning models across diverse applications, ranging from clinical decision support to autonomous systems, by providing methods to identify and address model discrimination at both the group and individual instance levels. The ultimate goal is to build more robust and equitable models that avoid perpetuating existing societal biases.