Fairness Accuracy

Fairness-accuracy trade-offs in machine learning explore the inherent tension between building accurate predictive models and ensuring those models don't unfairly discriminate against specific demographic groups. Current research focuses on developing algorithms and model architectures (including neural networks and graph neural networks) that mitigate bias through various techniques like sample weighting, personalized federated learning, and post-processing methods, aiming to optimize the balance between these competing objectives. This field is crucial for ensuring responsible AI deployment across diverse applications, impacting areas like criminal justice, healthcare, and finance by promoting equitable and trustworthy algorithmic decision-making. A significant focus is on developing robust methods for evaluating and comparing fairness-accuracy trade-offs across different datasets and fairness metrics.