Uncertainty Aware Fairness

Uncertainty-aware fairness in machine learning aims to address biases in model predictions by explicitly considering the uncertainty associated with those predictions, rather than relying solely on point estimates. Current research focuses on developing new fairness metrics that incorporate aleatoric and epistemic uncertainty, and on designing algorithms (like quantile regression and variational methods) that improve both prediction accuracy and fairness while providing reliable uncertainty quantification. This work is crucial for building trustworthy AI systems, particularly in high-stakes applications, by providing a more nuanced understanding of model biases and enabling more robust and equitable decision-making.