Fairness Measure

Fairness measures in machine learning and decision-making aim to quantify and mitigate bias, ensuring equitable outcomes across different demographic groups. Current research focuses on developing new metrics that account for uncertainty in predictions, handle multiple sensitive attributes, and address the incompatibility between various fairness criteria, often employing manifold-based approaches or statistical equivalence testing. These advancements are crucial for building trustworthy AI systems and promoting fairness in high-stakes applications like loan approvals, hiring processes, and biometric authentication, ultimately impacting the ethical deployment of AI in society.