Fairness Metric

Fairness metrics are quantitative measures used to assess and mitigate bias in machine learning models and other decision-making systems, aiming to ensure equitable outcomes across different demographic groups. Current research focuses on developing new metrics that address limitations of existing ones, particularly concerning uncertainty, data drift, and the imbalanced nature of real-world datasets; this includes exploring various model architectures and algorithms, such as those based on re-weighting, post-processing, and generative models, to improve fairness while maintaining accuracy. The development and application of robust fairness metrics are crucial for building trustworthy and equitable AI systems across diverse domains, impacting both the ethical development of AI and its practical deployment in sensitive areas like healthcare and finance.