Machine Learning Fairness

Machine learning fairness focuses on developing and deploying algorithms that avoid discriminatory outcomes across different demographic groups, aiming to ensure equitable treatment regardless of sensitive attributes. Current research emphasizes methods for quantifying and mitigating the fairness-accuracy trade-off, exploring techniques like self-supervised learning to improve model fairness and developing approaches that provide uncertainty quantification for fairness assessments. This field is crucial for building trustworthy AI systems, impacting various applications by promoting ethical and responsible use of machine learning in high-stakes decision-making processes.