Class Fairness

Class fairness in machine learning focuses on ensuring that algorithms perform equally well across different subgroups or classes, mitigating biases that can lead to unfair or discriminatory outcomes. Current research explores methods like adversarial training and data augmentation to improve fairness, but also investigates the limitations of these techniques and develops new approaches such as two-player game formulations and post-processing methods to address fairness violations. This work is crucial for building trustworthy AI systems, particularly in high-stakes applications like autonomous driving and loan applications, where algorithmic bias can have significant real-world consequences. The development of robust fairness testing methodologies is also a key area of focus, enabling the identification and mitigation of biases in existing systems.