Accuracy Disparity

Accuracy disparity, the unequal performance of machine learning models across different demographic groups or data classes, is a critical concern in AI fairness research. Current efforts focus on developing methods to mitigate this disparity, including novel loss functions that directly optimize for balanced accuracy across groups and techniques to address biases stemming from data representation, model architecture, and training processes. These investigations span various applications, from image recognition and face verification to toxicity detection and federated learning, highlighting the pervasive nature of this problem and the need for robust solutions. Addressing accuracy disparity is crucial for ensuring the equitable and trustworthy deployment of AI systems across diverse populations.