Performance Disparity

Performance disparity in artificial intelligence focuses on identifying and mitigating discrepancies in model performance across different demographic groups or data subsets. Current research emphasizes developing methods to detect these disparities, often using techniques like Conditional Value-at-Risk testing and Slice Discovery Methods, and exploring their root causes, such as biases in training data, model architecture limitations, and the presence of spurious correlations. Understanding and addressing these disparities is crucial for ensuring fairness, reliability, and equitable access to AI-powered systems across diverse populations, impacting fields ranging from healthcare and image recognition to speech processing and multilingual natural language processing.