Statistical Parity

Statistical parity, a fairness metric in machine learning, aims to ensure that algorithms' outputs are equally distributed across different demographic groups, preventing discriminatory outcomes. Current research focuses on addressing limitations of statistical parity, such as its incompatibility with other fairness metrics and its potential disregard for actual welfare consequences, exploring alternative approaches like social welfare optimization and causal fairness frameworks. This work is crucial for mitigating algorithmic bias and promoting equitable outcomes in various applications, driving advancements in both theoretical understanding and practical implementation of fair machine learning.