Description: Statistical parity is an equity criterion that seeks to ensure that the results of a statistical model are equal for different demographic groups, such as race, gender, or age. This concept is based on the premise that all individuals, regardless of their membership in a specific group, should receive equitable treatment in automated decision-making. Statistical parity serves as a fundamental pillar in the development of artificial intelligence (AI) and machine learning systems, where bias can arise from historical data that reflects social inequalities. By implementing statistical parity, the aim is to mitigate these biases, ensuring that models do not favor one group over another. This means that, for example, if a model predicts the likelihood of an individual obtaining a loan, the approval rate should be similar across different groups, thus avoiding discrimination. Statistical parity not only focuses on equal outcomes but also promotes transparency and accountability in the use of algorithms, which is crucial in a world where automated decisions significantly impact people’s lives.
