Fairness metrics

Fairness metrics are critical tools for ensuring that machine learning models are fair and unbiased. These measures allow for the evaluation of classification models and provide insights into whether certain groups are being unfairly favored or discriminated against. Demographic parity and equalized odds are two of the most widely used fairness metrics, both with their own unique approach to measuring fairness. By using these metrics, organizations can better understand how their models perform and take steps to address any biases that may exist.

Demographic parity

Demographic parity is a fairness metric that compares the predictions made between different groups, ignoring the actual true values. This metric is useful in cases where the input data is known to contain biases and the goal is to measure fairness. However, it is important to note that demographic parity only uses the predicted values and discards the information about the true values. It also uses...