Overview

Rather than thinking of fairness as a separate initiative, it’s important to apply fairness analysis throughout the entire process, making sure to continuously re-evaluate the models from the perspective of fairness and inclusion. The use of Model Performance Management tools or other methods should be considered to identify and mitigate any instances of intersectional unfairness. For example, a diversity rating audit that combines various attributes including age, gender, and ethnicity can be used to audit data sets used to train AI algorithms.