Programming 'Fairness' into Your Machine Learning Model

To combat ethical risk proactively without sacrificing model performance, we must first define'fairness'. A model is considered'fair' if it gives similar predictions to similar groups or individuals. In more detail, a model is'fair' if for both groups of the positive outcome, the predictor has equal true positive rates and equal false positive rates for the negative outcomes. Next, we can break up our bias detection and mitigation techniques into phases -- the same phases that govern the development of an AI model: Data understanding & preparation; model development & post-processing; and model evaluation & auditing.