Model Understanding With Azure Machine Learning - AI Summary

Data scientists and model evaluators – At training time to help them to understand their model predictions and assess the fairness of their AI systems, enhancing their ability to debug and improve models. Model performance tab: With the predefined female and male cohorts, we can observe the different prediction distributions between males and female cohorts, with females experiencing higher probability rates of being rejected for a loan. We sort our top feature importances by the Female cohort, which indicates that while the feature for "Sex" is the second most important feature to contribute towards the model's predictions for individuals in the female cohort, they do not influence how the model makes predictions for individuals in the male cohort. The dependence plot for the feature "Sex" also shows that only the female group has positive feature importance towards the prediction of being rejected for a loan, whereas the model does not look at the feature "Sex" for males when making predictions. The original fairness dashboard also enables the comparison of multiple models, such as the models produced by different learning algorithms and different mitigation approaches.