A key component of building trusted AI is ensuring alignment with your ethics and values. In DataRobot AutoML, bias and fairness testing allows you to flag protected features in your dataset and then actively guides you through the selection of the best fairness metric to fit the specifics of your use case. Once your models are built, DataRobot surfaces visual insights to illustrate the results of the selected bias and fairness test. If bias is identified, you can use the Cross-Class Data Disparity tool to perform root cause analysis, diagnosing the source of bias in your data, and ultimately directing you towards mitigation steps in your data collection or processing.
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