Abstract: Techniques for optimizing a machine learning model. The techniques may include obtaining multiple predictions from a machine learning model, the predictions being based on at least one input feature vector, each input feature vector having one or more vector values; creating at least one slice of the predictions based on at least one vector value; determining a sensitive bias metric for the slice based on a sensitive group; determining a base metric for the slice based on a base group; determining a parity metric for the slice based on a ratio of the sensitive bias metric and the base metric; and optimizing the machine learning model based on the parity metric.