Bias Reduction in Machine Learning Model Training and Inference

A system including one or more processors configured to perform a method for reducing bias in machine learning predictions, the method comprising:

• receiving, by the one or more processors, training data comprising a plurality of training observations, each training observation including (i) a target value, (ii) a plurality of feature values corresponding to a plurality of features, and (iii) at least one protected attribute value corresponding to at least one protected attribute;
• determining, based on the at least one protected attribute value, a plurality of partitions of the training data and a corresponding plurality of partition weights, each partition corresponding to a different protected attribute value or combination of protected attribute values;
• training, using the plurality of partitions, a partitioned supervised machine learning model that includes a plurality of model portions respectively associated with the plurality of partitions, wherein each model portion is trained on training observations assigned to the corresponding partition;
• receiving an inference observation comprising a plurality of inference feature values corresponding to the plurality of features;
• applying the plurality of model portions to the inference observation to determine a plurality of predicted outcome values for the inference observation, each predicted outcome value being generated by a different one of the plurality of model portions and corresponding to evaluation of the inference observation with respect to a different protected attribute scenario represented by the corresponding partition;
• determining, based on the plurality of predicted outcome values and the plurality of partition weights, a weighted predicted outcome value for the inference observation, wherein the weighted predicted outcome value is determined by combining the plurality of predicted outcome values according to the plurality of partition weights such that the weighted predicted outcome value reflects contributions from the different protected attribute scenarios;
• and
• storing or outputting the weighted predicted outcome value for use in controlling a downstream decision process.

One or more non-transitory computer readable media having instructions stored thereon for performing a method for reducing bias in machine learning predictions, the method comprising:

• receiving, by one or more processors, training data comprising a plurality of training observations, each training observation including (i) a target value, (ii) a plurality of feature values corresponding to a plurality of features, and (iii) at least one protected attribute value corresponding to at least one protected attribute;
• determining, based on the at least one protected attribute value, a plurality of partitions of the training data and a corresponding plurality of partition weights, each partition corresponding to a different protected attribute value or combination of protected attribute values;
• training, using the plurality of partitions, a partitioned supervised machine learning model that includes a plurality of model portions respectively associated with the plurality of partitions, wherein each model portion is trained on training observations assigned to the corresponding partition;
• receiving an inference observation comprising a plurality of inference feature values corresponding to the plurality of features;
• applying the plurality of model portions to the inference observation to determine a plurality of predicted outcome values for the inference observation, each predicted outcome value being generated by a different one of the plurality of model portions and corresponding to evaluation of the inference observation with respect to a different protected attribute scenario represented by the corresponding partition;
• determining, based on the plurality of predicted outcome values and the plurality of partition weights, a weighted predicted outcome value for the inference observation, wherein the weighted predicted outcome value is determined by combining the plurality of predicted outcome values according to the plurality of partition weights such that the weighted predicted outcome value reflects contributions from the different protected attribute scenarios;
• and
• storing or outputting the weighted predicted outcome value for use in controlling a downstream decision process.