Abstract: Techniques relate to detecting and reducing bias in regression-based machine learning models. A baseline model is trained using a regression algorithm and a baseline training data set comprising examples with independent parameters and corresponding continuous dependent values. A classification algorithm is executed on the training data set to determine, for at least a portion of examples, a probability distribution of class assignment across a set of classes. Class-aware machine learning models are trained using examples in accordance with their probability of belonging to respective classes. For each example, outputs from the baseline and class-aware models are combined according to class assignment to determine an expected class-aware output. A difference between the expected class-aware output and the baseline model output is evaluated.

Abstract: Techniques relate todetecting and reducing bias in regression-based machine learning models. A baseline model is trained using a regression algorithm and a baseline training data set comprising examples with independent parameters and corresponding continuous dependent values. A classification algorithm is executed on the training data set to determine, for at least a portion of examples, a probability distribution of class assignment across a set of classes. Class-aware machine learning models are trained using examples in accordance with their probability of belonging to respective classes. For each example, outputs from the baseline and class-aware models are combined according to class assignment to determine an expected class-aware output. A difference between the expected class-aware output and the baseline model output is evaluated.

Abstract: Techniques are described herein for verifying and/or reducing bias in machine learning (ML) models. An ML model may be trained using a baseline training data set (TDS). A second TDS may be generated from the baseline TDS based on executing a classification algorithm to assign each example to one of a set of classes. The class-aware ML models may be trained using class-specific examples (e.g., examples that correspond to a given class). Bias in the ML model may be detected based on providing an input to the ML model and each of the class-aware ML models and identifying differences between their respective outputs. If bias is detected, a parameter that highly correlates to the output of the ML model may be removed from the training data and the process may begin anew. Each iteration of the process may reduce the bias originally detected in the ML model.