Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.

Abstract: Systems and methods for generating and processing modeling workflows.

Abstract: Systems and methods for generating explanation information for a result of an application system. Explanation configuration is generated based on received user input. Responsive to an explanation generation event, a plurality of modified input variable value sets are generated for a first applicant by using the explanation configuration. For each modified input variable value set: a request is provided to a first application system for generation of a result for the modified input variable value set, and a result is received for the modified input variable value set. At least one input variable value is selected based on a comparison between a first result of a first input variable value set of the first applicant and results for the modified input variable value set. Explanation information is generated for the first result by using human-readable description information for each selected input variable value, in accordance with the explanation configuration.

Abstract: Systems and methods for generating and processing modeling workflows.

Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for generating explanation information for a result of an application system. Explanation configuration is generated based on received user input. Responsive to an explanation generation event, a plurality of modified input variable value sets are generated for a first applicant by using the explanation configuration. For each modified input variable value set: a request is provided to a first application system for generation of a result for the modified input variable value set, and a result is received for the modified input variable value set. At least one input variable value is selected based on a comparison between a first result of a first input variable value set of the first applicant and results for the modified input variable value set. Explanation information is generated for the first result by using human-readable description information for each selected input variable value, in accordance with the explanation configuration.