Technology for training a predictive model is described. A processing device collects digital interview data including features identified from candidate interviews. A model training tool selects a data set of the digital interview data. The data set includes a predicted performance outcome and an actual performance outcome for each of a plurality of candidates. The model training tool determines an error metric for each of the plurality of candidates. The error metric includes a relationship between the predicted performance outcome and the actual performance outcome for each candidate. The model training tool determines a number of candidates whose digital interview data includes a feature corresponding to a protected class. The model training tool normalizes an effect of each candidate on the error metric based on the corresponding protected class and applies the normalized error metric to reduce bias in the predictive model with respect to the protected class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: collecting, by a processing device, digital interview data from candidate interviews on a digital interview platform; selecting, by a model training tool executed by the processing device, a data set of the digital interview data to train a predictive model, wherein the data set comprises a predicted performance outcome and an actual performance outcome for each of a plurality of candidates; determining, by the model training tool, an error metric for each of the plurality of candidates, wherein the error metric comprises a relationship between the predicted performance outcome and the actual performance outcome for each of the plurality of candidates; determining, by the model training tool, a penalty value from a penalty function operating based on an applicable rule; updating, by the model training tool, the error metric based on the penalty value; detecting, by the model training tool, a feature in the digital interview data for a number of candidates, the feature corresponding to a protected class; normalizing, by the model training tool, an effect of each feature and candidate on the error metric based on the protected class to obtain an normalized error metric; and applying, by the model training tool, the normalized error metric to the predictive model to reduce adverse impact in the predictive model with respect to the protected class by directing the model or dropping features. 2. The method of claim 1, wherein the determining the penalty value from the penalty function comprises at least one of: determining the penalty value using a ⅘ths rule; determining the penalty value using a statistical significance rule; determining the penalty value using a Fisher's exact test; determining the penalty value using a z-test for proportions; or determining the penalty value using a chi-squared test. 3. The method of claim 1, wherein the determining the error metric comprises determining the error metric using an objective function. 4. The method of claim 3, wherein the objective function comprises a sum of squared errors operation. 5. The method of claim 1, wherein the detecting the feature in the digital interview data for the number of candidates comprises determining a feature corresponding to at least one of age, gender, sexual orientation, disability, or race. 6. The method of claim 1, wherein the detecting the feature in the digital interview data for the number of candidates comprises detecting at least one of an audio feature, a video feature, an image feature, an interaction feature, or a response feature. 7. A method comprising: collecting, by a processing device, digital interview data comprising features identified from candidate interviews; selecting, by a model training tool executing on the processing device, a data set of the digital interview data to train a predictive model, wherein the data set comprises a predicted performance outcome and an actual performance outcome for each of a plurality of candidates; determining, by the model training tool, an error metric for each of the plurality of candidates, wherein the error metric comprises a relationship between the predicted performance outcome and the actual performance outcome for each of the plurality of candidates; detecting, by the model training tool, a feature in the digital interview data for a number of candidates, the feature corresponding to a protected class; normalizing, by the model training tool, an effect of each candidate on the error metric based on the corresponding protected class; and applying, by the model training tool, the normalized error metric to reduce adverse impact in the predictive model with respect to the protected class. 8. The method of claim 7, wherein the determining the error metric further comprises determining a penalty value based on a penalty function. 9. The method of claim 8, wherein the penalty function comprises the ⅘ths rule. 10. The method of claim 8, wherein the penalty function comprises the statistical significance rule. 11. The method of claim 7, wherein the determining the error metric comprises determining the error metric using an objective function. 12. The method of claim 11, wherein the objective function comprises a sum of squared errors operation. 13. The method of claim 7, wherein the detecting the feature in the digital interview data for the number of candidates comprises determining a feature corresponding to at least one of age, gender, sexual orientation, disability, or race. 14. The method of claim 7, wherein the detecting the feature in the digital interview data for the number of candidates comprises at least one of an audio feature, a video feature, an image feature, an interaction feature, or a response feature. 15. A non-transitory computer readable storage medium including instructions that, when executed by a processing device of a computing system, cause the computing system to perform operations comprising: collecting, by a processing device, digital interview data from candidate interviews on a digital interview platform; selecting, by a model training tool executed by the processing device, a data set of the digital interview data to train a predictive model, wherein the data set comprises a predicted performance outcome and an actual performance outcome for each of a plurality of candidates; determining, by the model training tool, an error metric for each of the plurality of candidates, wherein the error metric comprises a relationship between the predicted performance outcome and the actual performance outcome for each of the plurality of candidates; determining, by the model training tool, a penalty value from a penalty function operating based on an applicable rule; updating, by the model training tool, the error metric based on the penalty value; detecting, by the model training tool, a feature in the digital interview data for a number of candidates, the feature corresponding to a protected class; normalizing, by the model training tool, an effect of each candidate on the error metric based on the protected class to obtain a normalized error metric; and applying, by the model training tool, the normalized error metric to the predictive model to reduce adverse impact in the predictive model with respect to the protected class. 16. The non-transitory computer readable storage medium of claim 15, wherein the error metric is modified by a penalty function, and wherein the penalty function comprises a function to incorporate a ⅘ths rule into the error metric. 17. A computing system comprising: a data storage device; and a processing device, coupled to the data storage device, to execute a digital interviewing platform comprising a model training tool, wherein the model training tool is to: collect digital interview data comprising features identified from candidate interviews; select a data set of the digital interview data to train a predictive model, wherein the data set comprises a predicted performance outcome and an actual performance outcome for each of a plurality of candidates; determine an error metric for each of the plurality of candidates, wherein the error metric comprises a relationship between the predicted performance outcome and the actual performance outcome for each of the plurality of candidates; detect a feature in the digital interview data for a number of candidates, the feature corresponding to a protected class; normalize an effect of each candidate on the error metric based on the protected class to obtain a normalized error metric; and apply the normalized error metric to the predictive model to reduce adverse impact in the predictive model with respect to the protected class. 18. The computing system of claim 17, wherein the digital interviewing platform is a web-based application. 19. The computing system of claim 17, wherein the model training tool is further to determine a penalty value based on a penalty function to determine the error metric, wherein the penalty function comprises the ⅘ths rule. 20. The computing system of claim 17, wherein the model training tool is further to determine a penalty value based on a penalty function to determine the error metric, wherein the penalty function comprises a statistical significance rule.