Machine learning apparatus and methods for predicting hiring progressions for demographic categories present in hiring data

An apparatus, comprising:

• a display configured to show a user interface including a visual representation of a hiring progression associated with a job description, and at least one of a visual representation of an inclusion meter, a visual representation of slate demographic data, or a visual representation of a slate goal;
• and
• a non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:
• retrieve, via the user interface and during a hiring process, an indication of the slate goal,
• receive, from a first hiring source and for the job description, a first candidate profile;
• retrieve, during the hiring process and based on the first candidate profile, an indication of slate demographic data,
• augment a first portion of training data to produce a second portion of training data by at least one of applying an image filter, adding noise, translating text, synonym-replacing text, or changing demographic information,
• a number and a variety associated with the second portion of training data being larger than a number and a variety associated with the first portion of training data;
• execute, during the hiring process, a trained machine learning model based on the slate goal and the slate demographic data, to predict the hiring progression, the trained machine learning model trained using the second portion of training data;
• transmit, during the hiring process and to the user interface, an indication of the hiring progression and an indication of the slate demographic data to cause the user interface to display the visual representation of the hiring progression and the visual representation of the slate demographic data based on the indication of the hiring progression and the indication of slate demographic data, respectively;
• generate, after predicting the hiring progression and using a natural language processing model, an updated job description based on the job description, the slate goal, and the hiring progression;
• determine that the hiring progression does not meet the slate goal;
• and
• in response to determining that the hiring progression does not meet the slate goal, send the job description to a second hiring source different than the first hiring source and more likely to attract job candidates that meet the slate goal than the first hiring source.

A method, comprising:

• retrieving a first set of slate goals, each slate goal from the first set of slate goals indicating a target number of individuals to be hired from a protected class from a set of protected classes;
• retrieving first timestamped slate demographic data associated with the set of protected classes;
• associating the first timestamped slate demographic data and the first set of slate goals with a set of hiring progression labels to generate labeled training data;
• training a decision tree gradient boosting model based on the labeled training data to produce a trained decision tree gradient boosting model;
• retrieving (1) a second set of slate goals different from the first set of slate goals and (2) second timestamped slate demographic data associated with the second set of slate goals, each slate goal from the second set of slate goals indicating a target number of individuals to be hired from a protected class from the set of protected classes;
• executing the trained decision tree gradient boosting model based on the second set of slate goals and the second timestamped slate demographic data, to predict a set of hiring progressions for the set of protected classes;
• displaying a visual representation for each hiring progression from the set of hiring progressions;
• determining that at least one hiring progression from the set of hiring progressions does not meet a slate goal from the second set of slate goals associated with the at least one hiring progression;
• sending a job description associated with the at least one hiring progression to a hiring source more likely to attract candidates that will satisfy the slate goal associated with the at least one hiring progression;
• and
• generating, after predicting the set of hiring progressions and using a natural language processing model, an updated job description based on the job description, at least one of the first set of slate goals or the second set of slate goals, and the set of hiring progressions.