Abstract: Removing bias when matching job candidates to open positions by obtaining candidates' data including information about the job candidates and a likelihood rate that the candidate matches the open position, identifying protected characteristics from the candidates' data, generating a training data set that does not bias within groups of candidates having different protected characteristics, where the training data set includes a portion of the job candidates, training a model based on the training data set, applying the trained model on a test set, where the test set is different from the training data set, and determining a fairness measurement value of the trained model using the results of the model on the test set and protected characteristics of candidates of the test set.

Abstract: A computerized method performed on digital data stored in a database, the method including obtaining one or more job requirements and a list of candidate resumes, extracting tasks from the resumes in the list of candidate resumes or employee profiles, converting the extracted tasks into a mathematical representation, executing a similarity function between the extracted tasks and tasks in the job requirements, assigning a score to jobs in the list of jobs according to the output of the similarity function, assigning a score to a specific candidate in the list of candidates according to the output of the similarity function.

Abstract: A computerized method performed on digital data stored in a database, the method including obtaining one or more job requirements and a list of candidate resumes, extracting tasks from the resumes in the list of candidate resumes or employee profiles, converting the extracted tasks into a mathematical representation, executing a similarity function between the extracted tasks and tasks in the job requirements, assigning a score to jobs in the list of jobs according to the output of the similarity function, assigning a score to a specific candidate in the list of candidates according to the output of the similarity function.

Abstract: Mapping data from a first computer application to a second computer application by obtaining a second organization's application schema with data, generating a mathematical representation of the field keys and the data in the data fields of the second organization's application, extracting data from a first organization's application; processing the data from the first organization's application and data from the second organization's application, and mapping data from the first organization's application into the second organization's application schema.

Abstract: Matching job candidates' experience to open positions by providing a first group of pairs of titles and job descriptions which are similar as first training data into a computerized model, providing a second group of pairs of titles and job descriptions which are not similar as second training data into the computerized model, training the model to output a similarity value of a candidate and an open position based on the first training data and the second training data, and inputting the candidate's data and an open position data into the trained model, where the trained model computes a similarity value indicating a likelihood of a match between the candidate and the open position.

Abstract: Removing bias when matching job candidates to open positions by obtaining candidates’ data including information about the job candidates and a likelihood rate that the candidate matches the open position, identifying protected characteristics from the candidates’ data, generating a training data set that does not bias within groups of candidates having different protected characteristics, where the training data set includes a portion of the job candidates, training a model based on the training data set, applying the trained model on a test set, where the test set is different from the training data set, and determining a fairness measurement value of the trained model using the results of the model on the test set and protected characteristics of candidates of the test set.