Abstract: An audio track capturing a conversation between an interviewer and an interviewee during an interview may be segmented, by executing a speaker identification engine, into a plurality of audio segments each being tagged as associated with one of the interviewer or the interviewee. A speech recognition and natural language processing (NLP) engine may be applied to the audio track to determine attributes associated with audio segments being tagged to the interviewer. The attributes may comprise timing parameters associated with the audio segment and a text content of the audio segment. A rule-based analysis engine may be executed, based on the attributes associated with audio segments being tagged to the interviewer, to determine whether the interviewer conducts the interview in compliance with predetermined rules. Responsive to determining that the interviewer does not conduct the interview in compliance with the predetermined rules, generating a notice regarding the non-compliance of the interview.

Abstract: A system and method for providing personalized presentation of job skills in one or more organizations. The system includes an interface device and a processing device to retrieve, based on one or more identifiers, a set of talent profiles stored in a human resource (HR) information system, calculate, based on the one or more skills in each of the set of talent profiles, an inventory of skills, for each skill in the inventor of skills, calculate a trend value based on timestamps and one or more skills associated with the set of talent profiles, responsive to identifying a first job profile, generate a first skill cloud, the first skill cloud comprising symbols representing skills contained in the inventory of skills arranged in an order determined based on the trend value associated with each skill in the inventory of skills, and present, in a graphical user interface implemented on the interface device, the first skill cloud.

Abstract: A system and method include one or more processing devices to obtain a job description comprising requirements for a job, identify, based on at least one requirement in the job description, qualified talent profiles that each characterizes a corresponding qualified person for the job, calculate, by applying a deep neural network to each of the qualified talent profiles, embedding vectors, determine a cluster of embedding vectors based on a similarity distance metric, determine linguistic units in a group of the qualified talent profiles, and determine implied traits based on traits explicitly specified in the job description and common traits among the group of qualified persons.

Abstract: A system and method for providing personalized presentation of job skills in one or more organizations. The system includes an interface device and a processing device to retrieve, based on one or more identifiers, a set of talent profiles stored in a human resource (HR) information system, calculate, based on the one or more skills in each of the set of talent profiles, an inventory of skills, for each skill in the inventor of skills, calculate a trend value based on timestamps and one or more skills associated with the set of talent profiles, responsive to identifying a first job profile, generate a first skill cloud, the first skill cloud comprising symbols representing skills contained in the inventory of skills arranged in an order determined based on the trend value associated with each skill in the inventory of skills, and present, in a graphical user interface implemented on the interface device, the first skill cloud.

Abstract: A system and method for implementing a talent management platform to determine, from a pool of applicants to a job opening, matching candidates, determine, using a neural network module, a set of feature values from the one or more feature values contained in the talent profiles associated with the matching candidates, generate a query based on the set of feature values, retrieve, based on the query, compensation data from a compensation database, and present, in a user interface, the compensation data as part of potential offers to the matching candidates.

Abstract: A system and method include one or more processing devices to implement a sequence of transformer neural networks, first and second sequence-to-sequence layers that each comprises a sequence of nodes, and an output layer to provide the first set and second set of score vectors to a downstream application of a natural language processing (NLP) task.

Abstract: A system and computer program enable a candidate to anonymously apply for a job position at an organization. In response to submitting a resume or other data for the purpose of applying anonymously for a job, the system generates an anonymous profile for the candidate and provides it to the organization to which the candidate is applying. The system excludes the candidate's name from the anonymous profile. In certain embodiments, the system also excludes one or more of the following: the candidate's address, data that is indicative of a candidate's race, age, or gender, and data that is not relevant for the job role for which the candidate is applying. After reviewing the anonymous profile, the organization has the option to reject the candidate or explore the candidate further. In response to the organization rejecting the candidate, the system notifies the candidate of the rejection without revealing the candidate's identity to the organization.

Abstract: An intelligent scheduler system implemented by one or more computer for objectively selecting interviewers for an interview of a candidate for a job opening at an organization. The one or more computers includes a storage device and a processing device to generate a calibrated job profile, generate an enriched talent profile of the candidate, identify, based on the calibrated job profile and the enriched talent profile, an aspect of the candidate to be evaluated during the interview, execute a deep neural network comprising an input layer to receive the aspect, the enriched talent profile of the candidate, and the enriched talent profiles of the potential interviewers, and an output layer to output match scores each indicating an effective measure of a corresponding one of the potential interviewers for evaluating the aspect during the interview, and determine, based on the match scores, qualified interviewers from the potential interviewers.

Abstract: A system and method relate to excluding biasing data from talent profiles, including identifying one or more classes of bias, obtaining a first talent profile, wherein the first talent profile comprises an identifier of a person, and a plurality of values characterizing aspects of the person, determining, from the plurality of values, a first value that is indicative of influences over at least one of the one or more classes of bias, and removing or substituting the first value in the first talent profile to generate a second talent profile.

Abstract: An intelligent system and method for analyzing documents and suggesting corrections based on diversity criteria include a processing device to analyze a job document, using a machine learning model, to identify a first expression representing a first qualification requirement favorable to a first class of applicants than a second class of applicants according to a diversity metric, responsive to identifying the first expression, determine, using a semantic relation map, a second expression representing a second qualification requirement that is less favorable to the first class of applicants when compared to the first expression, and responsive to determining that the second expression, present the second expression on the interface device as a suggested replacement to the first expression in the job document.

Abstract: The present disclosure describes a system, method, and computer program for removing or replacing information in candidate profiles that may influence bias. The system identifies data in candidate profiles that may influence bias with respect to one or more defined classes of bias, such as gender, race, or age. The identified data is removed or replaced with an abstracted/neutral form of the data. The system may also remove information that personally identifies a candidate. In certain embodiments, the system determines what data is relevant for the job for which a candidate is applying and, for data that is relevant, at what level it is relevant (i.e., at the base value or an abstracted level). In such embodiments, the system removes data from the candidate's profile that is not relevant for the job role and, where applicable, abstracts some of the remaining data to levels that are relevant for the job role.

Abstract: A system and method for event management including a processing device to identify target participants from the event participants, and transmit a request for information to each of the target participants, generate a corresponding first enriched talent profile associated with the at least one target participant, implement a machine learning module that is trained based on historical recruiting data relating to at least one of a talent event or an organization to which the at least one target participant belongs, for a calibrated position profile, execute the trained machine learning module using the first enriched talent profiles and the calibrated position profile as inputs to calculate a match score associated with each of first enriched talent profiles, and determine, based on calculated match scores, one or more target participants invited to meet at the talent event.

Abstract: A method and system relating to determine a match between a candidate and a job position for an enterprise, the method and system including receiving an enriched talent profile associated with the candidate, the enriched talent profile comprising a talent profile of the candidate derived from a first data source of the enterprise, and one or more supplemental data items obtained from a second data source independent from the first data source, generating a calibrated job profile for the job position, the calibrated job profile comprising information, and executing a machine learning module using the enriched talent profile and the calibrated job profile as inputs to determine a match score between the candidate and the job position.