Patent ID: 9652745
Date: 2017-05-16
CPC Classifications: G06K,G06Q,G09B

Claim:
1. A method comprising: receiving, at a human bias detection tool executed by a first computing device of a digital interviewing platform, user input from a second computing device over a network, the user input comprising a selection of a set of digital interviews of first candidates in a hiring process to monitor for and detect a metric of disparate impact, caused by an evaluator's bias that exceeds a specified limit in the hiring process; retrieving, by the human bias detection tool, evaluation data from a data storage device of the digital interviewing platform, the evaluation data generated with respect to a set of evaluators by evaluating recorded video responses of the first candidates to questions asked during the hiring process; retrieving, by the human bias detection tool from the data storage device, video frames of at least a portion of the recorded video of responses of the first candidates; performing, by the human bias detection tool, video analysis on the video frames to identify visual indicators of faces of the first candidates, wherein the visual indicators comprise relative spacing between identified features of the faces of the first candidates; combining, by the human bias detection tool, the visual indicators with an audio indicator and a categorical indicator that further characterizes respective first candidates, to generate a combined vector representation of the first candidates; performing, by the human bias detection tool, supervised learning of the combined vector representation of the first candidates with respect to one or more classifications of the first candidates, to train a classification model; applying, by the human bias detection tool, the classification model to second indicators captured of second candidates to classify the second candidates according to a protected class, wherein the second indicators comprise one or more of a second visual indicator, a second audio indicator, or a second categorical indicator of respective second candidates; determining, by the human bias detection tool, whether evaluation data for the second candidates indicates a disparate impact of one or more evaluators of the set of evaluators with respect to classifications of the second candidates according to the protected class, wherein the determining whether the evaluation data indicates the disparate impact comprises performing a relative selection rate analysis with respect to the classifications to obtain a metric of disparate impact reflected in the evaluation data for the second candidates; determining, by the human bias detection tool, that the metric of the disparate impact exceeds a specified limit of relative selection rate to other groups in the hiring process, the specified limit being less than 80% of a normal selection rate, which is a limit enforced for the protected class; generating, by the human bias detection tool, a notification containing information regarding the determination that the metric of disparate impact exceeds the specified limit in the hiring process; and sending, by the human bias detection tool, the notification to the second computing device over the network.