Abstract
Society suffers from biases and discrimination, a longstanding dilemma that stems from ungrounded, subjective judgments. Especially unequal opportunities in labor remain a persistent challenge, despite the recent inauguration of top-down diplomatic measures. Here we propose a solution by using an objective approach to the measurement of nonverbal behaviors of job candidates that trained for a job assessment. First, we implemented and developed artificial intelligence, computer vision, and unbiased machine learning software to automatically detect facial muscle activity and emotional expressions to predict the candidates’ self-reported motivation levels. The motivation judgments by our model outperformed recruiters’ unreliable, invalid, and sometimes biased judgments. These findings mark the necessity and usefulness of novel, bias-free, and scientific approaches to candidate and employee screening and selection procedures in recruitment and human resources.
Highlights
Society suffers from biases and discrimination, a longstanding dilemma that stems from ungrounded, subjective judgments
The current study presents evidence that, in an application training context, (i) recruiters disagree on how motivated applicants are, (ii) recruiters disagree with candidates themselves on how motivated they are, (iii) a machine learning model successfully dissociates the most motivated from the least motivated candidates by using action unit activity and emotional expressions as predictors, and (iv) recruiters correctly pay attention but incorrectly weigh relevant facial markers to determine a candidate’s motivation levels
Computer vision software detected the activation of facial action units (AU) and basic emotional expressions during the entire structured interview
Summary
Society suffers from biases and discrimination, a longstanding dilemma that stems from ungrounded, subjective judgments. The motivation judgments by our model outperformed recruiters’ unreliable, invalid, and sometimes biased judgments These findings mark the necessity and usefulness of novel, bias-free, and scientific approaches to candidate and employee screening and selection procedures in recruitment and human resources. Recent advancements in artificial intelligence, big data, and modelling replace human raters by objectively evaluating candidates through text mining their accomplishment r ecords[10,11,12,13,14] When these types of models are properly trained, they produce no biases. An alternative and perhaps more successful approach could be the utilization of computer vision techniques to objectively measure facial behavior to unveil how these relate to cognitive p rocesses[16], mental wellbeing[17], and personality (e.g.18–21) Many of these traits are relevant predictors of job performance and satisfaction[22,23]. It may tackle the financial burden brought about by standard, time-consuming application procedures, and may potentially prevent job-hopping, and unequal, inefficient h iring[26]
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