The Downside of Using Computer Algorithms To Score Job Interviews
New research suggests that applicants may dislike when algorithms are used to evaluate video interviews, leading to a potential decrease in the applicant pool.
New research suggests that applicants may dislike when algorithms are used to evaluate video interviews, leading to a potential decrease in the applicant pool.
New research finds that political affiliation can influence the employee selection process, and the practice of doxing offers new insights into how.
New research demonstrates that both practitioners and applicants dislike brainteaser interview questions. As such, organizations should consider dropping these questions from the hiring process.
Cognitive ability tests are often used in hiring based on their ability to predict successful job performance. But are they as useful when employees already have substantial job experience?
New research is shedding light on the stigma and bias that can be created in the hiring process when individuals disclose mental health statuses online.
New research shows that adding oddball questions into a job interview does not make organizations more attractive to applicants.
Researchers have re-analyzed the data and provided new recommendations about the types of tests that organizations should use when hiring. This new information can lead to improved decisions and fairness in the selection process.
New machine learning techniques offer a promising way for organizations to predict employee success without compromising fairness to applicants.
New research finds that an AI chatbot can infer someone’s personality. What are the implications for the future of employee selection?
Background information in asynchronous video interviews can lead to bias in the employee selection process.