How Automated Employee Selection Tools Remove Hiring Bias

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How Automated Employee Selection Tools Remove Hiring Bias

Modern hiring and recruitment is more diverse than ever before, yet many recruiters may still retain their hiring biases. Unfortunately, it’s difficult for people to escape the biasses they’re rasied with, which influence split-second decisions to move candidates on to the next process of hiring, to keep a resume, or even to grant an interview in the first place.

While education and reinforcement are key to reducing bias, organizations across the globe are utilizing automated employee selection tools or AI to remove some of that bias. This applies at nearly every level of the selection process, from initial application all the way to final candidate selection. It’s important to retain a human voice when hiring, simply because some aspects of personality and culture-fit require a human opinion. However, automation during the assessment and hiring process can greatly improve your selection and reduce total bias.

Resume Selection

The average job role receives hundreds of applications, which can be difficult to sort through at best. Automation allows you to select resumes based on mentioned job skills, resume points, and other screening factors. This allows you to screen out resumes that aren’t relevant or that don’t show promise, reducing the need for an individual to manually sort resumes and likely miss quality candidates out of boredom, frustration, or simply giving up on finding a certain number of qualified candidates.

Candidate Engagement

While not yet common, assessment centers are more often using chatbots and AI to engage with potential candidates, learn basic details, and move them through the early stages of the hiring process. This works to collect more data on the candidate, stripping away confirmation and projection bias and giving recruiters a better idea of who they are actually talking to.

Non-Biased Interview Evaluation

AI is able to evaluate individual responses, interview structure and answers, and responses to structured interviews to evaluate the candidate. This means that recruiters receive a machine evaluation of performance, minus biases relating to personality, gender, sexuality, or skin color, giving them and you another perspective on the potential candidate.

Blind Testing

Blind testing means that you evaluate candidates based on performance in testing, with an otherwise anonymized profile. The AI evaluates simple performance for whatever you’re testing (EQ, IQ, Skills, etc.) which is then backed up by a personal evaluation. While blind testing can have drawbacks in that it doesn’t necessarily link candidates with good traits to those showing good traits in another test, it can greatly reduce bias. For example, Princeton University found that blind testing increased the chances of women being hired to their orchestra from 25 to 46 percent.

Hiring bias greatly impacts organizations because it affects who is hired and why. With biases such as confirmation bias, where recruiters form an initial opinion, typically within a few seconds, and maintain it and make a hiring decision, to gender and sexuality biases, organizations can lose thousands of excellent candidates to bias. Using automation to add an extra level of evaluation works to remove this bias, because you have a third-party, unbiased opinion on a candidate.

This will, over time, work to improve the diversity of your hires and may allow you to make better hires.

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