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Recruiters turn to ‘algorithmic hiring’ to solve a workplace problem

Recruiters are trying to remove some of the human bias from decision making in the hiring process by turning to data and algorithms

Recruiters are trying to remove some of the human bias from decision making in the hiring process by turning to data and algorithms.

One way that bias can come into the hiring decision is the tendency for interviewers to hire candidates who remind themselves, potentially leading to a lack of diversity in the workplace. In Google’s first ever diversity report, it reported that only two per cent of its staff are black and three per cent Hispanic, in a potentially extreme example.

One solution has been proposed to try and take some of these biases out of the process by using systematic analysis of data, or using an algorithm. Businesses use personality tests on candidates during an initial screening process, then use data analysis to determine its ideal hires.

While any algorithm depends on what a business is searching for, a number of variables are set which include the data from personality tests to predict whether a candidate will quit.

This type of hiring has been on the rise over the past few years, with Google using an algorithm to quickly grow its workforce – by using an elaborate survey to hone in on candidates who will fit into the company’s culture.

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A study of ‘algorithmic hiring’ revealed that a simple equation was significantly better than humans at identifying high-performing employees, with the result being the same across different industries and levels of employment. The researchers believe that the results were due to humans paying too much attention to inconsequential details and using candidate information inconsistently.

A company now believes that this type of algorithmic hiring can also improve diversity, with the company saying that after they deployed their software they found an average increase of 26 per cent in African American and Hispanic hires.

Infor Talent Science, the company behind it said that “What we’ve found is regardless of [the industry], whether it’s restaurants, retail, call centres—it actually increases the diversity of the population. “

”What a systematic process does is it knows no colour, no race, no ethnicity. When [a hiring manager] doesn’t know a person and they don’t know what to look for, they basically hire people like themselves. It’s ‘We have something in common,’ or ‘Oh, I like you,’ then it’s ‘Okay you’re hired.’ What this does is it provides them with an objective piece of information that shows the probability that they’re going to be successful in the role. So it helps to qualify that pool.”

One of the caveats of Infor’s study is that their data is only based on hires who disclosed ethnic background. As with most surveys, checking the racial box is voluntary.

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Recruiters are trying to remove some of the human bias from decision making in the hiring process by turning to data and algorithms. One way that bias can come into the hiring decision is the tendency for interviewers to hire candidates who remind themselves, potentially leading to a lack of diversity in the workplace. In Google’s first ever diversity report, it reported that only two per cent of its staff are black and three per cent Hispanic, in a potentially extreme example. One solution has been proposed to try and take some of these biases out of the process by…

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