Today, many businesses struggle to hire a diverse workforce. A well-trained, data-centric model can effectively eliminate human bias.

Does Evren Lucas ask if the pandemic can be used to reset the nature of employment practices and minimize bias by using ‘the great resignation’?

Global pandemic disruption, which began in March 2020, resulted in knock-on effects throughout the economy, decimating businesses and resulting in mass unemployment. New opportunities for disruption across many industries are emerging with the convergence of technology and shifting attitudes in work culture. In addition to revolutionizing certain industries, digitization has also transformed our daily lives. Online services are becoming increasingly commonplace for services we use on a daily basis. The human component has been removed from traditional infrastructure as we have digitized everything that can be digitized. And that’s good. That’s the case. If it simplifies certain processes for most, then arguably, yes. If some infrastructures remain to serve those who cannot or won’t embrace change, then let’s embrace technological change.

New tools for a new age

We are at that point at the moment, I believe. Implementing AI, machine learning, and APIs help us better manage the complexity of modern living. Using these tools, how can recruitment be solved? Since the recruitment process has not changed much in real terms, the industry has had trouble finding a firm footing as cultural attitudes shift. It is not that different from a curriculum vitae prepared in the 1950s.

Most hiring managers start by sifting through hundreds and conducting telephone interviews. If all these hoops have been cleared, there will probably be a follow-up interview. How do you hire someone that way? How can we determine the best candidate for the job in the most equitable way? A shortlist of candidates is nothing more than the hiring manager’s preferences based on a candidate’s name, gender, where they went to school, and one person’s expectations of how they’ll perform.

As we are all susceptible to unconscious bias during recruitment, bias has been an issue in the workplace for decades. Businesses have long recognized this problem but have not made significant progress. Rather than hiring in-house, they’re outsourcing the search for viable candidates to third parties, streamlining and organizing hiring, and reducing human resources work. Establishing policies and strategies to combat unconscious bias is the next step up the management chain.

As a result of the Equality Act in the UK, setting quotas for workforce members based on their age, race, religion, gender, or disability is unlawful. Employers with protected characteristics can promote those with the same qualifications as others – but the employer does not have to.

This way, the law addresses positive discrimination or hiring someone with a protected characteristic regardless of their qualifications.

As a result of creating a longer shortlist, a recent Harvard Business Review study found ‘the proportion of female candidates increased by 15 to 20 percent,’ but broadening an informal shortlist is one approach to bias in recruitment, is that the best way to ensure workplace diversity?

Solving recruitment

Technology is making a real impact now that it has come a long way.

Removing human touch is generally considered a bad thing, but removing unconscious bias is a challenge that machines are ideally suited to solve. Several start-ups already use technology to ‘solve’ recruitment problems. With cloud computing, artificial intelligence, and machine learning, they hope to revolutionize the hiring process by offering Recruitment-as-a-Service (RaaS).

Companies will use a new end-to-end recruitment service to reduce bias drastically rather than paying hefty commissions to recruitment agents. Is there a model for such a platform? The first step in the process is assessing candidates’ suitability through a short set of standardized questions. Then, based on their answers, an algorithm grades them based on their responses against an ideal response. Voice masking may be used in introductory over-the-phone interviews to disguise identifying characteristics that could introduce bias at an early stage, even if they are unrelated to a candidate’s suitability.

In my opinion, digitalization can be used to reduce bias in hiring processes. Our programming will always have its quirks, but perhaps quality control on some of our decisions would be beneficial.

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