Blind Hiring: What is it? Does it work? How does tech help?

In an ideal world, job candidates’ age, gender, and cultural background shouldn’t influence their ability to get to interview – but research has consistently shown it does even when they have identical qualifications, due to a phenomenon known as unconscious bias.

This is particularly impactful in situations when we’re forced to deal with too much information or competing stimuli at once. In these situations, we do not have the time or mental energy to process each and every detail, so tend to favour ‘safe’ decisions as a sensible cognitive coping strategy.

In the hiring game, when we are often confronted with many similar resumes in front of us, this typically manifests as us making ‘safe’ decisions – selecting candidates who are ‘like us’. Companies consistently miss out on high quality candidates as a result, for no other reason than they are different to the norm.

“Blind hiring” mechanisms seek to counter unconscious bias by proactively removing various candidate identifiers during the hiring process. This approach has become increasingly popular with leading companies as a reliable method to achieve better hiring outcomes.

How is blind hiring being used?

The list of companies employing blind hiring is booming, but the reasons go beyond corporate social responsibility.

After extensive testing of candidate documentary appraisal and their performance in the company over time, Google noted only a 14% correlation between early assessment and performance over time. Meanwhile Ernst & Young removed the requirement for degrees in their job applications in 2015 because they had found a candidate’s degree had no relation to their future job performance.

In other words, while implementing a fairer hiring process is a positive for candidates from diverse or underrepresented backgrounds, it also makes sound business sense for organisations. By improving their mechanism to sort candidates, companies improve their corporate social responsibility and their bottom line simultaneously, while benefiting from the proven competitive advantage that a diverse but well aligned workforce brings.

For many companies however the road to blind hiring is less than simple. The process itself is typically quite manual and time consuming in nature: print out candidate documents as they come through, pay someone to draw a line through each identifier, and scan them back into the system for appraisal by the hiring panel. Even when implemented, this manual process does not account for potential issues such as gaps in work history or past bias candidates may have encountered in their careers to date meaning the effects on diversity outcomes may be limited.

In other words, manual blind hiring in isolation may not address the actual issue with resumes and cover letter themselves: that they are not a reliable guide to future success.

How does tech help?

Technology can help companies achieve better outcomes in two ways. The first is in lessening the workload associated with blind hiring and with shortlisting in general. Modern tech platforms enable HR teams and business owners to save many hours of staff time and in doing so, lessen both their time to hire and the cost associated with it.

The second is in improving results. Objective algorithm backed platforms can put candidates on a level playing field which solves some of the problems associated with unconscious bias and in doing so, improves hiring outcomes.

In our case, we also add a layer by ranking candidates in the first instance based on how closely their values and attitudes match those specified by the employer as required for a given role. This compatibility score is then used to rank candidates as they apply, with their resumes and other documents just a click away.

This unique approach solves the blind hiring issue while also bringing person-organisation fit back to the process, ensuring candidates are assessed for a broader definition of merit – not just on their documents, but also on how well they fit the role and organisation.

The result is an objective, defensible, data driven process that systematically improves outcomes for organisations and their job candidates over time.