The BBC recently launched a £1m drive to appoint more women, people with disabilities and those from black, Asian and minority ethnic backgrounds into senior jobs, whilst firms of all types are hiring heads of diversity – taking a leaf out of the tech industry’s book.
But despite the hundreds of initiatives, hours of brainpower and the creation of an $8bn unconscious bias training industry, many businesses continue to make small steps rather than big strides in the effort to hire and develop diverse teams. An inquiry by the equality and human rights commission revealed that in the UK alone, more than 60 per cent of FTSE350 companies failed to meet gender diversity targets.
Bias management: a flawed approach
There are various factors explaining why organisations have made relatively slow progress towards hitting diversity targets, but part of the issue lies in the approach to bias management. Firstly, bias is a fundamental component of human nature. No matter how self-aware we become, every decision we make is determined by subjective opinion. It is therefore futile for companies to “train out” unconscious bias when by definition, recruiters and managers are unaware of it.
By focusing on particular groups of people, who could eventually be unfairly prioritised, organisations also run the risk of perpetuating positive discrimination. This could lead to firms hiring less capable or suitable workers and create a perception that applicants are not being selected on merit alone. This can damage the credibility of a diversity and inclusion strategy, both in the eyes of current employees and the diverse candidates companies want to attract.
The tech-driven solution
By harnessing technology and data, it is possible for organisations to promote diversity and attract, recruit and retain top-tier talent. And by applying intelligent technology to hiring and performance management, companies can weed out unconscious bias and ensure any people decisions are based on merit alone.
There are three ways in which intelligent machines could hold the key to recruiting and developing more diverse and talented teams.
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(1) Data objectivity. While we cannot remove bias from our own brains, we can program computers to outsmart it. Technology is being developed that translates data captured from a wide range of signals during an assessment decision-making process. It also monitors reviewer behaviour during recruitment, helping pinpoint where bias crops up in the assessment and hiring process.
From this, actionable insights are provided which can help recruiters and HR professionals determine where high quality candidates may be slipping through the net, due to unconscious bias influencing decisions. So harnessing intelligent machines can bring greater objectively to recruitment and assessment processes, allowing businesses to hire the best candidates, regardless or their ethnicity, age, gender or sexual orientation.
(2) Building better teams. One key benefit of applying artificial intelligence to employment decisions is the removal of subjective decision-making from recruitment processes. This should, in theory, only allow the highest quality candidates to filter through.
The absence of conscious or unconscious bias will enable recruiters to hone in on candidates’ most important qualities – their skills, experience, mind-set and cultural fit. Hiring more candidates who excel in these areas, rather than subjective measures unrelated to performance, will pay dividends. Research has shown that the top talent at organisations produce four times more output than average employees.
This also applies to current employees, as HR professionals can draw on insights from machine learning to identify candidates most suited to promotion based on clear competency-based criteria. The benefits of this are twofold: firstly the most capable and suitable candidates will progress and deliver more value for their employer, and secondly the sense that all workers are being treated fairly and consistently is likely to improve overall levels of engagement.
(3) Reengineering recruitment and assessment processes. After identifying sources of bias and inconsistency in people decisions, organisations can make improvements to their recruitment and assessment processes so that they are fairer and more consistent. To give a concrete example, when Microsoft discovered that men were filling most of its engineering roles, they replaced numerical and verbal reasoning tests with video assessment and strengths-based tests. This approach led to women scoring 11 per cent higher than men in the assessments, and a 50/50 balance between male and female hires.
Machine learning can provide vital clues on the effectiveness of a firm’s approach to recruiting and developing talent. Using these insights, companies can create the conditions that allow more diverse and higher quality candidates to filter through.
Intelligent machines could hold the key to eradicating unconscious bias and creating more diverse and higher quality talent pipelines, by helping recruitment and HR professionals base their people decisions on the candidate qualities that really count.
Will Hamilton is the founder of LaunchPad Recruits.
Meanwhile, Nigel Dessau, CMO of Stratus Technologies, reflects on how increasing diversity is not only the right thing to do, but how it will also improve your workforce.