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Diversity and inclusion

How Machine Learning Could Allow Organisations To Make Fairer People Decisions

Will Hamilton

old-computers.jpgDiversity and inclusion is increasingly being legislated within organisations, but is something that firms still struggle to effectively implement. We can’t train out bias, but we can harness technology and data to help inform recruitment decisions.


Forward Thinking Approach

It’s encouraging to see large organisations like the BBC taking steps to make their workforces more diverse, especially given robust data stating that more diverse companies are also more successful. For example, the McKinsey report Diversity Matters reveals that businesses in the top quartile for racial, ethnic and gender diversity are more likely to outperform those in the bottom quartile, as more diverse organisations are better able to win top talent and improve employee satisfaction.

The problem is that tackling bias through measures such as name blind CVs, unconscious bias training, appointing Heads of Diversity, and so on, aren’t enough. Organisations still struggle to weed out unconscious bias from their recruitment processes, demonstrated in the slow progress of corporates towards hitting their diversity targets. With gender pay parity transparency expected to come into force in the UK this October, the issue of equality in employment has never been more pertinent.

Machine Objectivity

Machine learning is the new frontier in eradicating unconscious bias, allowing orgnisations to make fairer people decisions and hire the best candidates, regardless of their ethnicity, age, gender or sexual orientation.

We can’t remove unconscious bias from our own brains, but we can program computers to outsmart it. Data can be objective where we can’t, and can be used to enhance our own capabilities.

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.

Applying artificial intelligence to recruitment benefits workforces in more ways than one, and can also reduce the cost of hire, by reducing the time spent sifting through applications and CVs. This is particularly relevant to large corporations that may get hundreds or thousands of applicants for each position.

Employee Benefits

Boosting the diversity of organisations is great news for businesses – increasing innovation, staff morale and diversity of thought. Companies who are seen to hire, promote and nurture employees from a wide range of backgrounds will also gain a reputation for being progressive and a good employer, as well as improving their services, for example by having multi-lingual employees.

However, a diverse and inclusive workplace is also great news for the employees themselves. Candidates with the best skills and qualities for an organisation are likely to be the most motivated, loyal and engaged. They are also less likely to have personality clashes with their fellow employees, keeping productivity high and improving collaboration.

Data-led software also enables HR teams to identify and retain the highest performers, meaning that promotion decisions are based on measurable traits rather than subjective opinions, further boosting employee engagement.

Consequences of a Bad-Fit Hire

Machine learning holds benefits for more than diversity, and can be effective in reducing attrition. Hiring an employee that doesn’t fit in with the company is a multifaceted problem, and one familiar to many businesses of all sizes. Recruiting, hiring and training people is costly, both financially and in terms of resource, and at a time when the UK’s productivity gap is widening, every employee has to be making a real contribution to a company’s growth. A bad-fit employee is also unproductive and demoralising, and can have an impact on the rest of the team, who have to increase their workload to make up the slack.

With so much at stake, it’s crucial that recruitment processes are fit for purpose. Removing bias from hiring decisions enables recruiters and HR professionals to concentrate on honing in on the most important qualities – skills, experience and organisational fit.

Finding Top Talent

With many sectors still suffering from skills shortages, firms cannot afford to let bias narrow their view and risk missing out on the best candidates.

In order to effectively augment decision-making with data and smart technology, it’s time to remove the bias and focus on what really matters – finding the best candidate for the job.

This article originally appeared in the August Issue of HRDirector Magazine.

Will Hamilton

Will founded LaunchPad Recruits after 13 years spent working as a management consultant. Will’s corporate background easily translates to his creative talent and vision of building technologies and processes that really work. Although initially setting out to create a brilliant Video Interviewing platform, he has managed to establish a selection tool which delivers so much more.