Onrec logo The Online Recruitment Resource

Taking people out of the recruitment process, for the better?

It’s nothing new to hear that AI and machine learning are set to disrupt traditional processes and industries over the next few years. But whether or not this will leave us better or worse off is up for debate.

In recruitment, there is huge potential to use AI to remove discrimination and improve diversity and inclusion. Human-decision making is indelibly tinged with bias, with a range of factors subconsciously affecting a recruiter’s choices. From race and gender to accent or weight, it’s an unfortunate truth that inadvertent prejudices can influence recruiters’ judgement – there are numerous examples of Alexandras getting further in the process when they begin signing off emails as ‘Alex’.

GP Bullhound’s Technology Predictions 2019 Report has found that the use of AI and machine learning in recruitment and employee engagement would make the process of bringing on board new hires fairer, more efficient, and more fully integrated, by removing opportunities for bias. AI has the potential to change the process by replacing the human characteristics that make recruitment unfair and inefficient.

Machine learning also has the potential to override this inherent bias in recruitment – something which is only good news for firms aiming both to improve diversity and to recruit and retain the top talent.

There are countless examples of this technology successfully streamlining the recruitment process. Entelo and Wade & Wendy are using AI to enable an enhanced engagement with candidates during the recruiting process. Meanwhile, AllyO have used AI and machine learning to help clients increase capture and conversion rates up to six times over a more traditional recruitment approach.

The risk with AI in recruitment, however, is that bias becomes institutionalised rather than eliminated. AI requires training data, so it must learn from current management styles to develop its own practices. If a company’s current recruitment processes are biased, discriminatory, punitive or overly hierarchical, AI will learn to perpetuate this rather than uprooting it.

AI must be tuneable, with frequent tweaks to the algorithms necessary to override biases. Behavioural analytics must be used carefully - if AI becomes an independent decision-making system, it risks creating more problems than it solves.

It can, however, greatly streamline the recruitment process, making it fairer and more efficient. As with any new technology, the potential is there, but implementing it across the board effectively requires care, diligence and access to the relevant data.

For one, data and AI tools can be used to sort candidates’ profiles and identify the most suitable candidates. Over half of talent acquisition leaders state that the most difficult and time-consuming part of their job is identifying the right people from an endless pool of applicants.

It’s not only AI that will help, as there is also huge potential for technology to enhance recruitment functions through data analytics. The recruitment industry already has a huge range and depth of data at its fingertips, but it is failing to use this beneficially.

By using this data more efficiently, recruitment teams can develop a better understanding of what roles in which markets will pose challenges for them, as well as predicting periods of mass hiring and plan for growth initiatives based on the talent available. Data will become fully integrated into the candidate life cycle, and will fundamentally drive how, when and where firms recruit.

AI and machine learning will have a major impact on the recruitment process, bringing greater efficiency, fairness and accuracy in talent acquisition. But if this is to be implemented effectively, AI must be tuneable and transparent. For this to succeed, it requires leadership, great engineers and holistic company strategies, but the impact it can have on streamlining this industry will have numerous and hugely valuable benefits.