Consider this. A dedicated recruiter can spend in excess of two months of their time reviewing initial candidate applications. However many recruiters a company has, the time this take totals into a huge amount of months a year spent supporting just the initial review activity.
Artificial intelligence can revolutionise this process. It offers recruiters the opportunity to take evidence-based decision making to an entirely new level by factoring in an unprecedented amount of data from a wide array of sources, some of which might never have been considered previously.
Yet adopting new technology can always be a scary thing. Only recently, it was reported that an algorithm being tested as a recruitment tool by online giant Amazon was sexist and had to be scrapped. Whilst unconfirmed, this news raises a number of questions about how artificial intelligence or machine learning should be applied to talent acquisition in a way that will not pick up the unconscious bias of humans.
There are big benefits to AI in recruiting because in theory it affords employers an even greater ability to quickly flag candidates that have certain key indicators of success, thus streamlining the selection process and affording more time to nurture top talent ahead of competitors.
Constant machine learning will work to reduce unconscious biases and enhance diversity by uncovering strong candidates who may have gone unnoticed in a non-intelligent or manual process. In turn, recruiters gain insight and reasoning into which characteristics score the strongest.
Oleeo commissioned the Department of Computer Science at University College London to look into how algorithms can ensure that they do not inadvertently fall into gender bias, as Amazon appears to have done.
It revealed that removing any wording or phrases that could unconsciously predict the gender of a candidate would enable algorithms to make any gender prediction to be no better than random with no direct impact from the loss of information in the transformation and de-biasing steps.
Working in this way allows employers to foster diversity and accelerate candidate selection, promising no adverse selection in compliance with established selection rate guidelines around the four-fifths guidelines. Customised algorithms can elegantly handle high-volume automation and deliver at-a-glance qualified, quality candidate recommendations critical to recruiting success in large-scale hiring events.
It’s important though to put this into focus. An AI future is not about people versus machines, it is about people and machines collaborating in harmony using intelligent organizational design. After all, technology was created by people to enhance their lives. So, AI should be considered more as a leveller helping any recruiter to highlight the diamonds in the rough that no one else knows about.
Increasingly, companies want to do the right thing when it comes to fostering diversity from the start of the recruiting process. In terms of compliance, however, we’ve seen companies don’t have standard processes in place to ensure they are meeting set standards. Correctly tuned algorithms can help companies shift from being reactive to proactive in balancing the need to accurately and quickly identify high-quality candidates while simultaneously ensuring compliance.
This can lead to a greater democratisation of recruitment by:
- Recommending candidates who unequivocally perform better: delivering more sales, staying longer
- Better record keeping / reproducible decision making
- Removing the economic bias to exclude
- Enabling employers to better understand what drives performance
- Moving away from the familiar “tried & tested” and so on…
The automated cycle of recruitment means you should have a better talent pool of candidates coming through that reflect the future leaders you want joining your organisation. Clever data techniques will recommend candidates who unequivocally perform better and thereby deliver more revenue, profit, or stay longer in the business. It means that a business can go on to use algorithms based on how employees perform in the business rather than what line managers decide at interview.
In so doing, it is feasible that technology could effectively free up 66 months of recruiter resource each year – time which could be spent on adapting better engagement techniques to ensure a leading candidate with many offers at their disposal is more likely to buy into the culture, mission and vision of our clients ahead of market competitors with equally tempting offers on the table. In the recruitment game, closing down top talent ahead of competition is a big challenge and this technology is helping to offer a solution to this and reduce decline rates to suit corporate objectives.
To summarise, AI plays a crucial role in helping firms reduce reliance on gut instinct of recruiters and hiring managers by enabling them to effectively utilise the plethora of recruiting data they already have e.g. data on high, medium and low performing employees; candidate demographics, sources of hire and background data; assessment and psychometric data; structured interview data etc.
