An excursion into HR: Charles Hipps talks AI and recruitment.

Reading time: 4 minutes.

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 takes 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.

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.

Used well, practitioners can test theories, proactively solve problems and conduct more complex predictive analytics related to sourcing and hiring strategies. This isn’t necessarily something that’s in the future, it’s already here. You’re already using it in terms of your habits online and are inadvertently in a situation where you will be using AI to do most aspects of your life. 

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.

What do I mean? When you’re using aggregated data sets to make decisions, you need to make sure that you’re correcting for existing biases. Obviously AI technology has advanced a long way in recent years, but it would be foolish to assume that bias is no longer a problem.

It is important too that a tool never works based on historical data alone. Efforts must be made to constantly improve the robustness of any tool to help leading employers benefit from the best possible early evaluation of applicants based on responses given within online application forms.

This can be achieved by collecting enormous amounts of structured and unstructured data, processes that data using the best from thousands of machine-learning algorithms to most accurately predict outcomes, and refines that process as it learns.

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.

The Department of Computer Science at University College London looked 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.

In fact, more consistent disparate impact scores of close to 1.0 (i.e. no disparate impact observed) are recorded in hiring predictions undertaken in this way providing better hired prediction performance. It is also shown to have consistent negligible disparate impact across a range of hiring values, providing room for adjustment in recruitment screening thresholds without increasing disparate impact.

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 organisational 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.

This can lead to a greater democratisation of recruitment by:

• Recommending candidates who unequivocally perform better
• 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 and 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.

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, and structured interview data.

About the author

Charles Hipps is CEO of Oleeo