Everywhere you turn, the concept of Artificial Intelligence is surrounding us. It started a few years ago with the ability to gather huge amounts of data and use technology to analyse that information. The subsequent rise in use of analytics, particularly predictive analytics, has transformed almost every facet of our lives. Now we are using AI and Machine Learning (ML) increasingly with goals of 1.) Increasing our efficiency through having less repetitive and low-value tasks and 2.) Enabling us to make better decisions. But are we ready for that when it comes to talent acquisition?

Understanding the Terminology

First, there often is confusion between the terms AI and ML, the interrelationship between them, and how Big Data fits in between. Just to add to the mix, there also is Deep Learning to untangle to understand the differences. So, just what do these terms mean?

Artificial Intelligence – machines conduct themselves as a human would, using human intelligence. Tasks are done smartly. It includes human skills such as understanding language, recognizing objects and sounds, and problem solving.

Machine Learning – a method of achieving AI; machines learn through exposure to information and are not coded to do specific tasks.

Deep Learning – an approach to ML that gets its name from the way our brains function with layers of the neurons combining for a deeper understanding.

We are steadily moving towards AI that thinks as a human. We are seeing some of it already– when you go on the internet after binge watching a Netflix series, you will see ads that are similar to the program you watched. It may be annoying and like “Big Brother,” but you are much more likely to be interested in those targeted ads than random promotions for diapers. AI is also used by email providers to filter spam.

Downsides in the Current Technology

AI is significantly impacted by the exposure to information that is biased, which caused the debacle of Microsoft’s Tay Twitter chatbot in 2016. Designed to interact and learn from other users, within 24 hours the experiment was withdrawn as Tay quickly learned – and repeated – racist and other inappropriate comments. Additionally, AI technology evaluates a mass amount of historical information which results in biased results. For example, you want to source your next great executive and you use criteria of other successful executives in your company or field. The problem is that the results will likely show a white male because, statistically, that is the demographic that has held most executive roles regardless of their ability.

In theory, AI has a great appeal for recruiting as it’s perceived to be able to remove unconscious biases that all humans have. But the human interaction in recruiting – the ability to establish and nurture relationships – cannot be replaced by a machine. Also, technology is not able to truly assess cultural fit and whether a candidate has the right attitude to do the job. I recommend Fortune’s Unmasking A.I’s Bias Problem for further reading.

How Recruiters Can Use AI Today

Perhaps, for now, ML is the best tool for recruiters to leverage. ML enables us to streamline and automate sourcing activities by being able to analyse millions of resumes or bios to identify potential candidates that recruiters then can hone in on. It’s much more sophisticated than the keyword function of early applicant tracking systems and results in a much higher rate of success. Other tools that are being used to reduce the low-value (but necessary) recruiting tasks are scheduling interviews, screening candidates, keeping stakeholders informed about the process, and conducting background checks.