Want a job? Millions of individuals are submitting their resumes and posting them on job boards and resume databases in hopes that a human will eventually read how their experience and capabilities, compared with the company’s needs, are a match made in heaven.
Similarly, recruiters are scouring their networks - and those of their colleagues and clients - to identify the prime candidate for a given requisition.
In the world of the year 1990, or of the year 2000, shuffling through a stack of resumes may have been the case. But in the year 2017, a machine is the first to be slicing and dicing qualifications - yours if you submitted your resume for consideration, and your network’s if you are trying to harvest candidates via self-proclaimed experience and endorsements of social networks.
If the machine learning algorithms don’t recognize a candidate’s applicability or the member of your network’s suitability, then the AI sends them into the circular-file instead of to the hiring manager.
In CIO’s piece on How AI is Revolutionizing Recruiting and Hiring, we are told of reverse engineering the process to find the “perfect fit.” They compare it to Moneyball - matching specific skills/traits/expertise. The bottom line, the more you know about a given candidate, the more likely the AI/machine learning toolset can provide you predictive analysis on how an individual may do in a given role.
For an internal candidate, the amount of data would be significant. For an external candidate, it may be considerably less. You’re going to have to fill in the delta with interview data.
The importance of natural language capabilities cannot be overstated. Keywords and synonyms used in context or omitted entirely, but the framing of a specific skillset described can be instrumental at pulling from the submitted resume skills sets which may not be identified by a specific keyword.
While over at Boon, in a company blog, they discuss how machine learning has changed recruiting by looking at how recruiters might use these capabilities. The example used is a network of 5000 individuals on LinkedIn, the social network for professional engagement, and how “recruiters can start to recognize pure data points of candidates’ contact information, their profile, their work history, etc. and be able to match those with opportunities.”
Similarly, the algorithms may be tweaked to identify those developers who tend to move on to their next opportunities on a regular cadence, and thus are ripe for the picking.
One could argue that by monitoring social network chatter, that one can also identify candidates ripe for an approach about their next opportunity. Truly, there isn’t a one of us who hasn’t read of an individual whose bad day ends up in a diatribe about their employer. Those bad days are the recruiter’s opportunity, perhaps.
Thinking out of the box is a plus, as revealed in a piece offered up by Recruiting Daily, where they articulate why machine learning and AI matters in recruiting and hiring.
They compared the process to Waze, the traffic avoidance application, where everyone participates and we all get home quicker. The piece implies, when the candidates, recruiter and manager are all participatory, a “learning loop” occurs which permits greater knowledge about a specific candidate and their appropriateness for a given role.
As HR teams embrace AI and machine learning to slice and dice us as we push our resumes into the candidate queues and pools, one must be sure to review their AI and machine learning algorithms to ensure the rules are legal.
If you can’t ask a candidate a question about marital status or age, your AI shouldn’t be tasked with calculating age based on experience and then excluding those over 50.
In sum, AI has a place in human resources, and candidate identification, selection and assessment. As of now, it cannot and does not replace the visceral and professional input of the human.
The hiring manager and recruiters ultimately have to decide if a candidate fits or doesn’t.
Companies will be well served to ensure that all possible candidates are making it through their filtration system and inserted rules are not exclusionary, but inclusionary to bring the maximum number of qualified candidates to the table, even those with “out of band” resumes.
About Christopher Burgess
Christopher Burgess (@burgessct) is an author and speaker on the topic of security strategy. Christopher served 30+ years within the Central Intelligence Agency. Upon his retirement, the CIA awarded him the Career Distinguished Intelligence Medal, the highest level of career recognition. Christopher co-authored the book, Secrets Stolen, Fortunes Lost - Preventing Intellectual Property Theft and Economic Espionage in the 21st Century (Syngress, March 2008).