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.
How is Machine Learning and AI Being Used in Recruiting?
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 (Read more...)
This is a Security Bloggers Network syndicated blog post authored by Christopher Burgess. Read the original post at: Cylance Blog