It is a fact that recruitment function is expected to be industry agnostic or in other words a horizontal function. The academic background of most people in recruitment function is usually social sciences or human resource management and seldom in science and technology.
Given this background, an individual in recruitment function perpetually remains on learning curve on technology especially in evolutionary high-tech industry. Therefore, people in recruitment function have always been dependent on hiring teams for technical and functional assessment.
Over and above the natural non-technical and horizontal nature of people in recruitment function, there is an onslaught of variety of jargon laced Machine Learning and AI solutions on them. It is recommended that they understand three flawed propositions made by solution providers.
It is recommended that they understand three flawed propositions made by solution providers.
Machine matching between Job Description and Resumes
Two poems can have identical vocabulary and number of words but the meaning of two poems can be entirely different. Similarly, human beings are innately different and represent abstract properties through their CVs even with similar skills. The attempt to forcefully match bag of words found in CV with those in Job Description is to lose the essence of both.
For illustration, if the Job description is looking for a Horse which can run top-notch derbies, this solution will inherently match contextually irrelevant many four-legged mammals such as mule from Mongolia, zebra from Africa, may be a steed from Kentucky and what not.
Reason is that mathematically speaking, abstract but most important properties of a candidate are represented by statistically insignificant, usually just 1 or 2 words in the entire CV !
While this method feels like progress, it loads the recruitment function and hiring managers to weed out mule and zebra through several screening rounds, which are enormously costly and delay inducing.
Historical Hiring Trend based Projections
As we discussed in other Insight “Machine Learning in Recruitment Space”, any economist will clarify that projections based on historical trends are valid if and only if all the environment variables of past are unquestionably valid in present day as well. Given the economic growth, competition and arrival of new technologies, both candidates as well as companies and their roles are constantly in evolutionary state. Big data or no big-data, the very thought of using this method either must be very carefully planned or rejected all together, lest one should get surprised by issue such as Gender Discrimination at Amazon.
Compare new prospects with 10 best current performers in the role
Argumentatively, this approach of machine matching CVs of new prospects with 10 best current performers in the role appears to be a solid approach and a bright idea in the direction of finding automated solution. Some serious flaws creep in while executing it.
Most often, while companies have the as-received resumes of these 10 best performers which could be 2, 4, 5 or more years older whereas what these 10 are currently doing is seldom available as documents. In this scenario, the method has as serious a flaw as “Historical Trend based projections” described above.
Each human being is unique and is defined by her context towards ability to plan, perform and deliver outcome even while using identical tools and artefacts (an ocean swimmer is different person than a pool swimmer). Hence, other reason to doubt this method is that while it may work reasonably well on skills and tools front, it ignores contextual assimilation of 10 best performers and then comparison with new prospects.
Internal Team approach
There are tech companies who have built products and have successful market offerings using ML and AI. When the hiring teams and reporting managers (product, sales, engineering, customer service teams) are tired of doing costly and tiring screening interviews, they feel motivated to build and offer automated solutions to their recruitment team. Given the nature of non-technical folks in recruitment function and proven ML and AI technical capability associated with hiring teams, it’s not very difficult to get the initiative approved and budgeted.
One can remember an adage that “people with hammer in their hands, are always looking for nails”. However, with no deep understanding of problem, these initiatives invariably end up taking one of the 3 approaches mentioned above.
At Turbohire, what we specialize in, are the those insignificant information bytes in the CV, which define people and then with our proprietary information on real-world and real-work we produce meaningful poetic outcomes.