The academic background of most people in recruitment function is usually non-tech
There feels an onslaught of a variety of jargon-laced machine learning and AI solutions on them
Should keep into consideration the three flawed proposition made by the HR tech solution providers
You are a successful entrepreneur, your startup recently got funded and now you are required to hire “people” at brisk speed to meet the growth target as envisioned with investors. Most likely, the first step you will take is to hire a seasoned HR lead to chaperon your hiring and people growth. You will also expect recruitment function to use technology to bring in speed, efficiency and effectiveness to the whole process.
However, there are three serious dangers lurking behind the adoption of HR tech, due to the sole reason that the 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. Therefore, people in the 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 a variety of jargon-laced machine learning and AI solutions on them. Thus it is recommended that a startup entrepreneur should keep in consideration the three flawed proposition made by the HR tech solution providers.
Machine Matching Between Job Description And Resumes
Two poems can have identical vocabulary and number of words but the meaning of the two poems can be entirely different. Similarly, human beings are innately different and represent abstract properties through their CVs even with similar skills.
Thus, the HR tech professionals and startup entrepreneurs should understand that the attempt to forcefully match the density of certain 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, maybe a steed from Kentucky and what not.
The reason is that mathematically speaking, abstract but most important properties of a candidate are represented by statistically insignificant, usually just one or two words in the entire CV!
While this method feels like progress, it loads the recruitment function and HR tech professionals to weed out mule and zebra through several screening rounds, which are enormously costly and delay-inducing.
Historical Hiring Trend based Projections
The usual instinct says that “why not hire kind of people we have hired so far”. The solution finds its way to the CV of previously hired people and tries to find similar in the prospect pool. Any economist will clarify that projections based on historical trends are valid if and only if all the environment variables of the past are unquestionably valid in the present day as well.
Given the economic growth, competition and arrival of new technologies, both candidates, as well as the startup and their roles, are constantly in an evolutionary state. Big data or no big-data, the very thought of using this method either must be very carefully planned or rejected altogether, lest one should get surprised by an issue such as gender discrimination introduced by such algorithms 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 an automated solution. However, some serious flaws creep in while executing it.
Most often, while companies have the as-received CVs 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 the ability to plan, perform and deliver outcome even while using identical tools and artefacts (an ocean swimmer is a different person than a pool swimmer). Hence, another 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 may be a tech startup entrepreneur who has 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 for.
One can remember an adage that “people with a hammer in their hands, are always looking for nails”. However, with no deep understanding of the problem, these initiatives invariably end up taking one of the 3 approaches mentioned above.
So, when you are evaluating technology for hiring, make sure you ask explicit questions on the solution approach they are using.
