In Isaac Asimov’s fantastic science fiction volumes, the early part of the 21st Century has working artificial intelligence to which much of mankind’s decisions have already been outsourced. All humans are routinely tested by the AI, which determines which career their brains are best suited for. While technology has not yet caught up with Asimov’s boundless imagination, recruitment algorithms have begun to use cutting-edge technology and pattern analyzing software to help HR personnel select the right candidate for a job.While these technologies cannot be substituted for a human’s judgement, they can process information and do the mechanical parts of the process, increasing the efficiency of the recruitment funnel.
One of the biggest challenges in recruitment is getting the right kind of information that will answer questions about the candidate that are relevant to the company. Is this the kind of employee that will stay for a long period of time? Will this candidate fit in well into the team and the role? Does the candidate have the required skills? If not, does the candidate have the capacity to learn at a fast enough rate? In spite of various attempts and systems, no process can claim to capture the answers to all these questions in an efficient and accurate manner. Much of the process is eventually predicated on instinctive calls and personal opinions, which can lead to costly and time-consuming mistakes.
Several companies attempt to do a thorough check of an individual’s social media accounts to gather more information about them; this, however, can be misleading and also an invasion of privacy. Human recruiters can come to various different conclusions about a candidate through such an exercise, mostly due to their own inherent biases and previous experiences. Algorithms have been created that can gather information from an individual’s social media account and come to remarkably accurate predictions about their character based on the information provided. Further, since it is being done by a computer program, it is less likely to result in a severe privacy breach than if a human being does so.
Data analytics being run on potential candidates is one of the most exciting developments in recruitment technologies. Currently, recruiters and candidates both search for each other, often relying on jargon-laced keywords and descriptions to determine the right ‘fit’. The variety in these jargons and terms often makes it harder for the right candidate to find the right job. If they search for ‘digital marketer’, they will receive substantially different results than when searching for ‘growth hacker’. Inadvertent mismatches created due to skewed understandings of job roles are far too commonplace. Algorithms, with their perfect recall and dynamic memories can automatically trawl through millions of potential candidates, and select the ones that match the criteria supplied to them. Their speed, accuracy, and reach enables them to find the right candidate far better than human recruiters.
They will still make mistakes, or be misled by errors made either by the candidates or recruiters feeding them the information. But when machine learning and data science are added to the mix, these algorithms can learn and understand requirements better. They can collect the information and tailor the perfect description that catches all the relevant jargon to ensure the best discovery potential for the vacancy. As the algorithm and the system recognizes instances where it makes an error, it keeps adding to its data store and becomes more accurate. Over time, recruitment algorithms will be able to reach unimaginable levels of accuracy – certainly better than the shot-in-the-dark kind of hopeful recruitment that several companies still undertake.
Eventually, recruitment softwares could reach the level of being comprehensive talent management suites. By tracking the skill-sets available within a company and its requirements over time, such software could eventually predict the need for adding a new employee in a particular department, and indicate which skills such a candidate would require. The software could probably post the vacancy or find the candidate in a manner that would mean that they would be recruited exactly when they are needed. This would remove the delay that constantly hobbles companies as they scramble to find the right candidate for vacancies, but are constrained by the time taken to screen the candidate.
We are several decades if not centuries away from Asimov’s AI; technology is still incapable of making subjective judgements about how an individual will fit into a company’s culture and work ethic. The human element of the recruitment process is still integral in making the final decision about the suitability of a candidate, how well they might fit into the company and the team, and how much value they will add to the working environment. These new-age algorithms can make every part of the process up to that last decision easier. They can inform their human counterparts of the need for a candidate, find a set of suitable individuals, and accurately predict how capable they are to perform the job they are being asked to. The data-crunching capabilities of technology and the human decision at the end of the process make for a fairer and more efficient recruiting system.
For any hiring requirements, do visit the OLX People website.