Machine learning in hiring
How can machine learning in hiring change your recruitment process?
Machine learning in hiring to make recruitment easier—does this sound like a dream come true? Dive in to learn more!
Artificial intelligence and its wonderous capabilities are what everyone is talking about at present, and rightly so. There are some more incredible things that AI and machine learning can achieve in the world of business, and hiring is not far behind.
Even before chatbots and Web 3.0, we have tried using AI to improve our working process. The result is what we see today: AI is everywhere, and hiring is just one of its unique quirks. The good news is that it has made tech hiring much more straightforward. Here is how:
Pave the way to smarter hiring processes with AI!
We live in exciting times where AI’s use in recruitment is a surety like never before. However, we have been using AI for hiring processes, which is not entirely new. For over two decades, several online job-hunting platforms have promised to scrutinize applications before sending in the relevant ones for job postings.
Machine learning is a subset of AI, and ML makes a lot of difference in present-day hiring. So, why are we discussing it now? Let’s dig in deeper to analyze this further.
AI and Machine Learning in Hiring:
AI, in a broad sense, is the technology that refers to the use of technologies to build systems that can imitate the cognitive thinking abilities of the human mind. Machine learning helps the systems learn the process to improve the user experience. So, machine learning is the one that is ‘teaching’ the AI-integrated systems how to operate or screen, scrutinize, and shortlist candidates in a recruitment process.
With machine learning in the hiring process, things have improved. ML has put its stamp on every stage of the hiring process and has made things more automated, system-generated, and basically, all we know about artificial intelligence and machine learning.
With AI in the recruitment process, things have simplified significantly, and here is how:
Screening of tech talents:
According to LinkedIn statistics, a recruiter spends about 13 hours a week (almost 1/3 of a work week) sourcing candidates for a single role. Another survey by Ideal.com points out that 52% of talent acquisition specialists believe that screening is the ‘hardest part’ of the recruitment process.
Machine learning algorithms reduce screening time and effort to deliver results instantly from a database. With the use of machine learning in hiring, the screening time can come down considerably. Although there are challenges before one can make it happen, the algorithm needs some tweaks and turns before it sets a rhythm that can work for an organization.
For example, CodeMonk’s recommendation algorithm takes the exact data from the job postings to select suitable tech talents for the profile. A similar setup works for most organizations with some simple search words to scrutinize applications or search their database for the correct picks for a job.
Well, machine learning is all about teaching the systems already in place — the AI technology — to work. Tailoring it to our needs makes AI in recruitment all the more exciting, with the benefits of ML thrown into the mix. So, it’s up to the organizations to work out the minute details to bring down the most exhausting aspect of recruitment — candidate screening.
Interview and assessment
Machine learning in recruitment can make interview scheduling and assessments fully automated and quick. For employers, feeding their calendar schedule and availability can help fit in the candidates for the interview. For talents, they can check the recruiter’s availability and pick the best date. The whole process is quick and simplified, thereby reducing the hiring process.
The onboarding process for tech talents
Tech talent today looks for quick hiring and onboarding processes. Research on good or bad hiring practices found that employee retention is about 82%, with good onboarding processes for new hires. If you think about it, it’s pretty simple. AI in hiring can create a simplistic approach to the whole onboarding process. After a month-long hiring process, simple things like document checks and verifications or other onboarding processes can tire the new hires of the whole approach.to the entire
Why is using machine learning in hiring the best bet?
Before answering that question, let’s figure out this: how much time and effort do you want to spend in the hiring process? Well, if you think it’s way too much, then here is what AI can do:
Incorporating machine learning into hiring can be a good bet for several reasons:
Efficiency in screening:
Machine learning algorithms can quickly analyze and screen thousands of job applications, saving recruiters significant time and resources.
Accuracy in skill assessments:
Machine learning algorithms can accurately assess a candidate’s skills, experience, and fit for a role by analyzing data points from various sources, such as resumes, social media profiles, and skills tests.
Predictive Analysis:
Machine learning algorithms can anticipate which candidates are likely to be successful in the post by examining data from previous successful hires and recognizing trends that suggest probable success.
Unbiased hiring process:
Machine learning algorithms can eliminate biases in the hiring process, such as gender, race, age, and other factors that may consciously or unconsciously affect human recruiters’ judgments.
Perfect Scalability Options:
Machine learning algorithms can be easily scaled up or down to handle large job applications, making it easier for companies to manage recruitment during busy periods or when recruiting for multiple roles simultaneously.
Improved experiences:
Machine learning might assist in streamlining the recruiting process, requiring less time and effort from hires while giving a better overall experience.
Machine learning in recruitment: challenges to overcome
While machine learning in hiring can benefit organizations significantly, several challenges need addressing before we can go overboard with it.
Some of the challenges include:
a. Data availability and feeding:
As we discussed in the beginning, machine learning involves feeding a substantial quantity of data to AI systems to make them think how we want them to. However, the data must be accurate; otherwise, it could lead to undesirable outcomes.
b. Bias:
While machine learning could eliminate human discrimination, it can also create preferences if the data used to train the algorithm is biased. Ensuring that the data used is varied and representative of the population is critical.
c. The human point of contact:
While AI in hiring can enhance efficiency, objectivity, and accuracy, it can also feel impersonal to candidates. As a result, it is critical to combine machine learning with personal touches to produce a favorable applicant experience. The use of automated text to update the talents can help. However, providing a human point of contact for candidates to contact with questions or concerns can ease things up and give that human touch.
While there are challenges galore, machine learning in Hiring is very much here to stay. It not only eases the job of the hiring managers but eases up the candidate's experiences with multiple recruitment processes. However, all of it depends upon the initial research into building the ML experience for both parties in hiring.
For instance, CodeMonk employs a data-driven, tailored recommendation system to improve user interactions and the recruiting process. But it’s a work in progress, and there’s lots of study going on to make it better in the future than it is now.
So, while the use of AI and ML in the recruiting process will undoubtedly usher in a new era of recruitment, much work has to be done before we can truly excel at it.
