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Three Key Applications for Machine Learning in the Recruitment Industry



Although artificial intelligence (AI) will someday make many different jobs redundant, there will constantly be certain occupations that may only be executed by a person. For instance, recruiters don’t have to worry: their tasks require training, relationship building, and strategy development – and algorithms can’t perform these things for them. These components of the work make recruiting so valuable and rewarding, and AI helps recruiters do more.

Machine learning is only an essential part of AI. Software or machines are programmed to learn from information, identify patterns, and utilize data to make smart decisions. Consequently, there is considerable potential to improve recruiters’ jobs. Here are three key areas where machine learning is assisting recruiters.

1. Applicant Relationship Management

A way that human resources agencies are applying machine learning is through chatbots to automatically connect with applicants. Through machine learning, chatbots can find the best answers to various applicant queries and then network with them on behalf of the recruiter. This is mainly useful since it exempts valuable recruiting time. More refined software may also automate call and meeting scheduling.

2. Applicant Shortlisting

Another important machine learning task is helping recruiters choose candidates for particular roles. Through the use of machine learning algorithms to applicant data (from a variety of channels and sources, including employee history, social media, and employer data), it is possible to list the most skilled applicants based on the desired experience.

A similar process may also be used to screen CV; a machine can do this if it recognizes what conditions to search for. The recruiter is responsible for conducting the interview procedure and beyond. Machine learning not only speeds up the process but also help deal with another recruitment problem: discrimination. It only selects candidates following their suitability and qualifications for each position.

3. Placement Probability

Enhancements in machine learning have played a great role. There is a great chance that you will learn more concerning the idea of “placement probability” when conversing about candidate selection. By evaluating a potential candidate’s data against previously effectively placed applicants, algorithms may determine the probability that a particular candidate will acquire the job and allocate them a mark that reflects this.

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