Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

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Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

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How Machine Learning is improving the Hiring Process

By Jyoti Nigania |Email | Mar 26, 2018 | 5616 Views

Advancement in Technology into all industries and jobs tends to send ripples of worry with each evolution. It started with computers and continues with artificial intelligence, machine learning, IoT, big data and automation. There are conflicting views on how new technology will impact the future of jobs. But it's becoming clear that humans will need to work with technology to be successful especially as it relates to the hiring process. Machines are better at making processes more efficient. For example machine learning brings value by processing job applications faster than humans who can reduce the amount of time it takes to recruit and hire a new employee.

How Machine Learning is improving the Hiring Process

  • Recommendations to the Rescue 
HR professionals today use recruitment platforms to find potential employees through a search based system where they can narrow down a list of candidates based on factors like skill, industry, experience and location. But with machine learning capabilities, hiring managers don't have to manually dig through applications from hundreds of candidates to find the best fit. Instead, they can rely on networking and job sites to leverage machine learning and offer intelligent recommendations on the candidates who can fill a given role. This enables a more efficient hiring process for both job seekers and recruiters.

  • The Elimination of Bias
Machine learning can help level the playing field in hiring. It can be employed to provide equal exposure to opportunities, regardless of a candidate's pedigree or background. Algorithms should focus on skill based data, not on the universities where a candidate has studied, the companies where they have worked, or their ethnicity or gender.

One factor to consider is that candidates simply don't know their value and what compensation they should ask for. This is another area where machine learning can help. It can expose salary data for a candidate's specific role and geography, thereby making them better informed. On the employer side, it can also analyze and source salary data. This gives companies a clearer picture of a suitable salary offer, which is based on a candidate's skills and experience instead of their previous salary.

  • Awareness
Humans are inherently biased. But in most cases, we suffer from unconscious bias. Machine learning can help humans overcome these biases with data. Leveraging data, we can create awareness around preferences of hiring managers and recruiters. We have a natural propensity to surround ourselves with people who look like us. This is where data can help. By surfacing statistics around diversity, employers can be better informed and not risk losing out on diverse candidates.

Machine learning can quantify these outcomes for their organizations, which will then incentivize better human behavior when it comes to hiring. Organizations that rely on machine learning to strengthen their hiring processes will probably find that one of the biggest challenges is building or using a platform that's free of biased hiring and wage gaps.

Conclusion
Machine learning will bring the most success to the organizations that use its capabilities to increase productivity for their employees. This is especially true with hiring. Where machine learning can help narrow down and suggest job candidates, hiring managers can handle interviewing, negotiating and understanding the human on the other side of the table. Machine learning can quantify these outcomes for their organizations, which will then incentivize better human behavior when it comes to hiring.

Source: Forbes