Machine Learning is Disrupting HR: What You Must Know

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Machine LearningTraditionally, the collection, processing, and production of HR analytics have been a manual (or at best semi-automated) and slow process. Considering the wide range of responsibilities that HR leads, including finding the right talent for the right job, compensation and benefits, managing employee development and training, tracking and managing the workforce, managing employee engagement, internal communications, and managing exiting employees, it is easy to understand how a manual data process would hamstring the organization. As the demand for HR analytics has grown with the demand for all types of data and analytics, it is not surprising that HR managers are embracing machine learning as a potential solution to this problem.

First, let’s start with a definition: what is machine learning? From Wikipedia, “Machine learning is a field of artificial intelligence (AI) that uses statistical techniques to give computers the ability to “learn” (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.”

Unlike manual or semi-automated data analysis and processing approaches, machine learning is much faster, and therefore more responsive to dynamic situations, making it more valuable as it provides timely, accurate, and actionable information. This allows HR to provide more value to its current functions, such as predicting attrition, identify the candidate most likely to be successful in a specific role, and enhance training and professional development.

HR is arguably the most important function in any business. Unless human capital is managed well and optimized, organizational performance will suffer through low productivity, absenteeism, and turnover. Machine learning is being touted as the panacea for today’s modern HR organization which thrives on regular, updated, relevant analytics. So, what can it do?

There are many ways that machine learning can positively impact today’s HR organization:

  • Faster, Nimbler Response to a Changing Environment. Employee attitudes and sentiments, credentials and qualification, benefits data, compensation trends, the economy, and the job market, as well as information about competitive organizations – all add up to a lot of disparate data sources and data types.  Machine learning can process this data in high volumes, and provide relevant and actionable results.
  • It is not enough to report historical data; HR must predict data trends to recommend appropriate actions to the organization. Machine learning can often predict such key indicators as attrition, employee performance, need for training and development, and even adverse events such as unethical behavior.
  • Hiring Decisions. Machine learning can sort through the mass of information available on candidates to find the right talent based on the job role, and the individual’s credentials, experience, and interests. By using machine learning, businesses can find the right job candidates in online forums and social media, opening the way for a new, faster, and potentially more accurate approach to talent acquisition.

More and more businesses are using machine learning in their HR functions. The faster, more agile approach to processing data gives HR information to manage rapidly better evolving environments. Moreover, machine learning works great – until it doesn’t. Again, from the Wikipedia definition of Machine Learning, “These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.” Just recently, Amazon learned this lesson and scrapped its machine learning based hiring technology because it was introducing a bias against women candidates. Apparently, when you rely on historical data for machine learning applications, you may end up repeating the past, which may not be appropriate.

While machine learning HR applications are proliferating in many types of businesses, we must keep in mind that the field is still developing. While many organizations are seeing a boom from machine learning in the speed of accessing and benefitting from information, machine learning is expected to become even more accurate and prominent in the future.

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Kyle Burnam

Kyle Burnam is the CEO of Infosurv and the leader of its sister company, Intengo, where he oversees all client research and R&D projects. Having been in the industry since 2005, Kyle brings a wealth of experience to the table and an innovative eye to every project.