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HUMAN RESOURCES- Machine Learning in Human Resources- Applications and Trends- B-AIM PICK SELECTS

Human resources has been slower to come to the table with machine learning and artificial intelligence than other fields — marketing, communications, even health care. Artificial intelligence is transforming our lives at home and work.

At home, you may be one of the 1.8 Million people who uses Amazon’s Alexa to control the lights, unlock your car, and receive the latest stock quotes for the companies in your portfolio.

Similarly, in the workplace, artificial intelligence is evolving into an intelligent assistant to help us work smarter. Artificial intelligence is not the future of the workplace, it is the present and happening today.

The value beyond numbers for CEOs and managers is the power in understanding what’s actually happening within the company, i.e., with their people. Executives and leaders need information that helps them point people in the right directions; information — sales data, KPIs etc. — change over time, and machine learning can react faster than people in helping draw out the insights and inferences that might otherwise take reams of manpower or not be uncovered at all.

That is why it is essential to identify machine learning trends that have the potential to be applied to HR as we know it in the foreseeable future and those that do not. Today, we are interested in the former.

Machine Learning & Hiring

Let’s say a major corporation receives ten thousand of resumes every year. Let’s say they make a thousand hires every year. Let’s say that 500 of those work and 500 do not. Let’s say that this large corporation keeps all the data surrounding these 10,000 applications and 1,000 hires.

They keep track of who saw the job ad where. They keep track of all the applicants’ resumes and they come up with a way to categorize all the data contained in those resumes. They even include applicants’ social media activities in the data they obtain and keep. They keep track of their standardized, structured interview process. They keep track of the language used in the correspondence. They keep absolutely every piece of data that pertains to the applicants.

They feed all this data into software that uses machine learning, and they feed it continuously from the first day. Soon enough, certain patterns emerge.

The software discovers that a certain job ad website yields more successful hires. A certain interviewer is better at identifying the right talent than other interviewers. People who use a certain type of social media turn out to be better employees. The possibilities are endless, especially when you factor in combinations of individual factors and patterns.

A software that utilizes machine learning is the only kind of entity that can hope to analyze all this data and find the patterns. A human HR professional could never do something like this. A traditional, coded piece of HR software could never do this.

It should be pointed out, however, that some of the patterns and tendencies will turn out to be false positives and that a human will need to have the final decision when all is said and done. This kind of advanced analysis and pattern recognition can greatly increase the success rates of hiring decisions.

Employee Attrition

Employee attrition and the subsequent employee turnover is a topic that is being hotly debated these last couple of years, as true costs of high turnover have become widely-known and as companies are trying to do everything in their power to stop this bleeding of talent.

The problem is that when company’s internal communications are done the right way, it is impossible to do any kind of comprehensive analysis of people’s statements, questions, intentions and decisions that would lead to employee attrition. At least it is impossible for a human HR professional.

However, for a piece of software based on machine learning, certain patterns become identifiable. Just as an example, certain responses on employee satisfaction surveys and drops in efficiency can be observed as precursors to employee attrition and their quitting. There are myriad such signals and they often become important in certain combinations that are impossible to figure out for a mere human being. Machine Learning technology has been tested here and the chances are it will get better with time.

The Mythical Engagement

The world of HR has been in uproar about employee engagement for quite a while, with HR specialists claiming that these are the Last Days of Engaged Employees and that there is nothing companies can do to prevent their employees from leaving, or at least, mailing it in.

Employee engagement will always be a human-to-human practice; there can be no doubt about that. However, there is plenty to be gained from smart use of machine learning and software that helps identify trends when it comes to engaging employees by understanding what it is that keeps them happy at their organization. This can be done by having data from a common platform ingested into an ML system which understands and provides numerous ways in having engagement campaigns driven. Ultimately, this helps in ensuring that the employee be retained and prevent them from leaving for “greener pastures”.

Chatbots To Answer Frequently Asked Employee Questions

What if a Chatbot provides real time answers to a range of HR questions, including, “Are we off on Ambedkar Jayanti?” or “What are my medical benefits?” Chatbots are capable of answering any question and answer set that can be stored in a database. They can also be designed to proactively promote benefits to employees they may not yet know about. “Hey Nabeel, have you tried our Meditation class that we are offering in your building today at 3:00 pm? Click here to automatically book yourself. You’ve been working hard and you deserve it!”

There is also an opportunity to track employee issues using real time analytics and then apply sentiment analysis to address these issues. Let’s say that a majority of employees are asking questions about late payments for travel reimbursements. This data can indicate something in the system isn’t working correctly. Before things become a full blown issue, HR leaders can uncover the issue and communicate a solution.

Granted there will be questions chatbots cannot answer yet, but the opportunity is here to provide AI for all types of HR related questions that might be coming into your HR Service Center.

Proceed with Caution!

The use of AI & ML in HR comes with some risks, certainly not as dramatic as the predicted “singularity” of machine intelligence or the sinister SkyNet in the Terminator!

AI algorithms are imperfect, relatively new and not ready to take over HR processes. AI can’t say with absolute certainty, for example, that a person is going to lie based on certain communication patterns. Not only is it inaccurate, but it’s dangerous, and people will get upset. People are already nervous about how their employers might be using their data, so companies should be transparent about how AI will be used and emphasize the personal benefits.

Management should take steps to ensure that AI exists not to simply replace HR workers, but rather it can be used as a way to free them up to do more and better work, such as solving problems the software can’t resolve. Do not use it only as a tool to reduce cost. In addition, human oversight of AI tools is important to catch errors and put recommendations in context. These are learning systems and they’re not perfect.

Choosing the right data is essential to making AI solve real problems. For example, a company that feeds all of its historical data on high performers into an algorithm could perpetuate bias in recruitment if most of those performers were male. Looking at the data in a thoughtful way and crafting your choice of data and your presentation is important. Success in using these technology would be to first establish a baseline to measure improvement.

Closing Word

Machine learning has made some enormous strides over the last couple of years thanks to certain technological advances, but it is safe to say that we have yet to see its full impact on the world of business and HR specifically.

The important thing is not to oppose it immediately and see it as a bringer of doom. The future of HR will most probably involve a human-machine collaboration and that can end up being a good thing.

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