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HUMAN RESOURCES- How Machine Learning is Changing HR Industry- B-AIM PICK SELECTS

Machine learning is the hottest topic right now, and every industry out there is now on the hunt for new ways of using ML for their own benefits. Today we are going to talk about how machine learning can be applied to an area of human resources and how it revolutionizes it. Don’t let yourself be fooled by the fact that HR is all about humans. In the modern era, you can escape the invasion of technologies, and the right thing to do in this situation is to embrace ML and let it guide the way to a better future of HR.

Machine learning is teaching computers to recognize patterns in the same way as human brains do. It is learning from examples and experience instead of hard-coded programming rules and using that learning to answer questions.

The impact of machine learning in HR

Nowadays the understanding of the HR department has been changing. HR used to be about finding the right candidates, managing assessments, giving offers, managing employee careers and exits.

Human resources today need to step up because the expectations have risen. The HR department has to be able to predict attrition and candidate success.

Before machine learning has come to the rescue, HR managed data in a manual and semi-automated manner. To create analytics, it is necessary to gather, store, and process data. All of the above need to be done in a short period of time because the data would quickly become irrelevant as the situation is changing and the data needs updating.

Let’s investigate what machine learning can contribute to HR.

How machine learning is used in HR


In recruiting, machine learning can be used to analyze blog/social media profiles and identify candidate attributes that might not show up on the resume. Recruiters also use machine learning to proactively find the right people for openings with software that searches the internet to source prospects.

  • Experience;

  • Added insight into candidates, beyond resumes.

Potential pitfalls
  • Status updates or Instagram stories don’t correlate with future job performance;

  • Entails bias.


Video-based interviewing analyzed by ML can help determine an interviewee’s mood, whether or not the candidate is telling the truth, and more. The early stages of interviewing can become much easier with the use of ML-driven chatbots on business site to offer applicants onboarding.

So, the way it works is interview is recorded and is given to the neural network for analyzing it by criteria like muscular contractions and voice tone. For example, if a candidate scowls during the description of his previous job, it can be a sign of negativity. The voice tone can show how a candidate’s enthusiasm or indifference in a job spot or responsibilities.

Machine learning doesn’t care about a candidate’s sex or age, it doesn’t judge or criticize, doesn’t have a bad day. It’s completely unbiased.

  • Efficiency. The average time for a candidate to be hired can be cut from four months to four weeks.

  • Helps an organization to appear more tech-savvy to the job seekers.

Potential pitfalls
  • Requires thoughtful oversight. Be wary of introducing or perpetuating selection bias into input data, which could narrow the search criteria to limit opportunities for underrepresented groups.


Machine learning in the shape of a virtual assistant can take over the routine tasks of employee onboarding.

  • Chatbot frees HR to handle more challenging issues.

Potential pitfalls
  • Lose the opportunity to engage on a human, welcoming level with new hires.


Machine learning provides HR staff with mobile-friendly tools which can gather and share immediate feedback. Applying machine learning to identify themes and recurring issues in employee surveys can help to improve the quality of feedback.

  • There are machine learning apps which let people give or request feedback at any point in the year, without having to wait for the annual review.

  • With ML text analysis, the algorithms can do the grunt work searching through mountains of data.

Potential pitfalls
  • Performance review apps don’t address the issue with performance reviews, which hold people accountable at the expense of grooming talent for the future.

Benefits of using machine learning in human resources

Faster Response to Changing Dynamics

In the era of big data, HR has to manage employees data, basically on everything. Here are some examples:

  • Employee attitudes and feelings

  • Credentials and qualifications

  • Employee views toward policies

  • Compensation and benefits trends

  • Relevant external developments

An enormous amount of data continuously coming and manual management is just not enough to handle it. The solution to this particular problem is using machine learning that is perfect for consistently accepting, storing and processing such data volumes.

Accurate predictions

Machine learning plays the role of a forecaster that can predict key developments such as attrition, success in a job position and even such unpleasant things as unethical behavior. Let’s break down an example of hiring a candidate. An employee success odds depend on past data like previous performance, knowledge and skills, initiatives to improve, data from forum conversations and social media. The prediction is based on the analysis of the described data. Results can be converted to analytics and then decisions can be made.

Workflow automation

It is considered that machine learning in HR was used to automate the workflow. Scheduling is essential to HR with daily things like improving onboarding, scheduling interviews, and follow-ups and HR queries. But usually, it’s a fairly time-demanding thing to do. In most cases, HR staff can be saved by machine learning as it takes a big share of these tasks from them. It can rationalize the workflow and the HR department will have time to focus on more important problems.

Attracting top talent

A great amount of many companies are using machine learning to attract worthy candidates. Well-known companies like Glassdoor and LinkedIn use ML to limit searches and look for candidates with the help of intelligent algorithms.

There are machine learning tools developed by Google to improve communication. These tools can analyze the attributes of possible candidates and then present them job positions that are matching their skills, experience, and personality.


Based on the changing nature of ‘new generations’ entering the workforce, personalization turned into a vast part of attracting and hiring top talent.

Machine learning fits this job perfectly. It understands the specific needs of employees and creates custom training, reinforcements, and individual dynamic programs.

Measuring employee engagement

Employee engagement is a phrase all businesses use, it’s popularity is not just a buzz. Studies have shown that 70% of employees are engaged in their work.

A tool in the face of machine learning can become helpful in a situation like this. ML is better at processing, measuring and understanding this data than the human team. Such valuable information can become priceless in improving productivity and decreasing staff turnover rates.

Machine learning human resources apps

So, let’s investigate how machine learning is helping HR nowadays and what machine learning HR apps are used in this area. Here are some of the HR machine learning use cases.

Tracking of the applicants

When machine learning only started to be used for human resources, its first job was to manage tracking of the applicants and assessments.

Peoplise is a solution for calculating the fit grade for a candidate. It making a decision on a certain candidate easier by using the outcome of online interviews and digital screening.

Attracting talent

The attraction of talent found its niche in machine apps too. LinkedIn uses a standard kind of elementary machine learning—recommending jobs. Similar sites like Indeed, Glassdoor, and Seek use alike algorithms for building interaction maps based on the users’ information of past searches, interactions, and other activities.

PhenomPeople is a set of ML-based tools for leading potential talents to a company’s site by using different kinds of social media and job hunt channels.

Measuring engagement

As we discussed earlier, measuring employee engagement is an important part of HR responsibilities that influence the company’s success. Workometry and Glint are the companies that developed solutions used by many top organizations.

Such software systems are based on measuring, analyzing and reporting on employee engagement. For these purposes, the information is gathered from different sources.

Attrition detection

A big part of the HR department is to figure out the people’s reasons for staying at or leaving job positions. Identifying particular risk parameters based on an employee’s survey grade point is hard and almost impossible to do precisely for a human being. Machine learning is able to work with a vast amount of data and connect the needed dots instantly.

JPMorgan is a financial institution that uses machine learning algorithms to determine a person’s behavior.

Persona skills management

Machine learning has quite a potential for boosting personal skill management and development. ML human resources tools and platforms that are able to give divided instructions without using actual human trainers, these tools are time-saving and are giving people a chance to grow in their careers.

Workday is a company that builds custom advice for training workers based on a company’s needs, market trends, and worker individuality.

These kinds of suggestions are effective in a written form. This is why this kind of ML-based feedback is better for employees.

The future of machine learning HR apps

As we know, machine learning is changing the way human works today. Now, let’s peek into the future and see what else machine learning in HR can do if we give it time to evolve.

Enterprise management

The sphere of enterprise management has already started to embrace machine learning. Predictive analytics and big data management is the base of the “Intelligent Enterprise Approach” that Was invented by KPMG. This approach is helpful for making business decisions for optimizing key KPIs and other metrics.

Google’s People Analytics department was the first one to explore the area of using machine learning in human resources for enterprises. Their team is working on creating performance-management engines at the enterprise level. They are focusing on creating fresh ways of using data for the improvement of an employee lifecycle.

For the last five years, they have come up with the insights that have caused improvements in the company, like a limited amount of interviews for an applicant (quality didn’t increase after the fourth interview), optimal organization and department size, more efficient management of maternity leave.

Behavior tracking

Behavior tracking can become possible with the help of IoT wearables. Such gadgets are more efficient for the enterprise level, adding sensors to the workplace helps collect data companies (Bluetooth headphones and ID badges with smart technologies).

This kind of data, will allow companies to answer important questions like ‘how much does the marketing team talk to my sales team?’

Key considerations of machine learning in HR

  1. Data-driven: ML is entirely objective but it depends on the quality of the data being used.

  2. Need for human interaction: even though it seems like machine learning can do it all, consider some human with the candidate. Randstad research shows that 82% of respondents get frustrated with overly automated recruitment experiences. Based on that, it is important to think through how machine learning in HR is used and where human integration is necessary.


Machine learning has become an important part of human resources. It’s not only helping people to do their jobs but also can replace them where it’s needed to give HR employees more time to focus on more important tasks. Machine learning can bring a better future to the HR world.

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