The Role of Machine Learning in HR: Building a Data-Driven Function
Like all aspects of modern business, technology is changing how we operate and function, and if you're not leveraging it to your advantage, you will be left behind with the dust.
As our 2022 Insight222 People Analytics Trends report "Impacting Business Value: Leading Companies in People Analytics" highlights, people analytics as a function has a stronger influence on strategic HR decisions. And as the world of HR moves from traditional methods to more modern ones, HR departments everywhere are beginning to leverage artificial intelligence and ML-based solutions such as predictive analytics to spot patterns and uncover trends about their workforce to make more data-driven decisions ultimately.
Understanding Machine Learning in HR
Machine learning is a form of artificial intelligence (AI) that uses algorithms to identify patterns in large data sets. Just as if we were to break down its name, "machine" and "learning", it implies how the machine's algorithms can "learn" from past data and experiences, allowing them to make predictions and recommendations based on what they have seen before.
In the HR context, by leveraging predictive analytics and machine learning applications, HR departments can gain valuable insights into their workforce analytics to develop more effective people strategies. Here are some key benefits that come with using machine learning in HR.
Reducing Bias in Decision Making
Machine learning algorithms are designed to be unbiased and objective, which makes them ideal for helping HR professionals make decisions without the influence of personal, or unconscious biases or preferences. It is able to identify patterns and anomalies in the data that might not be visible to the human eye, allowing for greater accuracy and fairness in the decision-making process.
Predicting Employee Behaviour
ML algorithms and predictive analytics can also be used to predict employee behaviour, allowing HR departments to anticipate potential issues before they arise. For instance, by using data such as age, experience and time in the current job role, HR teams can predict employee attrition and adjust their strategies accordingly. They can also implement predictive analysis and review historical data to create actionable insights and create a better work environment for their teams.
Making Data-Driven Decisions
Finally, one of the key (and most prominent) benefits of leveraging people analytics and ML in HR is that it allows organisations to make more informed decisions. By automatically processing and analysing large datasets, HR departments can gain insights that would otherwise be difficult or time-consuming to obtain.
Building a Data-Driven HR Function with Machine Learning
HR departments are inundated with data from different systems and platforms, which can make it challenging to utilise the information in meaningful ways. However, ML can bridge that gap and use it to its advantage to build a data-driven HR function with a comprehensive view of its people operations.
For instance, machine learning can be used to:
Predict Employee Turnover
By analysing surveys, people data and HR records to analyse patterns and trends in past data, HR teams can predict which employees are likely to leave.
Identify High-Performing Candidates
By looking at previous successors and analysing their data, HR professionals can use machine learning to identify the best candidates for new roles.
Provide Insights on Employee Engagement:
By looking at employee engagement surveys and other sources of information, such as absence records, HR can use ML to understand employee engagement better and develop strategies to improve it.
Overcoming Challenges and Ethical Concerns
In 2017, Amazon had to terminate its AI recruitment system, as it kept discriminating against women during the hiring process. The reason for this bias was that the system was "trained" on data from past successful male applicants (the industry was predominantly male-dominated). So it was no wonder that when it came to making decisions on new applicants, it kept favouring male candidates over females.
This example, on its own, highlights the need for caution when using machine learning applications. This is why ensuring that algorithms are "trained" on unbiased data sets is important. Otherwise, its purpose is defeated, and a risk of unconscious bias could be introduced into the decision-making process.
Another challenge is the fact that machine learning algorithms can be difficult to explain and interpret, ultimately leading to what is acutely known as the "black box" effect. Therefore, to ensure transparency and trust in the system, HR professionals need to have the data literacy understanding and skills to understand and interpret why a particular decision was made or how an algorithm arrived at its conclusions.
Then, you have the issue of data privacy. As HR departments collect more data on employees, there is an increasing need to ensure that it is kept secure and protected against misuse or unauthorised access. So, it's important to implement appropriate measures and share data governance policies and procedures to protect employee data.
Upskilling HR Professionals for Data Analysis and Machine Learning
As Workday highlights in their article on AI and Machine Learning in HR, "Learning to work effectively with machines to augment human intelligence will be a critical part of making automation successful."
Therefore, to truly reap the benefits of machine learning, HR professionals must become knowledgeable about its potential applications and develop the necessary skills for effective implementation.
This could involve upskilling HR in people analytics and AI and introducing them to ML concepts like supervised learning, unsupervised learning, decision trees or neural networks.
If you're looking to expand your knowledge on machine learning, be sure to visit our myHRfuture Academy. We offer learning pathways and skill booster certifications that provide a comprehensive overview of using data analysis and machine learning to drive HR decisions. With a blend of training courses and expertly curated content, your team can build the technical skills to make the most of machine learning and gain real-life context to put their knowledge into practice.
Upskilling for Data Literacy and Machine Learning is Key to HR's Future Success
The future of HR is data-driven, and machine learning applications are set to revolutionise decision-making and drive business outcomes. Though to make the most of this technology, upskilling HR for data analysis and machine learning is an absolute must. By doing so, they can ensure ethical usage of machine learning algorithms, make processes more transparent and protect employee data privacy.
Ultimately, leveraging predictive analytics, machine learning, and people analytics will help HR to make more accurate decisions, improve employee engagement and foster a data-driven culture.
The bottom line is: if you're looking to gain the skills to take your HR department into the future, start by upskilling your HR teams for data literacy and machine learning.
Learn the impact of AI, how it has evolved and how it is effecting HR, with the myHRfuture Academy!
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