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MACHINE LEARNING applied to human resources

Machine learning is the process that drives many of the services we use today and can elaborate very precise assumptions about what we do, about the next one activities that we might want to do or, in the case of a voice assistant, about which ones words better match those funny sounds that come out of our mouth.

In reality, this process is quite simple: finding the model, applying the model. It is present in practice in many aspects of our lives. This is largely due to an invention of 1986, courtesy of Geoffrey Hinton, now known as the father of Deep Learning. Applied to human resources the use of Machine Learning is currently limited (even if the growth potential is wide).

Today, in most cases, Machine Learning is used to generate efficiency in recruitment processes, thanks to its ability to go beyond the verifiable skills, such as level of studies, etc.
Apply Machine Learning to recruitment processes, which are expensive and inefficient, can allow better research and discovery of the best candidates among thousands of people.

Let’s analyze other cases of using Machine Learning in the management of human resources

Get more qualified candidates and ensure equal opportunities by applying Machine Learning in the creation of job descriptions.
Create “neutral” job descriptions, which means job descriptions relevant but gender-neutral, ensures that the best candidates, both men, and women, apply for an available position.
It may seem irrelevant, but a recent study by Total Jobs concluded that the use of gender-neutral terminology attracts 42% more responses compared to job advertisements that have a non-neutral one. As a result, human resources have access to a larger and more qualified group of candidates. Develop a more qualified workforce with better recommendations for the training of your employees.

A second example of how Machine Learning can improve the management of human capital is its application to employee training programs. In many companies, employees have access to various training options, but often fail to find what is most suitable for them. Machine Learning algorithms can present internal and external courses that are better suited to the objectives of employee development based on many variables, including the skills that the employee intends to develop and courses carried out by other employees with similar professional goals.
We can transform the role of human resources and this transformation will become increasingly “disruptive” over time.

These two cases are clear examples in which Machine Learning raises the role of human resources from a tactical to a strategic role. Other contributions to this transformation are the application of intelligent software, which allows you to automate repetitive actions, allowing you to get better information on employees in the company and their potential turnover. By applying it, companies can intervene and react at the right time with corrective policies to curb any shortcomings, at the same time attracting more talents suited to the specific context.
As we move towards a future where human capital and programmatic machines (ie robot) must work together to create value for the company, the role and quality of the people who make up the workforce will become an asset increasingly strategic for the company. The CHROs must face the digital transformation to be well prepared for that reality.

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