Big data helps human resources create a quicker way of selecting the ‘right-fit’ candidates and also do away with many unconscious human biases that may creep into decision-making. Many Indian companies employ artificial intelligence (AI) as a means to recruit newer talent. Apart from this, it can also calibrate cost per hire, employee productivity and impact of HR programmes. Therefore, it is an ideal tool for not just finding the right hire but also for workforce engagement.
As great as it sounds, it does come with its share of challenges. Algorithm-based people analytics is meant to cut down prejudices, and provide data to infer from during decision-making. However, these algorithms are designed by humans and are bound to imitate the person’s biases. As per an IBM Research, bias in AI systems can erode the trust between humans and machines that learn. The sample is one of the key parameters fed to a system. The size of the sample may not be reflective of everyone’s thought process in a company.
“You need to question the algorithm and make sure that the right people are scoping them. For instance, if all white males in the US between the ages of 25-30 are creating these algorithms, they may not work if used in India. Unpacking and really seeing what is behind them is equally important. Go high tech so that you can be high touch.”
If we keep the privacy and compliance issues aside, there’s also the fear of the ‘human’ in human resources getting sidelined due to so much data dependency. Algorithms can hardly compute empathy, and for that, human touch is essential. Therefore, the question that arises here is will big data create more stereotypes?
Unmesh Pawar, partner, head – people, performance & culture, KPMG India, believes people analytics helps organisations improve employee experience, productivity, and therefore growth, for individuals and the organisation. He does caution, “One needs to keep an eye on a few things. First is data privacy, because people analytics will continue to collect data. There is a chance that people will lose trust or faith in their employers as these data sets will be used for decision-making. Second is the fact that people analytics is all about algorithms. Knowingly or unknowingly, we will end up reinforcing certain biases or certain prejudices, and what we may end up getting is more of the same thing rather than solutions for inclusion and diversity in the organisations. While the intention is to improve employee experiences, what we may end up building are robots, to an extent.”
“There are already a lot of stereotypes against the Millenials. However, most of them are not true. They know how to work in groups and also know how to work individually. They seek work challenges. They show commitment and loyalty to their work, not to the company. If the work is satisfying and rewarding, there’s higher likelihood of them continuing with their jobs.”
He also talks about the cost of implementing such technologies. While some organisation will invest in them, others may shy away from doing so. This will create a divide. He further speaks about the role of HR being people champions and showing empathy. Humanising HR should be central to people analytics. “You need to question the algorithm and make sure that the right people are scoping the algorithms. For instance, if all white males in the US between the age of 25-30 years are creating these algorithms, they may not work if used in India. Unpacking and really seeing what is behind them is equally important. Go high tech so that you can be high touch,” Pawar advises.
When it comes to prejudices created by these data sets at times, the one most talked about are the Millenials. Those born between 1981 to 1996 fall into this category and are often termed as the job-hopping bunch. However, Lalit Kar, Sr VP – HR, Reliance Retail, reveals that most of these reports are untrue. “There are already a lot of stereotypes against the Millenials. However, most of them are not true according to me. They know how to work in groups and also know how to work individually. What is important to them are work challenges. Give them work that needs physical and mental evolvement, and they will work hard. They show commitment and loyalty to their work, not to the company. If the work is satisfying and rewarding, there’s higher likelihood of them continuing with their jobs. It all boils down to individual characteristics, which existed in previous generations. What has changed now is that they have opportunities to move. The service sector has grown. There were hard working and lazy people earlier, and they exist even today.”
Therefore, while people analytics will enhance HR’s functions and make them smoother, there will always be the need for empathy. After all, even machines will hire humans.