There is a definite HR angle to Machine Learning, considering the automobile industry in the US. Since the time of Ford, and until recently, assembly line workers were the mainstay of this industry. Management experts expended considerable energy through work studies, to devise efficient methods by which assembly line workers could be at their productive best. According to “Robotics Online” (January 2017), by the 1980s, billions of dollars were being spent by car manufacturers worldwide on the assembly line. As a result, assembly line workers were continuously forced out of the workforce, as they were being replaced by smart machines. The job requirement in the machine learning era was for technologists who could program, run and maintain the robots that were performing assembly line jobs.
Jobs in the Machine Learning Age
The technologists who manage robots need to be trained computer programmers. Their levels of education are higher than those of assembly line workers. Consequently, their remuneration is higher. When assembly line workers choose to return to university and equip themselves with degrees in computer programming, they are up-skilling and by up-skilling, the former assembly line workers can oversee the smart machines that have replaced them. Unfortunately, not all displaced assembly line workers have the resolve and drive to up-skill when past their prime. Such workers can become disgruntled and embittered. These were the workers who voted Donald Trump into office.
Displaced low-wage workers are those who are negatively affected, as they do not have the background or opportunity to up skill. This then creates a new type of have-nots: the financially challenged, low-wage workforce displaced by automation. This exacerbates an existing inequality which should be addressed by HR initiatives at the government level. According to the Indian 11th five-year plan, only 10 percent of the Indian workforce has a university education. The remaining 90 percent have no recourse if their jobs are automated and they are rendered jobless.
The new have-nots will then get chained to a life at the bottom of the pyramid. They have spent all their work-lives in routine, repetitive jobs. Such jobs are the ones which are easily replaced by smart machines. Martin Ford has pointed out in his gloomy book ‘Rise of the Robots’ (2015) that the only way traditional workers can survive in this age of Machine Learning is by switching from routine, unskilled jobs to non-routine, skilled jobs. Meanwhile, another category of jobs is now on the threshold of becoming redundant on account of machine learning; that of white-collar workers.
The crying need of the hour is for the government to pass legislation that can ensure a company’s up skilling process for those employees who are to be replaced by smart machines. The law passed in 2014, requiring companies with net revenues of greater than ten crores to invest two percent of their revenue on Corporate Social Responsibility activities, is beginning to do wonders. We now need similar legislation for companies displacing jobs through automation. An article published in ‘The Hindu’ (July 4, 2016) notes that the Indian textile industry is likely to generate only 29 lakh jobs for humans in the next 5 years, as opposed to the government target of 1 crore new jobs in this industry. This is because automation is sweeping away jobs in the textile industry just as it is in the mining industry. This is bound to create widespread unrest amongst the employees of these two sectors, as well as volatility in Indian society at large.
Machine Learning and HR
Artificial Intelligence/Machine Learning is taking off in a big way in almost all functional areas of management be it finance, marketing, supply chain, logistics or HR. At present, though the adoption is somewhat slow in HR as compared to other functional areas of management as it is making inroads at a faster rate.
Artificial Intelligence/Machine Learning are software programs that mimic the way humans learn and solve complex problems. These systems are different from other application packages that have been used in some of the HR applications in the sense that they learn and adapt to the new environment as and when new data becomes available. In usual software packages, the outcomes are explicitly programmed and they do not have the ability to learn and adapt.
With the adoption of technology like mobile, cloud and social media a large amount of textual data is generated especially in the HR area. Previous computer technology was not equipped to handle such large volumes of textual data. With big data technology maturing, it is possible to analyze such huge volume of data and come out with behavioural patterns that were earlier not possible.
Some areas in HR where machine learning has been successfully applied are as follow:
Increased Success Rate in Hiring: Without machine learning when selecting the prospective applicants from a pool of CV’s is basically done by keyword matching. With machine learning and text analytics, one can analyze the data from other sources like blogs, posts in social media, tweets and retweets, posting on professional social media sites. Such selections will be a better matching than the simple keyword search.
Attrition Detection: This is a very important HR function which most HR managers are worried about. Organizations spend quite an amount of resources on training and development. If attrition is not controlled it will be a big issue. Employee surveys are carried out to detect this trend. The survey has so many dimensions it is humanly difficult, though not impossible, to detect hidden patterns which are predictors to attrition. Machine learning can identify certain signals and combinations of signals which are difficult to spot by humans. This can aid HR in taking suitable corrective action.
Post-hire Outcome: This is another important HR function which is difficult to predict. By better profile matching at the recruitment stage using machine learning and tracking the candidate behaviour in different channels one can predict the outcome. Also using web analytics, one can track the viewer of the ad and can also know which among websites is more successful than others. By keeping track of the interview process, one will be able to find out which interviewers are able to spot talent better than others. Such data can be fed to machine learning and train the machine to predict the post-hire outcome.
Machine learning technology is maturing and we are sure it will have a significant impact on HR functioning in years ahead.
Prof. S. Chandrasekhar – Senior Professor and Chairman Centre of Excellence – Business Analytics; Also Taught At: Fore School of Management IIM Lucknow, Manchester Business School UK, Indore, National Insurance Academy
Prof. Nina Jacob – Professor of Organizational Behaviour and HRM at the Institute of Finance and International Management; Director at Cardiff Programme, Universal Business School and Co-Chair, International Conference on Organisation Development, IMT, Ghaziabad. She has authored books on ‘Readings in Organizational Development’, ‘Intellectual Management’ and written papers on ‘Leadership and Change Management in a Crisis Situation’, ‘Human and Professional Values of Managers and Their Impact on the Profession’ and many editorials and book reviews.