Impact of Artificial Intelligence on Organizational Transformation. Группа авторов
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of Amazon in 2015. It found the favorable sum for the male by their new recruitment engine, at the time of new recruitment of the software developer for the AI team. It showed biases against women. As per the professionals, the AI may found difficulties in understanding the cultural barriers as the terminologies differ between cultures to culture. So, training the employees in right direction becomes the most primary care of using AI in recruitment as how to provide the training to the men who trains machines in order to avoid biases.
3.10.4 Job Losses
The biggest fear among the employee or rather in between the HR mangers is the loss of their job or an impact of the use of software on the importance of their workability. The fears are largely confined to the concern in regard to losing their job and the changes to change of their work through new automation capabilities. AI is causing damage to employees’ experience as an introduction of elimination of human labor, like the use of Uber’s self-driving cars results in the loss to taxi drivers.
3.10.5 Emotional Turmoil
The AI like other disruptive change agents on the workplace can create an emotional turmoil, resulting in intensified level of anxiety and fear. As per the Mewald C [13], people tend to fear or do not accept the ill-defined factors or unknown impact creating a negative impact of their jobs. The feeling of being judge by the machines creates a pressure and results in emotional ups and downs in mood based on the uncertainty of the results. As most of the leaders still doubt on the AI as these machines do not have feelings (except automated to display them) and they possess “no moral code” [14].
Another problem is the risk of machine learning repeating the prejudices; another is that AI may fail to imitate human instinct since machines do not understand how human consciousness works [15].
3.10.6 Fake Identity
It is believed that uses of AI can also be done in making the fake identity to impress the organization. Like Nvidia, a computer chip maker, is reportedly invested in high involving AI, creating very realistic fake celebrity photos. Researcher has recently reported that machine can help in creating a new real-time photos based on recognized common patterns. This fake photo may hamper the results of the company while selecting and recruiting process and so on.
According to McKinsey [16], comments that most of the most of the HR divisions or vertical departments of today’s era have changed into process-driven “machines” that manage people like assets, instead of treating them as exclusive human beings that encompass personalized attention. HR departments run top-down process systems to employees’ large number of resources, manage payroll, prepare annual appraisals, send instantaneous batches of employees to training, etc. It leaves no room for personalization, flexibility, and creativity.
3.10.7 Having an Audit Trail
The challenges with the machine learning are divided between the learning and outcomes, and all outcomes are based on what the machine has learned and on what basis it has come to its conclusions. The basis and the reasons of working are based on the written down rules and algorithms, which rules the entire human on the same ground. As it hard to decide on “what is always going on under the Bonnet”, so generating an audit troll of how the system makes a decision could be a tough task. The decision and processing based on these machines can raise challenge on selection and recruitment based on AI. The decision taken by the AI can be challenged under the General Data Protection Regulation (GDPR). The leading expert on AI [17] doubts on the success of AI on the understanding of machine in comparison to humans and also doubts on the decision-making power of AI by surpassing the explanatory power of human language and reasons.
3.10.8 Question on Decisions
No one can be judged by type of machines that could not understand the emotions rather work on the intelligence, so HR decisions may always be challenged with the base, criteria, or the frontiers on which the decision of recruitment, selection, promotion, and bonus are taken.
As John Hawksworth [18], PwC’s chief economist, made it specific that “legal and regulatory hurdles, in regard to organizational apathy and legal systems might cause a delay in moving toward AI and Robotics” [18]. According to Minon [4] it might be there that HR leaders may feel the need of the more digital tools to outpace the existing one with lot of more correction [19].
Indeed, the lust of HR leaders for being on the digital front and to acquire the mastery on the new tools may outperforms their ability of getting the tools [19]. Again, one more hurdle lies in the shortage of the AI-skilled employees and again the shortage of trainers to provide training in this regard.
AI-enhanced HR is also going to challenge the white collar job like lawyers, teachers, traders, sale and marketing, and doctors.
3.11 Conclusion
As AI and machine learning are playing with the technological landscape of the HR, it is becoming very crucial for the HR manager to find a way out to bring equilibrium in between the advancement of the technologies and the human pace. Drenched with mounting data and robust but affordable computing technologies, like AI, it is branching out into more and more diverse industries and areas of life. Luckily, the AI possesses the capability of improvement in almost all sectors and operation all the functions of any business. AI, by acquiring more and more capabilities, can indeed learn quickly; then, humans in long run may diminish human capability and they may change in their conditions, too.
The successful implementation becomes more crucial then the adoption. At end of the day, AI is not the answer of the every question neither it is a “Jin of Aladdin”, with “HUKUM mere AKKA”. The HR must find out the best way to use this “magical Lamp”. It is the tool and nothing more, the tool which works based on the data, and to be effective, the data must be effectively handled. Likewise, there is always two side of the coin, and it depends upon the user to use it wistfully so lies with AI in HR. A thoughtful use of the machines in combining the people can result in a solution for the managerial conflict which going on in totally believing a machine for “devolving” people management tasks as an HR function or to took up a strategic challenge by totally believing upon it.
References
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