Particular Difficulties and Possibilities of AI Applications in Human Resource Management

Applications of Artificial Intelligence in Human Resource Functions: Special Challenges and Opportunities

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The value that AI may provide to enterprises in general and to human resource management in particular is a topic on which experts agree conceptually. If implemented properly, AI technologies may not only increase corporate efficiency but also employee satisfaction and involvement in organizational efforts.

The actual situation is frequently contradictory despite this widespread assumption. According to surveys conducted by companies like MIT and Boston Consulting Group, 7 out of 10 AI initiatives did not have the impact that was anticipated, and plans for AI adoption fell from 20% in 2019 to 4% in 2020. When we concentrate on AI applications connected to Human Resource functions within enterprises, these results become much more depressing.

Before I discuss some of the specifics of using AI to address issues in HRM, I want to return to the fundamental idea of what HR is primarily intended to do. Employees must make two essential decisions when interacting with organizations, according to a pivotal 1958 article by scholars March and Simon: one is the “decision to produce,” and the other is the “decision to participate.”

While choosing to participate means that employees can decide whether or not to stay with the organization, choosing to produce includes deciding whether or not they are willing to generate (create) as much as the company needs. Both of these are quite distinct choices, and one of the key goals and tasks of human resource functions in any business is to assist people attain their greatest level of contribution to organizational results. In light of this, we can all agree that the focus of all HRM operations should be on the employees.

The good news is that these models may be utilized to maximize employee performance by encouraging and rewarding individuals by establishing individualized performance evaluation plans and incentives, given that AI systems are built on algorithms exploiting underlying employee data. Additionally, when applied responsibly, these models can aid in lessening the prejudice and injustice that are sometimes ingrained in managerial assessments based on the personal judgments of the person in charge.

On the other hand, cases of discriminating models that did little more than magnify the prejudice present in the underlying data utilized to create these models are also reported. Employees’ unfavorable responses to monitoring may also breed mistrust and make them reluctant to abide by rules based on algorithmic results. Given that AI systems for HR have significant implications for both people and society as a whole, the ethical hazards are simply too great. I will go into more detail on the specific difficulties and possibilities of implementing AI applications for HR in my future lecture at ODSC. Some of the highlights are below.

The applications of AI in HR and the difficulties in creating precise scores using machine learning models on HR data.

Methods for data preprocessing that can help HR models be more accurate employee responses to algorithmic management and being led by AI rather than a human boss.

Ways to stop employees from abusing AI models after it is used to inform decisions in order to try to cheat the system.

The precautions that must be followed when traveling down this path in order to prevent putting one’s reputation and finances at danger due to unanticipated repercussions of AI gone wrong!

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