HRM IN THE DIGITAL AGE: THE RISE OF AI AND AUTOMATION IN HR
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.6063Keywords:
Artificial Intelligence, Human Resource Management, Employee Well-being, Data Privacy, Ethical HR PracticesAbstract [English]
This work contributes to the ongoing discussion on the ethics of adequately implemented AI in Human Resource Management by recognizing that the existing HR systems used in various organizations may have been influencing employee well-being, organizational culture, job satisfaction, and employee acceptance. AI systems in HR provide important insights into data privacy concerns, issues around AI ethics, and transparency. The research design was quantitative, and data were gathered via structured questionnaires with a 5-point Likert scale from 400 respondents in Pune city, spanning a variety of industries. Stratified random sampling attempted to incorporate diversity across departments and experience levels. The data were analyzed through ANOVA and Regression Analysis using SPSS software, which tests the hypothesis thus defined.
The analysis revealed that AI-based HR systems up the likelihood of a high work-life balance and employee engagement; yet, no statistical evidence showed any correlation between AI integration and positive employee well-being, organizational culture, or job satisfaction. There were, however, concerns on the level of data privacy, algorithmic bias, and ethical transparency, yet they did not profoundly impact the overall acceptance of AI systems, as testified to by the study. The work points out that there is still a gap between the technological advancement and the trust of employees, and ethical governance and transparency in the application of AI for HR are in dire need.
Organizations must place a premium on transparency and various other ethical considerations other than employee trust, therefore enabling them to maximize all attainable benefits of AI on HR. Among the recommendations proposed within this study are widescale organizations creating sound data privacy policies, monitoring AI systems for real-time algorithmic biases, involving employees in the decision-making processes around AI implementation, and so forth. The study lays the groundwork for ongoing future studies to investigate other countries and industries using a qualitative research approach.
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