
International Journal on Science and Technology
E-ISSN: 2229-7677
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Impact Factor: 9.88
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 16 Issue 2
2025
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Leveraging AI and Machine Learning in HR Data Analytics for Performance Management Datasets
Author(s) | Ilango Kessavane |
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Country | United States |
Abstract | AI and machine learning are transforming HR performance management by providing data-driven insights that enhance employee evaluations, engagement, and productivity. Traditional performance management systems often suffer from subjectivity, infrequent assessments, and a lack of actionable insights. This white paper explores how AI and ML optimize HR analytics, making performance evaluations more accurate and unbiased. It discusses key applications such as predictive analytics for employee performance, AI-driven learning recommendations, and automation of HR reporting. It also highlights how AI helps reduce biases in evaluations and improve fairness in promotions and appraisals. While AI offers significant advantages, its adoption in HR comes with challenges, including data privacy concerns, potential biases in AI models, and the need for ethical transparency. The paper also examines future trends, such as generative AI in HR decision-making and real-time employee monitoring. For HR leaders, the path forward involves integrating AI responsibly, ensuring transparency, and leveraging data for better decision-making. By adopting AI-driven HR analytics, organizations can develop a more engaged and high-performing workforce while staying competitive. |
Keywords | HR analytics, workforce management, predictive analytics, performance reviews, employee engagement, HR teams |
Field | Engineering |
Published In | Volume 16, Issue 1, January-March 2025 |
Published On | 2025-03-08 |
Cite This | Leveraging AI and Machine Learning in HR Data Analytics for Performance Management Datasets - Ilango Kessavane - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.3729 |
DOI | https://doi.org/10.71097/IJSAT.v16.i1.3729 |
Short DOI | https://doi.org/g9fmwb |
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