| Abstract | The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has
significantly transformed performance appraisal practices. Traditional appraisal systems often suffer
from subjectivity, bias, and time constraints, limiting their effectiveness in accurately measuring
employee contributions. AI-driven appraisal systems, leveraging machine learning algorithms, natural
language processing, and predictive analytics, provide data-driven insights that enhance fairness,
transparency, and accuracy in evaluating employee performance. By analyzing real-time data from
multiple sources such as productivity metrics, project outcomes, and employee engagement patterns,
AI enables organizations to establish objective performance benchmarks and deliver personalized
feedback. Furthermore, AI tools can identify hidden talent, predict future performance trends, and
support informed decision-making in promotions, training, and succession planning. However, ethical
considerations, data privacy concerns, and the risk of over-reliance on algorithms remain critical
challenges. Overall, the adoption of AI in performance appraisal fosters a more continuous, unbiased,
and development-oriented evaluation system, aligning employee growth with organizational goals.
KEYWORDS: Artificial Intelligence, Human Resource Management, Performance Appraisal,
Machine Learning, Predictive Analytics, Employee Evaluation
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