• Ranjith Kumar Sha
• Kasak Gupta
• M S S Varalakshmi

Human Resource Management (HRM) has undergone significant transformation with the adoption of Artificial Intelligence (AI), particularly in performance management and feedback systems. Traditional performance evaluations often suffered from subjectivity, inconsistency, and limited frequency. AI introduces automation, real-time insights, and data-driven assessments, offering organizations greater efficiency, consistency, and scalability. This study explores two central dimensions: the effectiveness of AI-driven performance evaluation and the impact of algorithmic bias on employees. AI systems can analyze large datasets, detect performance patterns, and generate personalized, timely feedback. These capabilities help reduce managerial workload, streamline evaluation processes, and support employee development by identifying skill gaps and training needs. By applying uniform criteria, AI has the potential to make assessments more objective and actionable than traditional methods. Despite these advantages, challenges remain. A key concern is the bias embedded in AI algorithms, often arising from the historical data used for training. Such biases can perpetuate inequities based on gender, race, or tenure, negatively influencing promotions, compensation, and employee morale. Beyond technical limitations, ethical issues such as fairness, transparency, and accountability must also be addressed. Employees’ perceptions of trust and fairness in AI-driven systems play a decisive role in their acceptance and effectiveness. This study critically examines the psychological and professional consequences of flawed AI assessments, drawing from empirical data, case studies, and comparative models. The findings emphasize that while AI can enhance objectivity and efficiency, it must be implemented responsibly. Organizations are advised to adopt diverse datasets, transparent algorithms, and meaningful human oversight to ensure equitable outcomes. The research concludes that AI should complement, not replace the human judgment in performance management. Responsible adoption can align technological innovation with human values, fostering fairness, trust, and organizational growth.

Artificial Intelligence, Performance Management, Feedback Systems, Algorithmic Bias, Responsible HRM.

"ARTIFICIAL INTELLIGENCE IN HRM: OPPORTUNITIES AND ETHICAL CHALLENGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.39-47, November-2025, Available :http://www.jetir.org/papers/JETIRHD06006.pdf

"ARTIFICIAL INTELLIGENCE IN HRM: OPPORTUNITIES AND ETHICAL CHALLENGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. pp39-47, November-2025, Available at : http://www.jetir.org/papers/JETIRHD06006.pdf