Zahra Afridi¹ and Maqsood Mahmud²*

¹KnowledgeBrics, LTD, Northern Ireland, United Kingdom
²School of Computing, Ulster University, Belfast, Northern Ireland, United Kingdom

*Corresponding Author: Maqsood Mahmud, School of Computing, Ulster University, Belfast, Northern Ireland, United Kingdom.

Received: July 28, 2025; Published: August 14, 2025

Abstract

This study proposes a policy-aligned and ethical framework for integrating Artificial Intelligence (AI) into Human Resource Management (HRM), focusing on employee performance evaluation. Using data from 44 academic staff at Prince Muhammad University, a mixed-methods approach was employed combining survey analysis, sentiment mining, and predictive modeling. Results indicate higher AI familiarity and acceptance among younger respondents and those in technical disciplines, with strong support for AI’s role in improving productivity and reducing bias. However, older participants expressed notable ethical concerns, especially regarding privacy and transparency. Sentiment analysis revealed generally positive attitudes toward AI features, while a Random Forest model identified AI familiarity, monitoring capabilities, and perceived productivity benefits as key acceptance drivers. These insights highlight the need for HRM systems that are not only technologically robust but also ethically responsible and compliant with emerging global policies like GDPR and the UK AI Act. The framework offers actionable guidance for responsible AI adoption in HR practices.

Keywords: Artificial Intelligence; Human Resource Management; Ethical AI; Performance Evaluation; AI Governance; Predictive Modeling

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Citation

Citation: Zahra Afridi and Maqsood Mahmud. “AI in HRM: A Policy-Aligned and Ethical Framework for Performance Management".Acta Scientific Computer Sciences 7.6 (2025): 21-29.

Copyright

Copyright: © 2025 Zahra Afridi and Maqsood Mahmud. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.