Optimizing AI-powered Performance Appraisals: Balancing Objectivity with Human Judgment
| dc.contributor.author | Hasvin Kaur a/p Jasbir Singh | |
| dc.date.accessioned | 2026-08-20T02:37:33Z | |
| dc.date.issued | 2025 | |
| dc.description | This study examines the integration of Artificial Intelligence (AI) within performance assessment systems, focusing on merging algorithmic neutrality and human judgement to uphold fairness, transparency, and employee confidence. Identifying the limitations of conventional performance assessments, including bias, inconsistency, and subjectivity. This study examines AI's ability to enhance objectivity while acknowledging ethical considerations and the importance of human oversight. The research addresses the lack of understanding regarding the interpretation of AI-driven evaluations in non-Western cultural settings, particularly in Malaysia, where cultural principles influence perceptions of fairness. The aim of the research is to examine how AI can improve appraisals while maintaining fairness, ethics, and human oversight. A qualitative research design was employed using semi-structured interviews with three experts from the corporate, creative, and operations fields who actively incorporate AI in performance evaluations. Interviews were conducted over a focused data collection period using an approved interview protocol. A thematic analysis was conducted to identify significant patterns related to AI implementation, ethical issues, and the equilibrium between human and machine judgment. Results indicate that although AI improves efficiency and consistency, it falls short in assessing soft skills and contextual performance nuances. Human oversight is essential to mitigate algorithmic bias and ensure fairness. The study conceptually enhances Procedural Justice Theory by incorporating it into the domain of AI in HRM. Essentially, it provides a structure for HR professionals to implement AI evaluation tools thoughtfully, merging accuracy with compassion. The report recommends routine algorithm evaluations, teamwork among staff, and combined decision-making approaches. Future research should expand participant diversity and explore cross-cultural comparisons to enhance generalization. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/1670 | |
| dc.language.iso | English | |
| dc.publisher | Universiti Malaysia Sarawak (UNIMAS) | |
| dc.relation.ispartofseries | Faculty of Economics and Business | |
| dc.subject | AI integration, performance appraisals, human judgment, fairness, transparency, algorithmic bias, ethical considerations, employee confidence | |
| dc.title | Optimizing AI-powered Performance Appraisals: Balancing Objectivity with Human Judgment | |
| dc.type | Final Year Project |