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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 241 Documents
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Addressing Algorithmic Bias and Data Privacy in Human Resource Management Herdiana, Hendi; Munir, Munir; Hurriyati, Ratih; Sultan, Mokh. Adib; Tua, Frans David; Ergashevna, Buriyeva Kibrio
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3177

Abstract

Artificial intelligence (AI) has transformed Human Resource Management (HRM) by automating recruitment, enhancing performance evaluation, and enabling data-driven workforce planning. However, its adoption raises critical concerns related to algorithmic bias, data privacy, and employee trust, creating a significant gap in understanding how these technical and ethical dimensions interact. This study aims to synthesize current evidence on the impact of AI on HRM functions, the challenges associated with fairness and privacy, and employee perceptions of AI-enabled HRM systems. A Systematic Literature Review (SLR) was conducted following PRISMA 2020 guidelines and structured using the PICOC framework. Searches across major scientific databases identified 1,042 records, of which 35 peer-reviewed studies published between 2020 and 2025 met all eligibility criteria. The findings show that AI enhances HRM efficiency and decision quality but presents recurring risks of algorithmic bias, opaque decision-making, and weak data governance. Employee perceptions of fairness, transparency, and privacy strongly influence trust and acceptance of AI-based HRM systems. The review concludes that effective AI adoption requires socio-technical integration combining algorithmic capability with robust governance and ethical safeguards. The study introduces an integrated conceptual model linking AI capabilities, HRM functions, data governance, employee trust, and organizational outcomes—representing a key theoretical contribution and a novel synthesis of previously fragmented research.

Page 25 of 25 | Total Record : 241

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