Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal MANAJEMEN
Isna Eny Putri S
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Enhancing Fairness in HR Recruitment: A Hybrid AI-DSS Model vs. Traditional Methods Evaluated with DIR and EOD Metrics for Effective Recruitment Isna Eny Putri S; Agus Wibowo
MANAJEMEN Vol. 5 No. 1 (2025): MEI : MANAJEMEN (Jurnal Ilmiah Manajemen dan Kewirausahaan)
Publisher : LPPM Politeknik Pratama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/manajemen.v5i1.960

Abstract

The implementation of Artificial Intelligence-based Decision Support Systems (AI-DSS) in recruitment has significantly enhanced efficiency; however, concerns regarding algorithmic bias persist. Existing AI-DSS models primarily emphasize explicit data, often neglecting psychological and behavioral factors essential for fair recruitment. This study integrates Person-Job Fit and Person-Organization Fit theories into AI-DSS while employing adaptive learning techniques to mitigate bias. Using a mixed- methods approach with an explanatory sequential design, this research combines quantitative analysis (statistical comparisons of AI-DSS and traditional hiring methods, bias evaluation using fairness metrics) with qualitative insights (interviews with HR professionals and candidates). The findings indicate that AI-DSS improves selection efficiency and candidate performance yet remains susceptible to biases derived from historical data. Adaptive learning enhances fairness; however, ethical concerns about transparency and accountability persist. This research strengthens the AI recruitment debate by suggesting a comprehensive model that balances the operational efficiency, ethical needs, and fair practices. Explaining AI paradigms requires additional research to establish trust and flexibility for AI recruitment systems.
Co-Authors Agus Wibowo