Designing Recruitment Automation System to Reduce Gender, Age, Race, and Educational Background Bias in Indonesia
DOI:
https://doi.org/10.29407/kilisuci.v3i.7143Abstract
Research aim: This research aims to design a fair, technology-based recruitment system by eliminating bias and assessing candidates solely based on their competence.
Design/Method/Approach: This research utilizes the ADDIE model to develop a bias-free recruitment system.
Research Finding: The system effectively identifies and minimizes bias in candidate selection by prioritizing relevant qualifications and competencies, while excluding non-work-related factors.
Theoretical contribution/Originality: This research enhances the understanding of automation in employee recruitment in developing countries, particularly Indonesia, by introducing innovative recruitment algorithms that promote diversity and inclusivity across various companies.
Practical/Policy Implication: This research suggests that companies adopt a more equitable automation system to foster a diverse and innovative work environment.
Research limitation: This study requires further validation with large-scale, real-world data. Future research should utilize more diverse datasets and explore the system’s impact in a broader context.
Keywords:
Recruitment, fairness in recruitment, recruitment automation system, recruitment bias, Workplace DiversityDownloads
References
[1] Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-159. https://arxiv.org/abs/1712.03586
[2] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys, 54(6), 1-35. https://doi.org/10.1145/3457607
[3] Dastin, J. (2018). Amazon Scraps Secret AI Recruiting Tool That Showed Bias against Women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
[4] Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469-481. https://doi.org/10.1145/3351095.3372828
[5] Cowgill, B. (2019). Bias and Productivity in People Analytics: Evidence from a Field Experiment. Management Science, 65(7), 2961-2979. http://dx.doi.org/10.2139/ssrn.3584916
[6] Kurniawati, S., Suryadarma, D., Bima, L., & Yusrina, A. (2018). Education in Indonesia: A white elephant? Journal of Southeast Asian Economies, 35(2), 185–199. https://doi.org/10.1355/ae35-2e
[7] Ngadi, N., Asiati, D., Latifa, A., & Nawawi, N. (2021). Gender Inequality in the Indonesian Labor Market. In S. Dixit & S. Moid (Eds.), Women Empowerment and Well-Being for Inclusive Economic Growth (pp. 24-44). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-3737-4.ch002
[8] Cahyadi, R. A. H. (2019). Pengembangan Bahan Ajar Berbasis Addie Model. Halaqa: Islamic Education Journal, 3(1), 35-42. https://doi.org/10.21070/halaqa.v3i1.2124
[9] Magnus Carlsson, Henning Finseraas, Arnfinn H Midtbøen, Guðbjörg Linda Rafnsdóttir, Gender Bias in Academic Recruitment? Evidence from a Survey Experiment in the Nordic Region, European Sociological Review, Volume 37, Issue 3, June 2021, Pages 399–410, https://doi.org/10.1093/esr/jcaa050
[10] Loker.id. Mengapa Sistem Perekrutan Kerja di Indonesia Disebut Diskriminatif? [Internet]. 2024 [cited 2025 Mar 24]. Available from: https://www.loker.id/artikel/mengapa-sistem-perekrutan-kerja-di-indonesia-disebut-diskriminatif
[11] Cavanagh TM, Kraiger K, L. Henry K. Age-Related Changes on the Effects of Job Characteristics on Job Satisfaction: A Longitudinal Analysis. The International Journal of Aging and Human Development. 2019;91(1):60-84. https://doi.org/10.1016/s0742-7301(08)27002-7
[12] Green Network Asia. Menghapus Diskriminasi Usia Kerja [Internet]. 2024 [cited 2025 Mar 24]. Available from: https://greennetwork.id/ikhtisar/menghapus-diskriminasi-usia-kerja/
[13] Alessandro Fabris, Nina Baranowska, Matthew J. Dennis, David Graus, Philipp Hacker, Jorge Saldivar, Frederik Zuiderveen Borgesius, and Asia J. Biega. 2025. Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey. ACM Trans. Intell. Syst. Technol. 16, 1, Article 16 (February 2025), 54 pages. https://doi.org/10.1145/3696457
[14] Merluzzi, J. (2016). Lauren A. Rivera: Pedigree: How Elite Students Get Elite Jobs. Administrative Science Quarterly, 61(2), NP13-NP16. https://doi.org/10.1177/0001839216631490 (Original work published 2016)
[15] Liputan6.com. Lulus dari Kampus Tak Ternama Bisa Bikin Susah Dapat Kerja [Internet]. 2024 [cited 2025 Mar 24]. Available from: https://www.liputan6.com/bisnis/read/2123495/lulus-dari-kampus-tak-ternama-bisa-bikin-susah-dapat-kerja
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