Disrupsi Kesetaraan Gender dalam Rekrutmen Digital Berbasis AI di Indonesia
DOI:
https://doi.org/10.70182/jca.v2i5.1683Keywords:
diskriminasi algoritmik, kecerdasan buatan , diskriminasi tidak langsung , rekrutmen CPNS , regulasi Indonesia.Abstract
Perkembangan kecerdasan buatan (Artificial Intelligence/AI) dalam sistem rekrutmen telah mengubah proses seleksi tenaga kerja menjadi semakin berbasis data dan algoritma. Meskipun teknologi ini sering dipromosikan sebagai mekanisme yang lebih objektif dan efisien, berbagai penelitian menunjukkan bahwa algoritma rekrutmen berpotensi mereproduksi bias historis yang tertanam dalam data pelatihan, sehingga dapat menimbulkan diskriminasi tidak langsung (indirect discrimination) terhadap kelompok tertentu terutama perempuan. Penelitian ini bertujuan untuk mengidentifikasi potensi reproduksi bias gender dalam sistem rekrutmen berbasis AI, menganalisis bentuk diskriminasi tidak langsung dalam proses seleksi otomatis, serta mengevaluasi kesiapan kerangka regulasi Indonesia dalam merespons risiko tersebut. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kasus pada sistem rekrutmen aparatur sipil negara. Hasil penelitian menunjukkan bahwa penggunaan algoritma dalam proses seleksi memiliki potensi mereproduksi ketimpangan gender akibat bias data historis dan kurangnya transparansi dalam sistem pengambilan keputusan algoritmik. Sementara itu, kerangka regulasi nasional terutama Undang-Undang Ketenagakerjaan dan Undang-Undang Nomor 27 Tahun 2022 tentang Perlindungan Data Pribadi belum secara spesifik mengatur transparansi algoritma, mekanisme audit AI, maupun akuntabilitas terhadap keputusan otomatis dalam proses rekrutmen. Kondisi ini menunjukkan adanya kesenjangan regulasi dalam tata kelola penggunaan AI pada sistem rekrutmen di Indonesia.
Downloads
References
Ajunwa, I. (2020). The quantified worker: Law and technology in the modern workplace.Cambridge University Press.
Ajunwa, I. (2020). The paradox of automation as anti bias intervention. Cardozo Law Review.
Abrari, A., & Ariyadi, D. (2024). Analisis yuridis terhadap perlindungan hak asasi manusia dalam penyelenggaraan dan pemanfaatan teknologi artificial intelligence. Jurnal E-Dinamic Law, 3(1).
Ashton, A. L., Dewi, A. A. S. L., & Dinar, I. G. A. A. G. P. (2024). Penegakan hukum terhadap peran artificial intelligence di Indonesia. Jurnal Analogi Hukum, 6(3), 267–272.
Ahmad, S. (2024). The Role of Artificial Intelligence in Reducing Implicit Bias in Recruitment. International Journal of Advanced Research in Computer Science & Technology.
Ardhani, Z. (2024). Utilization of artificial intelligence (AI) in the employee recruitment process: A qualitative study on multinational companies in Indonesia. Jurnal Impresi Indonesia, 4(5).
Badan Kepegawaian Negara. (2023). Statistik Aparatur Sipil Negara.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732.
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. Cambridge, MA: MIT Press.
Badan Pengkajian dan Penerapan Teknologi. (2020). Strategi nasional kecerdasan artifisial Indonesia 2020–2045. Jakarta: BPPT.
Badan Pusat Statistik. (2023). Keadaan angkatan kerja di Indonesia. Jakarta: Badan Pusa Statistik.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT).
Bogen, M., & Rieke, A. (2018). Help wanted: An examination of hiring algorithms. Upturn.
Burrell, J. (2016). How the machine thinks: Understanding opacity in machine learning algorithms. Big Data & Society.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15.
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the “good society”: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528.
Creswell, J. W. (2013). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (3rd ed.). Thousand Oaks, CA: Sage Publications.
Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com
Djafar, W., & Al-Faruqi, M. R. (2021). Artificial intelligence governance and regulation in Indonesia: Challenges and opportunities. Jakarta: Institute for Policy Research and Advocacy (ELSAM).
Edwards, L., & Veale, M. (2017). Slave to the algorithm? Why a “right to an explanation” is probably not the remedy you are looking for. Duke Law & Technology Review, 16, 18–84.
European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act).
Febri, Y., Nabilla, G. A., Ilahia, S. N. K., Septiani, S. S., Asisti, P. A., & Iskandar. (2025). Perlindungan hukum terhadap data pribadi pelamar ASN dalam proses rekrutmen CPNS/PPPK yang menggunakan artificial intelligence (AI). Jurnal Hukum dan Kewarganegaraan, 13(2).
Fredman, S. (2011). Discrimination law (2nd ed.). Oxford University Press.
Fredman, S. (2016). Substantive equality revisited. International Journal of Constitutional Law, 14(3), 712–738.
Hukom, R., & Martinus. (2025). The effectiveness of artificial intelligence in judicial decision making in Indonesia. HAKIM: Jurnal Ilmu Hukum dan Sosial, 3(1).
Idris, M. F., & Kossay, M. (2024). Legal accountability of algorithmic bias: Examining the role of law in preventing discriminatory AI decisions. International Journal of Law and Society, 2(2). https://doi.org/10.62951/ijls.v2i2.521
Iqbal, M., Trisiana, R., Wijaya, M. S. T., & Hariyanto, R. (2024). Kesenjangan tata kelola AI di Indonesia: Antara regulasi lemah dan implementasi teknologi yang melesat. Yurijaya: Jurnal Ilmiah Hukum, 7(3). https://doi.org/10.51213/yurijaya.v7i3.229
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399.
Kementerian Riset dan Teknologi. (2020). Strategi Nasional Kecerdasan Artifisial Indonesia 2020-2045.
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2018). Human decisions and machin predictions. Quarterly Journal of Economics, 133(1), 237–293.
Kusuma, E. A. (2024). Integrating human judgment to address algorithmic bias in HR practices in the Indonesian context. Jurnal Ilmiah Manajemen Kesatuan, 13(6). https://doi.org/10.37641/jimkes.v13i6.4147
Köchling, A., & Wehner, M. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness in automated decision making. Business Research.
Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966–2981.
Mehrabi, N., et al. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press.
OECD. (2019). OECD principles on artificial intelligence. OECD Publishing.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing.
Pasquale, F. (2015). The black box society: The secret algorithms that control money an information. Harvard University Press.
Pramudya, M. E., & Abidin, A. Z. (2024). Determinants of Female Labor Force Participation Rate: Evidence from Indonesia. Balance: Jurnal Akuntansi dan Manajemen.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring Evaluating claims and practices. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: valuating claims and practices. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT).
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT).
Ruslan, K., & Sukma, W. L. (2024). Decomposition of Post-Pandemic Gender Wage Gaps in Indonesia. Jurnal Ketenagakerjaan.
Rohimi, U. E. (2025). Artificial intelligence and cybersecurity regulation in Indonesia: Towards an adaptive legal framework. Indonesian Cyber Law Review, 2(1).
Raihannabil, S. D., et al. (2023). Analysis of Socioeconomic and Demographic Characteristics of Female Participation in the Labor Market in Indonesia. Jurnal Ketenagakerjaan.
Rodiaminollah, M., Fahmi, M. A., & Qomariyah, L. N. (2025). Manfaat dan tantangan artificial intelligence (AI) dalam rekrutmen dan seleksi karyawan. Jurnal Ekonomi Syariah dan Hukum Islam.
Selbst, A. D., & Barocas, S. (2018). The intuitive appeal of explainable machines. Fordham Law Review, 87(3), 1085–1139.
Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), 367–377 https://doi.org/10.1145/3442188.3445922
Undang-Undang Republik Indonesia Nomor 39 Tahun 1999 tentang Hak Asasi Manusia.
UNCTAD. (2021). Digital economy report 2021. United Nations.
Utama, Z. M., Rizki, L. N., Suprakto, R. P., & Puspitarini, L. P. (2025). AI dalam rekrutmen: Analisis terhadap kecepatan, efisiensi waktu, objektivitas penilaian dan risiko kualitas. Scientific Journal of Reflection: Economic, Accounting, Management and Business, 8(4).
Veale, M., & Borgesius, F. (2021). Demystifying the EU Artificial Intelligence Act. Computer Law Review International.
Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Angelica Albina Pagnozzi, Riviera Elga Apriandita, Ika Arinia Indriyany (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.










