AN ANALYSIS OF INDONESIAN COMPUTER SCIENCE STUDENTS’ PERCEPTIONS OF ETHICAL ISSUES AND BIAS IN LOCAL LANGUAGE AI MODELS

Alfandi; Kahfi Lanang; M. Ramdan; M. Giopani Batubara

Articles

Vol. 10 No. 8 (2026): Kohesi: Jurnal Sains dan Teknologi, ISSN 3025-1311

AN ANALYSIS OF INDONESIAN COMPUTER SCIENCE STUDENTS’ PERCEPTIONS OF ETHICAL ISSUES AND BIAS IN LOCAL LANGUAGE AI MODELS

Alfandi^▸^▾
Kahfi Lanang^▸^▾
M. Ramdan^▸^▾
M. Giopani Batubara^▸^▾

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Submitted: January 26, 2026
Published: 2026-01-26

Abstract

Artificial Intelligence (AI) systems that support local languages have grown rapidly in Indonesia, yet concerns about ethical use and inherent bias remain underexplored—especially among future technology professionals. This study investigates how Indonesian Computer Science students perceive ethical issues and algorithmic bias in AI models trained on local language data. Using a mixed-methods approach, a structured survey and in-depth interviews were conducted with 250 undergraduate students from multiple universities across Indonesia. Quantitative analysis measured levels of awareness, perceived risk, and ethical concern, while qualitative insights captured students’ reflections on fairness, transparency, and social impact. Results show that although most participants recognize the potential benefits of AI for local language processing, there is a significant gap in understanding how bias can affect underserved communities and reinforce harmful stereotypes. Additionally, many students expressed a desire for ethics education to be better integrated into computer science curricula. These findings highlight the need for more comprehensive ethical frameworks tailored to regional contexts, and suggest that enhancing ethical literacy among future engineers is essential for the responsible development of AI technologies. This research contributes to ongoing discussions about AI governance and supports the design of pedagogical interventions to foster more ethically informed practitioners.

References

[1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 610–623.
[2] Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability and Transparency, 77–91.
[3] 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.
[4] Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. ACM Conference on Equity and Access in Algorithms, 1–9.
[5] UNESCO. (2022). Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO Publishing.
[6] Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.
[7] Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W. W. Norton & Company.

Keywords

Artificial Intelligence Ethics, Algorithmic Bias, Local Language AI Models, Student Perception, Computer Science Education, Indonesia

How to Cite

AN ANALYSIS OF INDONESIAN COMPUTER SCIENCE STUDENTS’ PERCEPTIONS OF ETHICAL ISSUES AND BIAS IN LOCAL LANGUAGE AI MODELS. (2026). Kohesi: Jurnal Sains Dan Teknologi, 10(8), 1571-1580. https://cibjournal.com/index.php/kohesi/article/view/7239

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[1] [1] Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 610–623.

[2] [2] Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability and Transparency, 77–91.

[3] [3] 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.

[4] [4] Suresh, H., & Guttag, J. V. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. ACM Conference on Equity and Access in Algorithms, 1–9.

[5] [5] UNESCO. (2022). Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO Publishing.

[6] [6] Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson Education.

[7] [7] Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W. W. Norton & Company.