THE ETHICAL DILEMMAS OF AI-DRIVEN DECISION-MAKING IN BUSINESS: A MULTI-PERSPECTIVE CASE STUDY ON CORPORATE ACCOUNTABILITY AND TRANSPARENCY

Rachmat Setyawan; Wesly Tumbur ML Tobing; Sri  Handoko

doi:10.51903/z0b0ep50

Authors

Rachmat Setyawan Universitas Sains dan Teknologi Komputer
Wesly Tumbur ML Tobing Universitas Sains dan Teknologi Komputer
Sri Handoko Universitas Sains dan Teknologi Komputer

DOI:

https://doi.org/10.51903/z0b0ep50

Keywords:

AI, etika bisnis, transparansi, bias algoritmik, akuntabilitas

Abstract

The application of artificial intelligence (AI) in business decision-making has significantly impacted operational efficiency and effectiveness. However, ethical challenges such as algorithm transparency, AI system bias, and ethical policy accountability remain major concerns. This study aims to examine how companies manage ethical dilemmas in AI implementation through a multi-perspective case study of three companies in the financial, e-commerce, and technology sectors. The findings reveal that algorithmic transparency remains limited, AI bias has the potential to create unfairness, and ethical accountability implementation varies across companies. Some companies have adopted explainable AI (XAI) and algorithmic audits as mitigation strategies, while others still rely on internal guidelines without strict oversight mechanisms. This study provides insights into the need for stricter regulations, the importance of regular algorithmic audits, and the encouragement of more transparent and accountable AI adoption in the business world.

References

Almasarwah, A., Wreikat, A. Al, Marei, Y., & Alsharari, N. (2024). AI’s influence on corporate transparency and financial performance: a new era. International Journal of Behavioural Accounting and Finance, 7(3), 233–253. https://doi.org/10.1504/IJBAF.2024.143833

Busuioc, M. (2021). Accountable Artificial Intelligence: Holding Algorithms to Account. Public Administration Review, 81(5), 825–836. https://doi.org/10.1111/PUAR.13293

Desma, S. (2025). Local Culture-Based Marketing Strategy: A Study of Companies in the Archipelago. TechTalent & Business Review, 1(1), 16–22. https://jurnal-nusantara.bangangga.com/index.php/TTBR/article/view/16

Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974. https://doi.org/10.1016/J.JBUSRES.2020.08.024

Edith Ebele Agu, Angela Omozele Abhulimen, Anwuli Nkemchor Obiki-Osafiele, Olajide Soji Osundare, Ibrahim Adedeji Adeniran, & Christianah Pelumi Efunniyi. (2024). Discussing ethical considerations and solutions for ensuring fairness in AI-driven financial services. International Journal of Frontline Research in Multidisciplinary Studies, 3(2), 001–009. https://doi.org/10.56355/ijfrms.2024.3.2.0024

Emma, L. (2024). The Ethical Implications of Artificial Intelligence: A Deep Dive into Bias, Fairness, and Transparency. https://www.researchgate.net/publication/386045670

Hanna, M. G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M., & Rashidi, H. H. (2025). Ethical and Bias Considerations in Artificial Intelligence/Machine Learning. Modern Pathology, 38(3), 100686. https://doi.org/10.1016/J.MODPAT.2024.100686

Hoerber, T., Weber, G., Cabras, I., & Ulnicane, I. (2017). Chapter 14 Artificial intelligence in the European Union. The Routledge Handbook of European Integrations, 254–269. https://doi.org/10.4324/9780429262081-19

Houser, K. A. (2019). Can AI Solve the Diversity Problem in the Tech Industry: Mitigating Noise and Bias in Employment Decision-Making. Stanford Technology Law Review, 22. https://heinonline.org/HOL/Page?handle=hein.journals/stantlr22&id=291&div=&collection=

Lee, J. (2020). Access to Finance for Artificial Intelligence Regulation in the Financial Services Industry. European Business Organization Law Review, 21(4), 731–757. https://doi.org/10.1007/S40804-020-00200-0/METRICS

Marwala, T. (2024). Self-regulation Versus Government Regulation. The Balancing Problem in the Governance of Artificial Intelligence, 207–221. https://doi.org/10.1007/978-981-97-9251-1_13

Mujtaba, D. F., & Mahapatra, N. R. (2024). Fairness in AI-Driven Recruitment: Challenges, Metrics, Methods, and Future Directions. https://arxiv.org/abs/2405.19699v2

Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The Ethical Implications of Using Artificial Intelligence in Auditing. Journal of Business Ethics, 167(2), 209–234. https://doi.org/10.1007/S10551-019-04407-1/TABLES/6

Pascale, J., Lineback, J. F., Bates, N., & Beatty, P. (2022). Protecting the Identity of Participants in Qualitative Research. Journal of Survey Statistics and Methodology, 10(3), 549–567. https://doi.org/10.1093/JSSAM/SMAB048

Rana, S. C., Francis, U., Zavi, L., Ella, S., Honein-Abou Haidar, G., & Peter, D. (2023). Cultural differences in simulation debriefing: A qualitative analysis. Heliyon, 9(4), 2405–8440. https://doi.org/10.1016/j.heliyon.2023.e14904

Rodgers, W., Al-Shaikh, S., & Khalil, M. (2024). Protocol Analysis Data Collection Technique Implemented for Artificial Intelligence Design. IEEE Transactions on Engineering Management, 71, 6842–6853. https://doi.org/10.1109/TEM.2023.3269291

Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/10.1016/J.HRMR.2022.100925

Santos, K. da S., Ribeiro, M. C., de Queiroga, D. E. U., da Silva, I. A. P., & Ferreira, S. M. S. (2020). The use of multiple triangulations as a validation strategy in a qualitative study. Ciência & Saúde Coletiva, 25(2), 655–664. https://doi.org/10.1590/1413-81232020252.12302018

Shneiderman, B. (2020). Bridging the Gap Between Ethics and Practice. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4). https://doi.org/10.1145/3419764

Tambari Faith Nuka, & Amos Abidemi Ogunola. (2024). AI and machine learning as tools for financial inclusion: challenges and opportunities in credit scoring. International Journal of Science and Research Archive, 13(2), 1052–1067. https://doi.org/10.30574/ijsra.2024.13.2.2258

Tanaka, H., & Nakamura, Y. (2024). Ethical Dilemmas in AI-Driven Decision-Making Processes. Digital Transformation and Administration Innovation, 2(2), 17–23. https://journaldtai.com/index.php/jdtai/article/view/13