FILSAFAT STATISTIKA BISNIS DI ERA BIG DATA DAN AI: RELASI TRIADIK ONTOLOGI, ETIKA, DAN EPISTEMOLOGIS
Keywords:
ontologi data, etika algoritma, epistemologi bisnisAbstract
Statistika bisnis di era big data dan AI menghadapi tantangan filosofis multidimensi yang melibatkan ontologi, etika, dan epistemologi. Penelitian ini bertujuan menganalisis relasi triadik ketiga perspektif melalui pendekatan kritis-filosofis dengan metode hermeneutika dan studi kasus (seperti algoritma rekruitmen, ESG, dynamic pricing). Hasilnya menunjukkan bahwa data bisnis bersifat konstruktif, dipengaruhi bias historis dan kepentingan korporasi, sedangkan praktik etis terhambat oleh ketidaktransparanan algoritma. Epistemologi yang reduksionis mengabaikan kompleksitas kausalitas dan konteks kualitatif. Simpulan riset menekankan pentingnya integrasi refleksi filosofis pada statistika bisnis untuk mencapai keadilan dan keberlanjutan. Rekomendasinya mencakup regulasi transparansi dan adopsi kerangka audit interdisipliner.
References
Baudot, L., Huang, Z., & Holderness Jr, D. K. (2020). Accounting manipulation, governance, and social trust. Accounting, Auditing & Accountability Journal, 33(4), 809–837. https://doi.org/https://doi.org/10.1108/AAAJ-07-2018-3589
Boyd, R., Carrigan, M., & Ferguson, P. (2023). The ethics of ESG metrics: Between transparency and greenwashing. Journal of Business Ethics, 187(3), 401–417. https://doi.org/https://doi.org/10.1007/s10551-023-05432-x
Cadwalladr, C., & Graham-Harrison, E. (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. The Guardian.
Daston, L., & Galison, P. (2007). Objectivity. Zone Books.
Floridi, L. (2019). Translating principles into practices of digital ethics: Five risks of being unethical. Philosophy & Technology, 32(2), 185–193. https://doi.org/https://doi.org/10.1007/s13347-019-00354-x
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/https://doi.org/10.1007/s11023-018-9482-5
Frodeman, R. (2014). Sustainable knowledge: A theory of interdisciplinarity. Palgrave Macmillan. https://doi.org/https://doi.org/10.1057/9781137303028
Gadamer, H.-G. (2004). Truth and method. In Continuum. Continuum.
Gal, M., & Rubinfeld, D. (2020). Data standardization. NYU Journal of Law & Business, 16(1), 40–78.
Gigerenzer, G. (2002). Calculated risks: How to know when numbers deceive you. Simon & Schuster.
Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., & Sriram, S. (2006). Modeling customer lifetime value. Journal of Service Research, 9(2), 139–155. https://doi.org/https://doi.org/10.1177/1094670506293810
Hacking, I. (1999). The social construction of what? Harvard University Press.
Hájek, A. (2003). What conditional probability could not be. Synthese, 137(3), 273–323. https://doi.org/https://doi.org/10.1023/B:SYNT.0000004904.91112.16
Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1), 1–12. https://doi.org/https://doi.org/10.1021/ci0342472
Horkheimer, M. (1972). Critical theory: Selected essays. Continuum.
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications. https://doi.org/https://doi.org/10.4135/9781473909472
Laufer, W. S. (2003). Social accountability and corporate greenwashing. Journal of Business Ethics, 43(3), 253–261. https://doi.org/https://doi.org/10.1023/A:1022962719299
Leonelli, S. (2016). Data-centric biology: A philosophical study. University of Chicago Press. https://doi.org/https://doi.org/10.7208/chicago/9780226416502.001.0001
Lessig, L. (2011). Republic, lost: How money corrupts Congress, and a plan to stop it. Twelve.
MacKenzie, D. (2019). How algorithms interact: Goffman’s ‘interaction order’ in automated trading. Economy and Society, 48(2), 188–209. https://doi.org/https://doi.org/10.1080/03085147.2019.1574865
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/https://doi.org/10.1177/2053951716679679
Munafò, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., du Sert, N. P., & Ioannidis, J. P. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 1–9. https://doi.org/https://doi.org/10.1038/s41562-016-0021
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science. Science, 349(6251), aac4716. https://doi.org/https://doi.org/10.1126/science.aac4716
Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the separation of technology, work and organization. Academy of Management Annals, 2(1), 433–474. https://doi.org/https://doi.org/10.1080/19416520802211644
Pearl, J., & Mackenzie, D. (2019). The book of why: The new science of cause and effect. In The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
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/https://doi.org/10.1145/3351095.3372828
Unilever. (2023). Unilever Climate Transition Action Plan. Unilever. https://www.unilever.com/planet-and-society/climate-action/
van Dijck, J. (2014). Datafication, dataism, and dataveillance. Surveillance & Society, 12(2), 197–208. https://doi.org/https://doi.org/10.24908/ss.v12i2.4776
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the GDPR. International Data Privacy Law, 7(2), 76–99. https://doi.org/https://doi.org/10.1093/idpl/ipx005
Yulianto, H. (2023). Manajemen Strategis: Dasar Konsepsi Pada Organisasi Bisnis. Yudha English Gallery.
Yulianto, H. (2024a). Eksplorasi Kerangka Filosofi Inovasi Terhadap Kinerja Startup. JICN: Jurnal Intelek Dan Cendekiawan Nusantara, 1(6), 10101–10117.
Yulianto, H. (2024b). Filsafat Matematika Bisnis. JICN: Jurnal Intelek Dan Cendikiawan Nusantara, 1(2), 796–807.
Yulianto, H. (2025). Dekonstruksi Statistik dan Data Sains: Pendekatan Hermeneutik Gadamerian. Jurnal Intelek Dan Cendekiawan Nusantara, 2(3), 7644–7661.
Yulianto, H., & Iryani. (2021). Ilmu Pengetahuan dan Teknologi dalam Historis Peradaban Manusia: Tinjauan Inkuiri Filosofis. Cross-Border, 3(1), 153–168.
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
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