Algorithmic Governance and Digital Ethics: An Organizational Approach to Automated Decision-Making
Keywords:
algorithmic governance, digital ethics, automated decision-making, algorithmic bias, transparency, Latin America
Abstract
Automated decision-making through algorithms has redefined power and control structures in public and private organizations. This transformation poses critical ethical challenges regarding transparency, oversight, and fairness in the use of digital technologies. This article empirically analyzes the state of algorithmic governance and digital ethics in organizations from Argentina, Brazil, Chile, and Uruguay, using a quantitative study involving 120 institutions. Structured instruments, factorial analysis, and hierarchical clustering were applied to assess four dimensions: algorithmic transparency, oversight mechanisms, ethical governance structures, and bias perception. Findings reveal uneven progress across countries, low formal audit levels, weak institutional adoption of ethical frameworks, and a gap between bias awareness and mitigation. The study concludes that digital ethics has not yet been consolidated as a strategic function in most organizations and outlines action paths to embed ethical principles into organizational frameworks for automated decision-making.Downloads
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References
Batool, A., Faheem, M., Naseem, R., & Butt, W. H. (2025). Artificial intelligence governance: A systematic literature review. Journal of Responsible Technology, 14, 100197. https://doi.org/10.1016/j.jrt.2023.100197
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2
Mökander, J., Polonska, A., Larsson, S., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI: A conceptual framework and research agenda. AI and Ethics, 1, 465–478. https://doi.org/10.1007/s43681-021-00038-5
OECD. (2021). OECD Framework for the Classification of AI Systems. OECD Publishing. https://www.oecd.org/going-digital/ai/classification/
Starke, C., Lünich, M., & Marcinkowski, F. (2021). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Information, Communication & Society, 26(1), 1–20. https://doi.org/10.1080/1369118X.2021.1950891
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
Wright, D., & Kreissl, R. (Eds.). (2014). Surveillance in Europe. Routledge.
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12160
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088-2
Mökander, J., Polonska, A., Larsson, S., & Floridi, L. (2021). Ethics-based auditing to develop trustworthy AI: A conceptual framework and research agenda. AI and Ethics, 1, 465–478. https://doi.org/10.1007/s43681-021-00038-5
OECD. (2021). OECD Framework for the Classification of AI Systems. OECD Publishing. https://www.oecd.org/going-digital/ai/classification/
Starke, C., Lünich, M., & Marcinkowski, F. (2021). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Information, Communication & Society, 26(1), 1–20. https://doi.org/10.1080/1369118X.2021.1950891
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
Wright, D., & Kreissl, R. (Eds.). (2014). Surveillance in Europe. Routledge.
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12160
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
Published
2021-01-21
How to Cite
Rangel Mendez, B. (2021). Algorithmic Governance and Digital Ethics: An Organizational Approach to Automated Decision-Making. IJMSOR: International Journal of Management Science & Operation Research, 10(1), 86-97. Retrieved from https://ijmsoridi.com/index.php/ijmsor/article/view/137
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