References
- BAROCAS, S. & SELBST, A. D. (2016): Big data’s disparate impact. In: California Law Review, 104(3), pp. 671–732.
- BEAUCHAMP, T. L. & CHILDRESS, J. F. (2019): Principles of biomedical ethics (8th ed.). New York: Oxford University Press.
- BUOLAMWINI, J. & GEBRU, T. (2018): Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Proceedings of Machine Learning Research, 81, pp. 1–15.
- BRENNAN, D. (2024): Revisiting Czech philosophical critiques of science in the age of generative AI and big data. In: Ethics & Bioethics (in Central Europe), 14(3–4), pp. 235–238.
- BURRELL, J. (2016): How the machine “thinks”: Understanding opacity in machine learning algorithms. In: Big Data & Society, 3(1), pp. 1–12. [online] [Retrieved January 14, 2026] Available at:
https://doi.org/10.1177/2053951715622512 - CARROLL, A. B. (1991): The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders. In: Business Horizons, 34(4), pp. 39–48. [online] [Retrieved December 22, 2025] Available at:
https://doi.org/10.1016/0007-6813(91)90005-G - COMMITTEE OF SPONSORING ORGANIZATIONS OF THE TREADWAY COMMISSION (COSO) (2017): Enterprise risk management: Integrating with strategy and performance. New York: AICPA.
- DIAKOPOULOS, N. (2016): Accountability in algorithmic decision making. In: Communications of the ACM, 59(2), pp. 56–62. [online] [Retrieved December 26, 2025] Available at:
https://doi.org/10.1145/2844110 - DONALDSON, T. & PRESTON, L. E. (1995): The stakeholder theory of the corporation: Concepts, evidence, and implications. In: Academy of Management Review, 20(1), pp. 65–91. [online] [Retrieved December 26, 2025] Available at:
https://doi.org/10.5465/amr.1995.9503271992 - DOSHI-VELEZ, F. & KIM, B. (2017): Toward a rigorous science of interpretable machine learning. In: arXiv. [online] [Retrieved January 15, 2026] Available at:
https://arxiv.org/abs/1702.08608 - EUROPEAN PARLIAMENT & COUNCIL OF THE EUROPEAN UNION (2016): Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation). In: Official Journal of the European Union. [online] [Retrieved January 2, 2026] Available at:
https://eur-lex.europa.eu/eli/reg/2016/679/oj - EUROPEAN PARLIAMENT & COUNCIL OF THE EUROPEAN UNION (2016): Regulation (EU) 2016/679 (GDPR), Article 22 (Automated individual decision-making, including profiling). [online] [Retrieved January 16, 2026] Available at:
https://gdpr-info.eu/art-22-gdpr/ - EUROPEAN PARLIAMENT & COUNCIL OF THE EUROPEAN UNION (2024): Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). In: Official Journal of the European Union. [online] [Retrieved December 27, 2025] Available at:
https://eur-lex.europa.eu/eli/reg/2024/1689/oj - FAMA, E. F. & JENSEN, M. C. (1983): Separation of ownership and control. In: Journal of Law and Economics, 26(2), pp. 301–325. [online] [Retrieved December 30, 2025] Available at:
https://doi.org/10.1086/467037 - FREEMAN, R. E. (1984): Strategic management: A stakeholder approach. Boston: Pitman.
- FREEMAN, R. E., HARRISON, J. S. et al. (2010): Stakeholder theory: The state of the art. Cambridge: Cambridge University Press.
- FRENCH, P. A. (1979): The Corporation as a Moral Person. In: American Philosophical Quarterly, 16(3), pp. 207–215. [online] [Retrieved January 15, 2026] Available at:
https://www.sci.brooklyn.cuny.edu/~schopra/Persons/French.pdf - GARRIGA, E. & MELÉ, D. (2004): Corporate social responsibility theories: Mapping the territory. In: Journal of Business Ethics, 53(1–2), pp. 51–71. [online] [Retrieved January 15, 2026] Available at:
https://doi.org/10.1023/B:BUSI.0000039399.90587.34 - HARDT, M., PRICE, E. & SREBRO, N. (2016): Equality of opportunity in supervised learning. In: D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon & R. Garnett (eds.): Advances in Neural Information Processing Systems, vol. 29. Red Hook, NY: Curran Associates, pp. 3315–3323.
- JENSEN, M. C. & MECKLING, W. H. (1976): Theory of the firm: Managerial behavior, agency costs and ownership structure. In: Journal of Financial Economics, 3(4), pp. 305–360. [online] [Retrieved January 16, 2026] Available at:
https://doi.org/10.1016/0304-405X(76)90026-X - JOBIN, A., IENCA, M. & VAYENA, E. (2019): The global landscape of AI ethics guidelines. In: Nature Machine Intelligence, 1(9), pp. 389–399. [online] [Retrieved January 14, 2026] Available at:
https://doi.org/10.1038/s42256-019-0088-2 - KELLOGG, K. C., VALENTINE, M. A. & CHRISTIN, A. (2020): Algorithms at work: The new contested terrain of control. In: Academy of Management Annals, 14(1), pp. 366–410. [online] [Retrieved December 27, 2025] Available at:
https://doi.org/10.5465/annals.2018.0174 - KROLL, J. A., HUEY, J. et al. (2017): Accountable algorithms. In: University of Pennsylvania Law Review, 165(3), pp. 633–705.
- MATTHIAS, A. (2004): The responsibility gap: Ascribing responsibility for the actions of learning automata. In: Ethics and Information Technology, 6(3), pp. 175–183. [online] [Retrieved January 14, 2026] Available at:
https://doi.org/10.1007/s10676-004-3422-1 - MITCHELL, M., WU, S. et al. (2019): Model cards for model reporting. In: Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT’19)*, pp. 220–229. New York: ACM. [online] [Retrieved January 14, 2026] Available at:
https://doi.org/10.1145/3287560.3287596 - MITTELSTADT, B. D., ALLEN, L. et al. (2016): The Ethics of Algorithms: Mapping the Debate. In: Big Data & Society, 3(2), pp. 1–21. [online] [Retrieved January 15, 2026] Available at:
https://journals.sagepub.com/doi/10.1177/2053951716679679 - NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY (NIST) (2023): Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. Gaithersburg, MD: U.S. Department of Commerce. [online] [Retrieved January 2, 2026] Available at:
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf - OECD (2015): G20/OECD Principles of Corporate Governance. Paris: OECD Publishing.
- OECD (2019): Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449). OECD Legal Instruments. [online] [Retrieved January 30, 2026] Available at:
https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 - O’NEIL, C. (2016): Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown.
- PASQUALE, F. (2015): The black box society: The secret algorithms that control money and information. Cambridge, MA: Harvard University Press.
- RAJI, I. D., SMART, A. et al. (2020): Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT’20)*, pp. 33–44. New York: ACM. [online] [Retrieved January 16, 2026] Available at:
https://doi.org/10.1145/3351095.3372873 - RAWLS, J. (1971): A theory of justice. Cambridge, MA: Harvard University Press.
- RUDIN, C. (2019): Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. In: Nature Machine Intelligence, 1, pp. 206–215. [online] [Retrieved January 15, 2026] Available at:
https://doi.org/10.1038/s42256-019-0048-x - SEN, A. (2009): The idea of justice. Cambridge, MA: Harvard University Press.
- SELBST, A. D., BOYD, D., FRIEDLER, S. A., VENKATASUBRAMANIAN, S. & VERTESI, J. (2019): Fairness and Abstraction in Sociotechnical Systems. In: Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency (FAT)*, pp. 59–68.
- SUCHMAN, M. C. (1995): Managing legitimacy: Strategic and institutional approaches. In: Academy of Management Review, 20(3), pp. 571–610. [online] [Retrieved January 20, 2026] Available at:
https://doi.org/10.5465/amr.1995.9508080331 - SUNSTEIN, C. R. (2016): The ethics of influence: Government in the age of behavioral science. Cambridge: Cambridge University Press.
- SUSSER, D., ROESSLER, B. & NISSENBAUM, H. (2019): Online manipulation: Hidden influences in a digital world. In: Georgetown Law Technology Review, 4(1), pp. 1–45.
- ŠPIRKOVÁ, S. (2023): Free will, moral responsibility and automatisms. In: Ethics & Bioethics (in Central Europe), 13(1–2), pp. 55–71.
- ŠVAŇA, L. (2023): War and peace as consequences of human nature? In: Ethics & Bioethics (in Central Europe), 13(1–2), pp. 72–82.
- THALER, R. H. & SUNSTEIN, C. R. (2008): Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
- TRICKER, R. I. (2015): Corporate governance: Principles, policies, and practices (3rd ed.). Oxford: Oxford University Press.
- UNESCO (2005): Universal Declaration on Bioethics and Human Rights. Paris: United Nations Educational, Scientific and Cultural Organization. [online] [Retrieved January 20, 2026] Available at:
https://unesdoc.unesco.org/ark:/48223/pf0000146180 - VOIGT, P. & VON DEM BUSSCHE, A. (2017): The EU general data protection regulation (GDPR): A practical guide. Cham: Springer. [online] [Retrieved January 20, 2026] Available at:
https://doi.org/10.1007/978-3-319-57959-7 - WACHTER, S., MITTELSTADT, B. & FLORIDI, L. (2017): Why a right to explanation of automated decision-making does not exist in the GDPR. In: International Data Privacy Law, 7(2), pp. 76–99. [online] [Retrieved January 16, 2026] Available at:
https://doi.org/10.1093/idpl/ipx005 - ZUBOFF, S. (2019): The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: PublicAffairs.
Language: English
Page range: 77 - 90
Published on: Jun 10, 2026
Published by: University of Prešov
In partnership with: Paradigm Publishing Services
Keywords:
Related subjects:
© 2026 Martin Pazdera, published by University of Prešov
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.