Ethical Governance of Artificial Intelligence in Contemporary Organizations

Márton Farkas

Ethical Governance of Artificial Intelligence in Contemporary Organizations

Author(s)	Dr. Márton Farkas
Country	United States
Abstract	Artificial intelligence has become embedded in organizational processes involving recruitment, employee evaluation, customer profiling, credit assessment, fraud detection, resource allocation, strategic forecasting, and automated service delivery. Although these applications may improve efficiency and analytical capacity, they also introduce risks involving algorithmic discrimination, privacy intrusion, opaque decision-making, cybersecurity vulnerabilities, accountability gaps, and the gradual displacement of meaningful human judgment. Organizations consequently require governance arrangements capable of translating ethical principles into enforceable responsibilities, documented controls, measurable performance standards, and accessible procedures for challenging harmful outcomes. This study develops and evaluates an integrated ethical AI governance framework for contemporary organizations. It combines board-level accountability, risk-based system classification, algorithmic impact assessment, data governance, fairness testing, explainability, human oversight, stakeholder participation, incident reporting, and continuing post-deployment monitoring. A simulation-based methodology compares four organizational governance configurations across 240 hypothetical AI implementation scenarios representing human-resource management, financial decision-making, customer analytics, and operational planning. The configurations range from fragmented compliance to integrated, lifecycle-based ethical governance. No real employees, customers, organizations, or personal records were included. The simulated analysis indicates that lifecycle-integrated governance produces the strongest outcomes across accountability, fairness control, transparency, privacy protection, human oversight, and incident preparedness. Its composite ethical-governance score reaches 89.4, compared with 43.7 for fragmented compliance, while the residual high-risk incident rate declines from 21.8% to 5.9%. The findings suggest that ethical AI governance cannot be achieved through broad principles or periodic legal review alone. Effective governance requires clearly assigned decision rights, risk-proportionate controls, independent assurance, reliable documentation, stakeholder-sensitive explanations, and continuing review of AI systems after deployment.
Keywords	artificial intelligence governance, organizational ethics, algorithmic accountability, responsible AI, human oversight, algorithmic fairness, corporate governance, AI risk management
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-01-31

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Ethical Governance of Artificial Intelligence in Contemporary Organizations

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