Ethical risks of artificial intelligence-based decision support systems in organizations

This article analyzes the ethical risks arising from the use of artificial intelligence (AI)-based decision support systems in organizations. While these systems improve the speed and validity of decisions by processing large amounts of data, they also raise issues of algorithmic bias, opacity, erosion of responsibility, and privacy protection. Based on a literature review and an analysis of real-world AI case studies, four groups of issues that create ethical risks are identified: bias and fairness issues, transparency and explainability issues, accountability and responsibility in decision-making, and privacy and privacy issues. It is shown that mitigating these risks requires not only compliance with regulatory requirements but also organizational measures (ethical system design, algorithmic audit, the creation of AI ethics committees, and continuous monitoring of models after deployment). The combination of fairness, explainability, and accountability allows organizations to leverage the benefits of AI in decision-making without deviating from ethical standards. The scientific novelty of this study lies in its systematization of the ethical risks of AI-based decision support systems as applied to organizations. The practical significance of this study lies in the potential use of the proposed strategies for the ethical implementation of AI in the design and implementation of decision support systems in organizations.