Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)

• URL: http://arxiv.org/abs/2508.03769v1
• Date: Tue, 05 Aug 2025 04:01:22 GMT
• Title: Development of management systems using artificial intelligence systems and machine learning methods for boards of directors (preprint, unofficial translation)
• Authors: Anna Romanova,
• Abstract summary: The study addresses the paradigm shift in corporate management, where AI is moving from a decision support tool to an autonomous decision-maker.<n>A central problem identified is that the development of AI technologies is far outpacing the creation of adequate legal and ethical guidelines.<n>The research proposes a "reference model" for the development and implementation of autonomous AI systems in corporate management.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The study addresses the paradigm shift in corporate management, where AI is moving from a decision support tool to an autonomous decision-maker, with some AI systems already appointed to leadership roles in companies. A central problem identified is that the development of AI technologies is far outpacing the creation of adequate legal and ethical guidelines. The research proposes a "reference model" for the development and implementation of autonomous AI systems in corporate management. This model is based on a synthesis of several key components to ensure legitimate and ethical decision-making. The model introduces the concept of "computational law" or "algorithmic law". This involves creating a separate legal framework for AI systems, with rules and regulations translated into a machine-readable, algorithmic format to avoid the ambiguity of natural language. The paper emphasises the need for a "dedicated operational context" for autonomous AI systems, analogous to the "operational design domain" for autonomous vehicles. This means creating a specific, clearly defined environment and set of rules within which the AI can operate safely and effectively. The model advocates for training AI systems on controlled, synthetically generated data to ensure fairness and ethical considerations are embedded from the start. Game theory is also proposed as a method for calculating the optimal strategy for the AI to achieve its goals within these ethical and legal constraints. The provided analysis highlights the importance of explainable AI (XAI) to ensure the transparency and accountability of decisions made by autonomous systems. This is crucial for building trust and for complying with the "right to explanation".

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