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

AI is moving from advisor to decision-maker faster than governance can keep up: The paper identifies a critical gap where AI systems are already being appointed to leadership roles in companies, yet legal and ethical guidelines lag significantly behind technological development.

"Computational law" could translate rules into machine-readable formats: The research proposes converting legal and ethical guidelines into algorithmic language to reduce ambiguity, though this raises questions about whether complex legal concepts can be adequately captured in code.

AI systems need clearly defined operating boundaries, similar to autonomous vehicles: The paper advocates for a "dedicated operational context" that specifies exactly where, when, and under what conditions autonomous AI can make corporate decisions—rather than deploying it without such constraints.

Training data matters for embedding ethics from the start: The model recommends using controlled, synthetically generated data during AI development to ensure fairness and ethical considerations are built in from the beginning, rather than trying to fix problems after deployment.

A comprehensive reference model is needed before widespread adoption: Rather than allowing ad-hoc implementation, the paper argues companies need a structured framework combining legal translation, operational boundaries, ethical training data, and game theory optimization before deploying autonomous AI to boards of directors.