标题： 董事会的人工智能系统和机器学习方法管理系统的开发（预印本，非官方翻译） 显示英文标题

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

摘要： 该研究探讨了公司管理范式的转变，其中人工智能从决策支持工具转变为自主决策者，一些人工智能系统已在公司中被任命为领导角色。 识别出的一个核心问题是，人工智能技术的发展远远超过了制定适当法律和伦理指南的进度。 该研究提出了一个“参考模型”，用于公司在管理中开发和实施自主人工智能系统。 该模型基于几个关键组件的综合，以确保合法和道德的决策。 该模型引入了“计算法”或“算法法”的概念。 这涉及为人工智能系统创建一个独立的法律框架，将规则和法规转化为机器可读的算法格式，以避免自然语言的歧义。 本文强调了为自主人工智能系统创建“专用操作环境”的必要性，类似于自动驾驶车辆的“操作设计领域”。 这意味着创建一个特定的、明确界定的环境和规则集，使人工智能能够安全有效地运行。 该模型主张对人工智能系统进行受控的、合成生成的数据训练，以确保公平性和伦理考量从一开始就嵌入其中。 博弈论也被提议作为计算人工智能在这些伦理和法律限制内实现其目标的最佳策略的方法。 提供的分析强调了可解释人工智能（XAI）的重要性，以确保自主系统做出的决策具有透明度和问责制。 这对于建立信任和遵守“解释权”至关重要。 显示英文摘要

摘要： 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".