构建可动态适应的伦理推理框架，让AI在复杂场景中做出合乎道德的决策。
Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems
- 融合概率推理与知识表示，实现上下文感知的伦理判断。
- 支持单智能体与多智能体系统的可扩展伦理决策。
- 为真实世界中的道德困境提供理论基础与实施路径。
在复杂现实环境中运行的AI与自主系统亟需计算伦理框架。现有方法往往缺乏在动态、模糊情境中整合伦理原则的适应性，限制了其在多样化场景中的有效性。为此，我们提出一个整体性的元层级框架，结合中间表征、概率推理与知识表示，强调可扩展性，支持个体决策与多智能体系统集体行为中的伦理推理。通过融合理论原则与上下文因素，该框架实现结构化且情境敏感的决策，确保与总体伦理标准对齐。我们进一步探讨了伦理推理机制应遵循的定理，为实际应用奠定基础。这些构建旨在推动具备鲁棒性与伦理可靠性的AI系统发展，使其能够应对真实世界中的复杂道德决策挑战。
原文摘要 · Abstract (English)
A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts, limiting their effectiveness across diverse scenarios. To address these challenges, we outline the necessary ingredients for building a holistic, meta-level framework that combines intermediate representations, probabilistic reasoning, and knowledge representation. The specifications therein emphasize scalability, supporting ethical reasoning at both individual decision-making levels and within the collective dynamics of multi-agent systems. By integrating theoretical principles with contextual factors, it facilitates structured and context-aware decision-making, ensuring alignment with overarching ethical standards. We further explore proposed theorems outlining how ethical reasoners should operate, offering a foundation for practical implementation. These constructs aim to support the development of robust and ethically reliable AI systems capable of navigating the complexities of real-world moral decision-making scenarios.
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