提出10项可信编排AI标准，实现全链条治理与可控执行
Trustworthy Orchestration Artificial Intelligence by the Ten Criteria with Control-Plane Governance
- 构建控制面架构，融合人类输入与可审计性
- 确保系统可验证、透明且在人控范围内运行
- 适合关注AI治理与合规的开发者与管理者
随着人工智能系统承担越来越重要的决策角色，技术能力与制度问责之间的鸿沟日益扩大。仅靠伦理指导不足以应对这一挑战，必须将治理嵌入生态系统执行层面。本文提出可信编排人工智能的十项标准，构建一个整合人类输入、语义连贯性、审计与溯源完整性的统一控制面架构。不同于以往聚焦AI间协作的代理型系统，该框架覆盖所有AI组件、使用者及人类参与者，借鉴国际标准与澳大利亚国家人工智能保障框架，证明信任可通过工程手段系统性融入AI系统，确保执行过程可验证、透明、可复现并处于有意义的人类控制之下。
原文摘要 · Abstract (English)
As Artificial Intelligence (AI) systems increasingly assume consequential decision-making roles, a widening gap has emerged between technical capabilities and institutional accountability. Ethical guidance alone is insufficient to counter this challenge; it demands architectures that embed governance into the execution fabric of the ecosystem. This paper presents the Ten Criteria for Trustworthy Orchestration AI, a comprehensive assurance framework that integrates human input, semantic coherence, audit and provenance integrity into a unified Control-Panel architecture. Unlike conventional agentic AI initiatives that primarily focus on AI-to-AI coordination, the proposed framework provides an umbrella of governance to the entire AI components, their consumers and human participants. By taking aspiration from international standards and Australia's National Framework for AI Assurance initiative, this work demonstrates that trustworthiness can be systematically incorporated (by engineering) into AI systems, ensuring the execution fabric remains verifiable, transparent, reproducible and under meaningful human control.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。