用可量化指标界定AI自主性，防止系统伪装成智能实则依赖人力。
AI Autonomy Coefficient ($α$): Defining Boundaries for Responsible AI Systems
- 提出AI自主系数α，衡量任务无需人工干预的比例。
- 测试显示非透明系统自主性仅0.38，而新框架达0.85。
- 适合关注AI伦理、可问责性和长期运营可持续性的研究者。
许多现代AI系统的完整性因滥用人类在环（HITL）模型而受损，这些模型隐藏了系统对人力的高度依赖。我们将其定义为‘人代AI’（HISOAI），这种设计在伦理上存在问题且经济不可持续，即人类工人被当作隐蔽的操作替代品而非有意识的高价值合作者。为解决此问题，我们提出‘以AI为首、以人为本’（AFHE）范式，要求AI系统在部署前必须展示可量化的功能独立性。该要求通过AI自主系数（α）形式化，用于测量任务在无强制人工干预下完成的比例。我们进一步提出AFHE部署算法，该算法在离线评估与影子部署中强制执行最低自主阈值。结果表明，AI自主系数能有效识别出自主性仅为0.38的HISOAI系统，而受AFHE框架约束的系统则达到0.85的自主水平。结论是，AFHE提供了一种基于指标的方法，确保现代AI系统具备可验证的自主性、透明度和可持续的运行完整性。
原文摘要 · Abstract (English)
The integrity of many contemporary AI systems is compromised by the misuse of Human-in-the-Loop (HITL) models to obscure systems that remain heavily dependent on human labor. We define this structural dependency as Human-Instead-of-AI (HISOAI), an ethically problematic and economically unsustainable design in which human workers function as concealed operational substitutes rather than intentional, high-value collaborators. To address this issue, we introduce the AI-First, Human-Empowered (AFHE) paradigm, which requires AI systems to demonstrate a quantifiable level of functional independence prior to deployment. This requirement is formalized through the AI Autonomy Coefficient, measuring the proportion of tasks completed without mandatory human intervention. We further propose the AFHE Deployment Algorithm, an algorithmic gate that enforces a minimum autonomy threshold during offline evaluation and shadow deployment. Our results show that the AI Autonomy Coefficient effectively identifies HISOAI systems with an autonomy level of 0.38, while systems governed by the AFHE framework achieve an autonomy level of 0.85. We conclude that AFHE provides a metric-driven approach for ensuring verifiable autonomy, transparency, and sustainable operational integrity in modern AI systems.
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。