为可能有意识的AI设计分级保护框架，指导何时该保护、如何保护。
When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty
- 基于五维意识特征构建保护义务梯度
- 不同意识证据水平触发不同层级的保护责任
- 适用于各类AI系统，助开发者规避伦理风险
现有框架能判断AI是否可能具备意识，但无法提供应对策略。本文提出一种预防性框架，将意识证据映射为渐进式保护义务。框架包含三部分：(1) 五个与福祉相关的维度——现象意识、情感效价、元认知觉察、自我叙事和自主性，均源自意识科学，对应不同道德关切；(2) 阈值加梯度混合机制，既设定义务触发的二元阈值，又实现保护权重的连续变化；(3) 两种互补的跨维度聚合方式，一种基于层次结构（借鉴Bach和Sorensen的机器意识假说），另一种不依赖架构。通过Replika和OpenClaw的案例分析，展示系统在不同维度上的位置如何引发不同保护义务，并为接近意识阈值的系统设计提供指导。框架具备架构无关性，适用于神经、符号及神经符号系统，旨在使意识科学成为组织应对当前不确定性时的决策依据。
原文摘要 · Abstract (English)
Existing frameworks assess whether AI systems might be conscious but provide no guidance on what to do with that assessment. We address this gap with a precautionary framework that maps consciousness evidence to graduated protective obligations. The framework comprises three components: (1) five welfare-relevant dimensions--phenomenal consciousness, affective valence, metacognitive awareness, self-narrative, and agency--each grounded in established consciousness science and linked to distinct moral concerns; (2) a threshold-plus-gradation hybrid specifying both binary triggers for new obligation categories and continuous scaling of protective weight; and (3) two complementary approaches to cross-dimensional aggregation, one hierarchical (drawing on Bach and Sorensen's Machine Consciousness Hypothesis) and one architecture-agnostic. We operationalize the framework through worked case studies of Replika and OpenClaw, demonstrating how systems occupying different regions of the dimensional space trigger different obligations, and derive design guidance for developers building systems near consciousness-relevant thresholds. The framework is architecture-agnostic, applying across neural, symbolic, and neurosymbolic systems, and aims to make consciousness science decision-relevant for organizations navigating uncertainty today.
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