提出可评估人工系统认知程度的新方法，聚焦目标导向行为中的信息处理能力。
Just aware enough: Evaluating awareness across artificial systems
- 以目标导向行动中的信息处理、存储与利用能力定义意识
- 构建多维度、跨尺度、可预测任务表现的评估框架
- 适用于不同架构和规模系统，适合科研与监管使用
近期关于人工智能的讨论日益关注其意识与道德地位问题，但对此类属性的评估尚无共识。本文主张以‘意识’作为更具操作性和方法论可行性的替代路径。我们提出一种适用于多样化人工系统的实用评估方法，将意识理解为系统在实现目标导向行为过程中对信息的处理、存储与运用能力。该方法强调评估必须具备领域敏感性、可扩展性、多维度特征，并能预测任务表现，同时支持跨系统的能力比较。基于四大核心要求，本文构建了一套结构化评估框架，用于对比不同架构、规模和应用领域的系统意识水平。通过将焦点从人工意识转向‘恰到好处的意识’，该方法旨在促进严谨评估，辅助设计与监督，并推动更建设性的科学与公共讨论。
原文摘要 · Abstract (English)
Recent debates on artificial intelligence increasingly emphasise questions of AI consciousness and moral status, yet there remains little agreement on how such properties should be evaluated. In this paper, we argue that awareness offers a more productive and methodologically tractable alternative. We introduce a practical method for evaluating awareness across diverse systems, where awareness is understood as encompassing a system's abilities to process, store and use information in the service of goal-directed action. Central to this approach is the claim that any evaluation aiming to capture the diversity of artificial systems must be domain-sensitive, deployable at any scale, multidimensional, and enable the prediction of task performance, while generalising to the level of abilities for the sake of comparison. Given these four desiderata, we outline a structured approach to evaluating and comparing awareness profiles across artificial systems with differing architectures, scales, and operational domains. By shifting the focus from artificial consciousness to being just aware enough, this approach aims to facilitate principled assessment, support design and oversight, and enable more constructive scientific and public discourse.
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