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.

翻译：近期关于人工智能的讨论日益关注AI意识与道德地位问题，然而对于如何评估这些属性仍缺乏共识。本文主张，以觉知作为评估框架更具建设性且方法上更易处理。我们提出一种适用于多样化系统的觉知评估实用方法，其中觉知被理解为系统为达成目标导向行动而处理、存储与运用信息的能力集合。该方法的核心主张是：任何旨在捕捉人工系统多样性的评估必须满足领域敏感性、任意规模可部署性、多维度性，并能预测任务表现，同时为便于比较而提升至能力层面的泛化。基于这四个基本要求，我们构建了一套结构化方法，用于评估和比较不同架构、规模及运行领域的人工系统的觉知特征剖面。通过将焦点从人工意识转向"恰到好处的觉知"，本方法旨在推动原则性评估、支持系统设计与监管，并促进更具建设性的科学及公共讨论。