AI透明度：治理合规还是利益相关者需求？
AI Transparency: Governance Compliance or Stakeholder Requirements?
AI总结：
通过分析92份澳大利亚政府AI透明度声明，提出RCIN框架评估利益相关者需求，发现合规普遍但校准不均，存在“透明度幻觉”。
摘要：
透明度越来越被要求用于公共部门的AI系统，组织需要发布声明描述其AI使用和监督安排。然而，尽管有限证据表明这些声明能适当服务于相关利益相关者群体，这些文物的存在往往被视为等同于透明度本身。从需求工程的角度来看，这引发了一个验证问题：遵守强制披露标准并不一定确保对不同风险暴露、决策控制和参与程度的利益相关者具有足够的透明度。本文对澳大利亚政府机构根据国家AI治理授权发布的92份公开AI透明度声明进行了实证分析。我们引入了利益相关者风险-控制-参与-需求（RCIN）框架，根据利益相关者的结构位置和透明度需求对其进行分类。使用从强制标准派生的结构化评估标准，我们评估了授权和已发布声明如何针对每个利益相关者类别进行校准。结果表明，虽然结构性合规普遍存在，但透明度校准并不均匀。服务于高控制利益相关者的标准得到一致实现，而对高风险、低控制利益相关者最关键的标准较少且实质性不足。我们将此概念化为“透明度幻觉”：一种通过合规文物表面满足透明度，但对承受AI支持决策最大风险的利益相关者校准不均的状况。该研究将透明度视为一个利益相关者校准的验证问题，表明文物级合规在此背景下并不构成需求验证。
英文摘要：
Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements. However, the existence of such artefacts is often treated as equivalent to transparency itself, despite limited evidence that they proportionately serve relevant stakeholder groups. From a requirements engineering perspective, this raises a validation concern: compliance with mandated disclosure criteria does not necessarily ensure transparency adequacy for stakeholders with different levels of risk exposure, decision control, and involvement. This paper presents an empirical analysis of 92 publicly available AI transparency statements published by Australian Government agencies under the national AI governance mandate. We introduce the stakeholder Risk--Control--Involvement--Need (RCIN) framework to differentiate stakeholder classes according to their structural position and transparency needs. Using a structured rubric derived from the mandated criteria, we evaluate how both the mandate and published statements are calibrated to each stakeholder class. The findings show that while structural compliance is widespread, transparency calibration is uneven. Criteria serving high-control stakeholders are consistently realised, whereas criteria most critical for high-risk, low-control stakeholders are fewer and less substantively addressed. We conceptualise this as the Transparency Illusion: a condition in which transparency appears satisfied through compliant artefacts yet remains unevenly calibrated to stakeholders bearing the greatest exposure to AI-supported decisions. The study frames transparency as a stakeholder-calibrated validation problem, demonstrating that artefact-level compliance does not constitute requirements validation in this context.
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