AI数据透明政策的局限性：三大披露谬误
The Limits of AI Data Transparency Policy: Three Disclosure Fallacies
AI总结：
本文从制度视角分析了AI数据透明政策的三大局限性，指出透明政策在目标、执行和影响方面的差距，并提出有效的透明路径。
摘要：
数据透明性已成为应对人工智能相关担忧的呼声：数据质量、隐私和版权是其中的主要问题。然而，尽管这些呼吁对于问责至关重要，当前的透明政策往往未能达到其预期目标。类似营养事实标签用于食品，目前针对人工智能的透明政策也缺乏对有效披露研究的充分考虑。我们从制度视角出发，识别了三大常见的数据披露政策实施中的谬误。首先，许多数据透明提案在声明的透明目标与实际需要实现这些目标的披露之间存在规范差距。其次，改革尝试在纸面要求的披露与实际执行以确保合规之间存在执行差距。第三，政策提案在披露信息与开发者实践和公众理解的实质性变化之间存在影响差距。基于透明度的社会科学研究，我们的分析指出了有效的透明路径，而非仅仅是象征性的。
英文摘要：
Data transparency has emerged as a rallying cry for addressing concerns about AI: data quality, privacy, and copyright chief among them. Yet while these calls are crucial for accountability, current transparency policies often fall short of their intended aims. Similar to nutrition facts for food, policies aimed at nutrition facts for AI currently suffer from a limited consideration of research on effective disclosures. We offer an institutional perspective and identify three common fallacies in policy implementations of data disclosures for AI. First, many data transparency proposals exhibit a specification gap between the stated goals of data transparency and the actual disclosures necessary to achieve such goals. Second, reform attempts exhibit an enforcement gap between required disclosures on paper and enforcement to ensure compliance in fact. Third, policy proposals manifest an impact gap between disclosed information and meaningful changes in developer practices and public understanding. Informed by the social science on transparency, our analysis identifies affirmative paths for transparency that are effective rather than merely symbolic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
