AI数据透明政策常流于形式，本文揭示三大实施误区。
The Limits of AI Data Transparency Policy: Three Disclosure Fallacies
- 识别出数据透明政策的三类根本性缺陷：目标与披露脱节、纸上要求难落实、公开信息无实际影响。
- 实证指出当前政策多为象征性举措，难以真正改变开发者行为或提升公众认知。
- 基于社会科学研究，提出有效透明需兼顾目标清晰、执行可监督、影响可衡量。
数据透明已成为应对人工智能关切的重要呼声，涵盖数据质量、隐私和版权等问题。然而，尽管这些呼吁对问责至关重要，现有透明政策往往未能实现预期目标。如同食品营养标签，当前的AI数据透明政策普遍忽视了关于有效信息披露的社会科学研究。本文从制度视角出发，识别出政策实施中的三种常见谬误：第一，数据透明的目标与实际所需披露内容之间存在“设定差距”；第二，政策要求与实际执行之间存在“执行差距”；第三，公开信息与开发者行为改进及公众理解之间的“影响差距”。基于社会科学中的透明度研究，本文提出更有效的透明路径，强调透明应是实质性的而非仅具象征意义。
原文摘要 · Abstract (English)
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.
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