The Limits of AI Data Transparency Policy: Three Disclosure Fallacies

TL;DR

This paper critically examines current AI data transparency policies, identifying three common fallacies—specification, enforcement, and impact gaps—that hinder effective accountability and proposes paths for more meaningful disclosures.

Contribution

It offers an institutional perspective highlighting three fallacies in AI data transparency policies and suggests effective, research-informed paths for improvement.

Findings

Many policies lack clear disclosure specifications.

Enforcement of transparency requirements is often weak.

Disclosed information rarely leads to meaningful change.

Abstract

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.