High-risk AI transparency? On qualified transparency mandates for oversight bodies under the EU AI Act

The legal opacity of AI technologies has long posed challenges in addressing algorithmic harms, as secrecy enables companies to retain competitive advantages while limiting public scrutiny. In response, ideas such as qualified transparency have been proposed to provide AI accountability within the confidentiality constraints. With the introduction of the EU AI Act, the foundations for human-centric and trustworthy AI have been established. The framework sets regulatory requirements for certain AI technologies and grants oversight bodies broad transparency mandates to enforce the new rules. This paper examines these transparency mandates under the AI Act and argues that it effectively implements qualified transparency, which may potentially mitigate the problem of AI opacity. Nevertheless, several challenges remain in achieving the Act’s policy objectives.

Paper

Full text

PDF

High-risk AI transparency? On qualified transparency mandates for oversight bodies under the EU AI Act

Semantic Scholar · 2025

Abstract

The legal opacity of AI technologies has long posed challenges in addressing algorithmic harms, as secrecy enables companies to retain competitive advantages while limiting public scrutiny. In response, ideas such as qualified transparency have been proposed to provide AI accountability within the confidentiality constraints. With the introduction of the EU AI Act, the foundations for human-centric and trustworthy AI have been established. The framework sets regulatory requirements for certain AI technologies and grants oversight bodies broad transparency mandates to enforce the new rules. This paper examines these transparency mandates under the AI Act and argues that it effectively implements qualified transparency, which may potentially mitigate the problem of AI opacity. Nevertheless, several challenges remain in achieving the Act’s policy objectives.

References (57)

Scroll for more · 38 remaining

Similar papers

© 2026 NYSGPT2525 LLC