12,445 papers · continuously updated · last export: 10 Aug 2026livingmeta.ai
← Browse all papers
AI evidence extraction

From disclosure to evidence: Toward auditable AI use and contribution provenance

Hengzhi Hu, Harwati Hashim · Accountability in Research · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10
Relevance
1/4
Quality (LMQS)
I
Evidence
2
Citations
5.73
FWCI
Top 10%
Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/08989621.2025.2549993

Methodology & findings

Study design

The paper is a perspective/commentary piece that discusses emerging governance frameworks for generative AI across multiple published works in Accountability in Research.

Main result

The paper identifies that "transparency is elevated as an end in itself while verification" mechanisms remain underdeveloped in current AI governance frameworks. The authors argue that existing disclosure requirements do not adequately support auditable AI use and contribution provenance tracking.

Reports effect sizes.

Research paradigm

Critical discourse analysis / normative analysis of governance frameworks

Author conclusions

The authors conclude that current AI governance approaches require movement "from disclosure to evidence" with stronger mechanisms for verifying AI contributions and establishing auditable provenance, rather than relying on transparency alone as a governance solution.

Risk of bias

null

Limitations

  • The abstract provided is truncated at 200 characters, limiting full extraction of explicitly stated limitations
  • The paper appears to sketch "an emerging yet incomplete governance arc" suggesting acknowledged incompleteness in current frameworks.

Open questions raised

  • The paper identifies that while transparency is elevated as a governance priority for generative AI, the field has not adequately developed verification mechanisms and auditable provenance systems to ensure accountability is actually achieved.
  • The paper identifies gaps in verification mechanisms for AI contributions, incomplete governance frameworks for generative AI, and insufficient linkage between disclosure requirements and auditable evidence of AI use and provenance.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 79%

Explore related topics

Responsible AI UseAI GovernancePosition Paper

Related papers

← Browse all papers