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The paper investigates how different stakeholders—end‑users, AI builders, and policymakers—define and experience transparency in generative AI. It finds that while all groups agree on core goals of accuracy and privacy, end‑users perceive larger gaps between desired and available transparency features, with only 28% feeling well served compared to 53% of builders and 49% of policymakers. The study also shows that transparency is context‑dependent, shifting priorities based on task and role, and calls for pluralistic transparency infrastructures rather than a one‑size‑fits‑all approach.
arXiv:2606. 08323v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration.
arXiv:2606. 30652v1 Announce Type: cross Abstract: Transparency is increasingly mandated for public-sector AI systems, with organisations required to publish statements describing their AI use and oversight arrangements.
arXiv:2606. 11116v1 Announce Type: cross Abstract: As newsrooms integrate generative AI, journalists face a disclosure challenge: how to communicate AI involvement in ways that maintain reader trust.
arXiv:2511. 08639v4 Announce Type: replace-cross Abstract: Existing AI disclosure mandates in scholarship require that AI assistance be reported but leave transparency philosophically unspecified: they fix the duty without explaining what the duty serves.
arXiv:2610.16887v1 Announce Type: new Abstract: Transparency in AI-supported decision systems is widely required but poorly defined. Existing approaches treat transparency as a property of systems or...