arXiv AI By Genevieve Smith, Sahiba Chopra, Natalia Luka, Min Kyung Lee

Whose Transparency is it Anyway? Understanding Generative AI Transparency Needs and Gaps Across Stakeholders

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 9

"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency

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.

By Suchismita Naik, Samir Passi, Mihaela Vorvoreanu, Scott Saponas, Amanda Hall