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

Authors: Genevieve Smith , Sahiba Chopra , Natalia Luka , Min Kyung Lee

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Abstract: Transparency has become a central principle in AI governance, yet there is little agreement about what transparency means, for whom, and in what contexts. Drawing on interpretive flexibility within the Social Construction of Technology, this paper examines how different stakeholders understand and experience transparency in generative AI. This study draws from a global survey of 400 end-users, AI builders, and policymakers, complemented by a structured focus group with end-users, grounding the quantitative findings in qualitative insights from the group most underserved by current transparency practice. We present three key findings. First, all three groups share similar understandings of what transparency means and what it should afford, centered on ensuring accuracy and privacy. Second, gaps between the importance of transparency features and their perceived availability are substantially larger for end-users than builders, revealing how power shapes what gets built and who it serves. Only 28% of end-users report that current transparency practices reflect their needs very well, compared to 53% of builders and 49% of policymakers. Third, transparency is situationally constructed, priorities shift with context and task structure, with role-specific frames activating when context demands them and receding when it does not. We argue that meaningful AI transparency requires moving beyond a one-size-fits-all solution, including current emphases on AI disclosure and static documentation, toward pluralistic transparency infrastructures that reflect the needs of diverse users and stakeholders.

Subjects:

Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)

Cite as:

arXiv:2611.05549 [cs.AI]

(or

arXiv:2611.05549v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.05549

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Natalia Luka [ view email ]

[v1]

Tue, 3 Nov 2026 19:40:36 UTC (2,029 KB)

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