The Impact of Transparency in AI Systems on Users’ Data-Sharing Intentions: A Scenario-Based Experiment

Julian Rosenberger (), Sophie Kuhlemann (), Verena Tiefenbeck (), Mathias Kraus () and Patrick Zschech ()
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Julian Rosenberger: Universität Regensburg
Sophie Kuhlemann: Friedrich-Alexander-Universität Erlangen-Nürnberg
Verena Tiefenbeck: Friedrich-Alexander-Universität Erlangen-Nürnberg
Mathias Kraus: Universität Regensburg
Patrick Zschech: TU Dresden

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 477-493 from Springer

Abstract: Abstract Artificial Intelligence (AI) systems are frequently employed in online services to provide personalized experiences to users based on large collections of data. However, AI systems can be designed in different ways, with black-box AI systems appearing as complex data-processing engines and white-box AI systems appearing as fully transparent data-processors. As such, it is reasonable to assume that these different design choices also affect user perception and thus their willingness to share data. To this end, we conducted a pre-registered, scenario-based online experiment with 240 participants and investigated how transparent and non-transparent data-processing entities influenced data-sharing intentions. Surprisingly, our results revealed no significant difference in willingness to share data across entities, challenging the notion that transparency increases data-sharing willingness. Furthermore, we found that a general attitude of trust towards AI has a significant positive influence, especially in the transparent AI condition, whereas privacy concerns did not significantly affect data-sharing decisions.

Keywords: AI; Data-sharing; Privacy; Digital markets; Personalization (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08480-4_30

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