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

TL;DR

This study examined how transparency in AI systems affects users' willingness to share data, finding no significant difference between transparent and non-transparent AI, but highlighting the role of trust in influencing sharing intentions.

Contribution

It provides empirical evidence that transparency alone does not increase data-sharing willingness, emphasizing the importance of trust over privacy concerns in AI interactions.

Findings

No significant difference in data-sharing willingness between transparent and non-transparent AI.

Trust in AI positively influences data-sharing intentions, especially with transparent AI.

Privacy concerns did not significantly impact users' data-sharing decisions.

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.