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.

EU regulations and business result in a growing awareness of the benefits of inter-organizational data sharing. However, data sharing is often hindered by obstacles such as potential data misappropriation and perceived risks that outweigh possible benefits. One mitigation strategy is using a data trustee – a neutral data intermediary – that addresses these challenges and ensures secure and trusted data sharing. These data trustees uphold the rights of the data provider and give the data user legal assurance and clarity about what they are allowed to do with the data they obtain. Data trustees are still a novel phenomenon, and only a few are operating in the market. In our paper, we shed explore the characteristics of data trustees through a business model lens.