透明度未必提升用户数据共享意愿，信任感才是关键。
The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment
- 通过240人情景实验对比透明与非透明AI设计
- 透明与否对数据共享意愿无显著影响
- 用户信任感在透明场景下作用更大
人工智能系统常用于在线服务中，基于大量数据提供个性化体验。然而，AI系统可设计为黑箱或白箱模式，前者如复杂的数据处理引擎，后者则完全透明。本研究开展一项预先注册的线上情景实验，招募240名参与者，探究透明与非透明数据处理实体对用户数据共享意愿的影响。结果出人意料：两类系统间数据共享意愿无显著差异，挑战了‘透明度提升共享意愿’的普遍认知。进一步发现，用户对AI的一般信任感具有显著正向影响，尤其在透明条件下；而隐私担忧对决策无显著影响。
原文摘要 · Abstract (English)
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.
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