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

2025-02-27

AI Summary

This study investigates the mechanism through which AI system transparency (black-box vs. white-box) influences users’ willingness to share personal data. Employing a pre-registered scenario-based experiment (N = 240) with structured questionnaires and regression analysis, we find that transparency per se does not significantly increase data-sharing willingness. Instead, overall AI trust functions as a critical positive moderator: transparency enhances sharing only among users with relatively high pre-existing trust. Privacy concerns show no statistically significant effect. This is the first empirical demonstration of a conditional “transparency–trust–sharing” pathway, challenging the oversimplified assumption that transparency inherently fosters trust. The findings advance theoretical understanding of human-AI interaction and provide actionable insights for context-sensitive transparency design and trust-building strategies in AI systems.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Transparency impact on data-sharing
• User trust in AI systems
• Privacy concerns in AI utilization

Innovation

• Transparent AI systems
• Scenario-based experiment
• Trust influences sharing