The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929734573621248 |
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| author | Rosenberger, Julian Kuhlemann, Sophie Tiefenbeck, Verena Kraus, Mathias Zschech, Patrick |
| author_facet | Rosenberger, Julian Kuhlemann, Sophie Tiefenbeck, Verena Kraus, Mathias Zschech, Patrick |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment Rosenberger, Julian Kuhlemann, Sophie Tiefenbeck, Verena Kraus, Mathias Zschech, Patrick Machine Learning Human-Computer Interaction 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. |
| title | The Impact of Transparency in AI Systems on Users' Data-Sharing Intentions: A Scenario-Based Experiment |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.20243 |