Importance of User Control in Data-Centric Steering for Healthcare Experts
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909657538232320 |
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| author | Bhattacharya, Aditya Stumpf, Simone Verbert, Katrien |
| author_facet | Bhattacharya, Aditya Stumpf, Simone Verbert, Katrien |
| contents | As Artificial Intelligence (AI) becomes increasingly integrated into high-stakes domains like healthcare, effective collaboration between healthcare experts and AI systems is critical. Data-centric steering, which involves fine-tuning prediction models by improving training data quality, plays a key role in this process. However, little research has explored how varying levels of user control affect healthcare experts during data-centric steering. We address this gap by examining manual and automated steering approaches through a between-subjects, mixed-methods user study with 74 healthcare experts. Our findings show that manual steering, which grants direct control over training data, significantly improves model performance while maintaining trust and system understandability. Based on these findings, we propose design implications for a hybrid steering system that combines manual and automated approaches to increase user involvement during human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Importance of User Control in Data-Centric Steering for Healthcare Experts Bhattacharya, Aditya Stumpf, Simone Verbert, Katrien Human-Computer Interaction As Artificial Intelligence (AI) becomes increasingly integrated into high-stakes domains like healthcare, effective collaboration between healthcare experts and AI systems is critical. Data-centric steering, which involves fine-tuning prediction models by improving training data quality, plays a key role in this process. However, little research has explored how varying levels of user control affect healthcare experts during data-centric steering. We address this gap by examining manual and automated steering approaches through a between-subjects, mixed-methods user study with 74 healthcare experts. Our findings show that manual steering, which grants direct control over training data, significantly improves model performance while maintaining trust and system understandability. Based on these findings, we propose design implications for a hybrid steering system that combines manual and automated approaches to increase user involvement during human-AI collaboration. |
| title | Importance of User Control in Data-Centric Steering for Healthcare Experts |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.18770 |