交互式机器学习界面用于疾病风险预测：对风险感知和行为的影响
Interactive Machine Learning Interfaces for Disease Risk Prediction: Effects on Risk Perception and Behaviour
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- University of Waterloo（滑铁卢大学）
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中文总结 AI 辅助
本研究通过15人混合方法实验，发现用户对交互式2型糖尿病风险界面的实际理解优于自我感知，但存在术语、风险框架和解释障碍，据此提出设计指南以提升信任与公平。
中文摘要 AI 辅助
机器学习风险模型正越来越多地应用于面向患者的健康工具中，但用户对这些系统所呈现信息的理解程度仍不明确。在本研究中，我们探讨人们如何解读一个交互式2型糖尿病（T2D）风险界面，以及与之交互是否会影响他们对行为改变的态度。通过一项包含15名参与者的探索性混合方法研究，我们比较了参与者的感知理解与实际理解，并从定性访谈中识别出关键主题。我们发现，参与者对界面的理解往往比他们最初认为的更好，但仍面临与术语不清、风险框架模糊以及模型输入解释有限相关的重要障碍。最后，我们提出了相关的设计指南，并讨论了围绕信任和公平性的更广泛问题。我们的研究结果强调了在面向患者的机器学习界面中，直观的视觉设计、熟悉的呈现方式和清晰的解释的重要性。
英文摘要
Machine learning risk models are increasingly being used in patient-facing health tools, but it remains unclear how well users understand the information these systems present. In this work, we study how people interpret an interactive Type 2 Diabetes (T2D) risk interface and whether interacting with it influences their attitudes toward behavioural change. Through an exploratory mixed-methods study with 15 participants, we compare participants' perceived understanding with their actual understanding and identify key themes from qualitative interviews. We find that participants often understood the interface better than they initially believed, but still faced important barriers related to unclear terminology, ambiguous risk framing, and limited explanations of model inputs. Finally, we propose relevant design guidelines and discuss broader issues surrounding trust and fairness. Our findings highlight the importance of intuitive visual design, familiar presentation, and clear explanations in patient-facing ML interfaces.
