用相似案例辅助医生理解AI，减少误信错误结果的风险。
Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making
- 通过展示与当前病例最相似的历史案例来引导医生上手AI
- 使用相似案例解释后，正确决策率提升，错误决策率下降
- 适合医疗AI落地初期需培训医生信任AI的场景
越来越多研究关注AI解释对人类决策阶段的影响。然而，先前研究发现用户容易过度依赖‘错误’的AI输出。本文提出交互式示例解释方法，帮助医疗专业人员更好地在人机协作决策中建立对AI的合理依赖。我们构建了一个基于神经网络的决策支持系统，用于评估中风康复者的运动质量，并引入交互式示例解释——从模型训练集中系统性地呈现与测试样本最近邻的案例，以辅助用户理解与上手。通过面向领域专家和医疗专业人员的实验，结果显示：在上手阶段采用交互式示例解释，相比仅提供特征解释，能显著提升医生对AI的合理依赖，使正确决策比例提高，错误决策比例降低。研究也揭示了在人机协作决策中辅助用户上手的新挑战。
原文摘要 · Abstract (English)
A growing research explores the usage of AI explanations on user's decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on `wrong' AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals' onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors' exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users' onboarding with the AI model. To investigate the effect of interactive example-based explanations, we conducted a study with domain experts, health professionals to evaluate their performance and reliance on AI. Our interactive example-based explanations during onboarding assisted health professionals in having a better reliance on AI and making a higher ratio of making `right' decisions and a lower ratio of `wrong' decisions than providing only feature-based explanations during the decision-support phase. Our study discusses new challenges of assisting user's onboarding with AI for human-AI collaborative decision-making.
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