Sixth：理解和解决机器学习在高风险决策中的可用性挑战
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
摘要：
机器学习（ML）正被应用于一系列多样化且不断增长的领域。在许多情况下，领域专家-通常在ML或数据科学方面没有专业知识-被要求使用ML预测来做出高风险决策。结果可能会出现多种ML可用性挑战，例如用户对模型缺乏信任，无法协调人类与ML的分歧，以及对将复杂问题过度简化为单一算法输出的道德担忧。在本文中，我们通过与儿童福利筛查人员的一系列合作，调查了儿童福利筛查领域存在的ML可用性挑战。在机器学习科学家、可视化研究人员和领域专家（儿童筛选器）之间的迭代设计过程之后，我们首先确定了四个关键的机器学习挑战，并在一个有前途的可解释的机器学习技术上进行了磨练，以解决这些问题（本地因素贡献）。然后，我们实现并评估了我们的可视化分析工具Sirocket，以提高本地因素贡献的可解释性和交互性。我们的工具的有效性证明了两个正式的用户研究与12名非专家参与者和13名专家参与者分别。我们收集了有价值的反馈，从中我们组成了一个设计影响列表，作为研究人员的有用指南，这些研究人员旨在为儿童福利筛查人员和其他类似领域专家部署的ML预测模型开发可解释和交互式可视化工具。
英文摘要：
Machine learning (ML) is being applied to a diverse and ever-growing set of domains. In many cases, domain experts - who often have no expertise in ML or data science - are asked to use ML predictions to make high-stakes decisions. Multiple ML usability challenges can appear as result, such as lack of user trust in the model, inability to reconcile human-ML disagreement, and ethical concerns about oversimplification of complex problems to a single algorithm output. In this paper, we investigate the ML usability challenges that present in the domain of child welfare screening through a series of collaborations with child welfare screeners. Following the iterative design process between the ML scientists, visualization researchers, and domain experts (child screeners), we first identified four key ML challenges and honed in on one promising explainable ML technique to address them (local factor contributions). Then we implemented and evaluated our visual analytics tool, Sibyl, to increase the interpretability and interactivity of local factor contributions. The effectiveness of our tool is demonstrated by two formal user studies with 12 non-expert participants and 13 expert participants respectively. Valuable feedback was collected, from which we composed a list of design implications as a useful guideline for researchers who aim to develop an interpretable and interactive visualization tool for ML prediction models deployed for child welfare screeners and other similar domain experts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
