Hirsch Tad, Merced Kritzia, Narayanan Shrikanth, Imel Zac E, Atkins David C
University of Washington, Seattle, USA,
University of Utah, Salt Lake City, USA,
DIS (Des Interact Syst Conf). 2017 Jun;2017:95-99. doi: 10.1145/3064663.3064703.
We describe the design of an automated assessment and training tool for psychotherapists to illustrate challenges with creating interactive machine learning (ML) systems, particularly in contexts where human life, livelihood, and wellbeing are at stake. We explore how existing theories of interaction design and machine learning apply to the psychotherapy context, and identify "contestability" as a new principle for designing systems that evaluate human behavior. Finally, we offer several strategies for making ML systems more accountable to human actors.
我们描述了一种面向心理治疗师的自动化评估与训练工具的设计，以说明创建交互式机器学习（ML）系统所面临的挑战，尤其是在人类生命、生计和幸福受到威胁的情况下。我们探讨了现有的交互设计和机器学习理论如何应用于心理治疗背景，并将“可争议性”确定为设计评估人类行为系统的一项新原则。最后，我们提供了几种使机器学习系统对人类行为者更具可问责性的策略。
