Better User Interfaces for Occasionally Failing Systems

技术越来越多地使用不可靠的系统作为解释输入的中心手段。常见的例子包括移动设备中的语音和文本输入，以及汽车中的车道偏离检测。这种依赖暴露了一个人们通常不了解底层系统的根本问题，看起来很小的系统或人为错误可能会导致潜在的灾难性后果。虽然技术改进部分解决了这一问题，但拟议的研究计划通过更好地理解、理论、人类与不可靠系统交互的模型以及不可靠系统的新用户界面(UI)技术，追求一种补充方法。*拟议计划的第一部分分析了偶尔出现故障的系统周围的人类行为。我以前曾研究过这种行为，在这里我将把我的实验工作扩展到其他领域，例如语音识别。此外，该项目将通过众包研究收集更大的数据集，并分析它们的行为模式。之前，我在两个任务领域中发现了由于挫折而导致的行为变化，通常可靠性约为95%。这种变化和人类对失败的适应将针对任务域、任务关键程度、用户和上下文的不同组合进行系统研究。*基于所获得的见解，第二个目标着眼于新的用户界面机制，以减轻不可靠的自动化的影响，例如，通过避免重复的错误预测，减少用户的挫败感。其他工作将研究新的解决方案，其通过寻找具有低预测置信度和高校正的实例来自动识别故障，例如在眼睛跟踪中。然后将鼓励用户切换到不同的用户界面模式或应用其他修复程序。另一个主题将调查传感技术，例如EEG，是否可以通过提供不同的预测/反应来触发更好的系统对故障的响应。*补充第二个目标，第三个重点是通知技术改进。用户更正表示有关底层技术运行不够好的事件的有价值的信息。我将设计不同的方法，以适当的匿名方式向系统开发人员传达这些实例和结果的用户更正。*建议的研究的原创性来自于它为不完美的系统迭代开发UI的综合方法，包括减少或防止故障再次发生的新机制。收集到的数据将使我们能够更好地了解人类对不完美的系统所做的权衡，并指导理论、模型和系统的开发。预计这一结果将改善每天有数百万人使用的许多应用程序的用户界面。因此，即使是看似很小的改进也会产生重大影响。这项工作将共同产生更好的用户界面，更好地理解人类围绕不完美系统的行为，并为其提供理论，同时为系统改进提供信息。

Technology increasingly employs unreliable systems as a central means to interpret input. Common examples include voice and text input in mobile devices, and lane departure detection in cars. This reliance exposes a fundamental problem people do not generally understand the underlying systems, and seemingly small system or human errors can lead to potentially disastrous consequences. While technical improvements partially address this, the proposed research program pursues a complementary approach through better understanding, theories, models of human interaction with, and new user interface (UI) technologies for, unreliable systems.***The first part of the proposed program analyzes human behaviours around occasionally failing systems. I have previously studied such behaviours and will here expand my experimental work to other domains, e.g., speech recognition. Additionally, the program will gather larger data sets with crowd-sourced studies and analyze them for behavioural patterns. Previously, I identified a behavioural change due to frustration, typically around 95% reliability, in two task domains. This change and human adaptation to failures will be studied systematically for different combinations of task domain, task criticality, user, and context.***Building on insights gained, the second objective looks at new UI mechanisms to alleviate the effects of unreliable automation, e.g., by avoiding repeated wrong predictions, reducing user frustration. Other work will investigate novel solutions that automatically identify failures by looking for instances with both low prediction confidence and high correction, e.g., in eye tracking. Users will then be encouraged to switch to a different UI modality or apply other fixes. Another thread will investigate if sensing technologies, e.g., EEG, can trigger better system responses to failures, by offering different predictions/reactions.***Complementing the second objective, the third one focuses on informing technical improvements. User corrections express valuable information about incidents where the underlying technologies are not working well enough. I will devise different ways to communicate such instances and resultant user corrections to system developers in appropriately anonymized ways.***The originality of the proposed research stems from its integrative approach for iteratively developing UIs for imperfect systems, including new mechanisms to reduce or prevent failure reoccurrence. The collected data will enable us to better understand the trade-offs humans make around imperfect systems, and guide theory, model and system development. The results are expected to improve UIs for many applications, used by millions of people every day. Thus, even seemingly small improvements will have significant impact. Together the work will yield better UIs and a better understanding of, and theories for, human behaviours around imperfect systems, while simultaneously informing system improvement.**