Better User Interfaces for Occasionally Failing Systems

技术越来越多地采用不可靠的系统作为解释输入的主要手段。常见的例子包括移动设备上的语音和文本输入，以及汽车上的车道偏离检测。这种依赖暴露了一个基本问题，人们通常不了解底层系统，看似小的系统或人为错误可能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、人类交互模型和针对不可靠系统的新用户界面（UI）技术，寻求一种互补的方法。***所提出的程序的第一部分分析了偶尔发生故障的系统周围的人类行为。我之前已经研究过这种行为，并将在这里将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，并对其进行行为模式分析。之前，我在两个任务领域中发现了由于挫折而导致的行为改变，通常约为95%的可靠性。这种变化和人类对失败的适应将系统地研究任务域、任务关键性、用户和上下文的不同组合。基于所获得的见解，第二个目标着眼于新的UI机制，以减轻不可靠自动化的影响，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，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，例如在眼动追踪中。然后将鼓励用户切换到不同的UI模式或应用其他修复程序。另一个线程将调查是否传感技术，如脑电图，可以触发更好的系统响应故障，通过提供不同的预测/反应。作为第二个目标的补充，第三个目标侧重于为技术改进提供信息。用户更正表达了有关底层技术不能很好工作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由此产生的用户更正。***提出的研究的独创性源于其为不完善系统迭代开发ui的综合方法，包括减少或防止故障再次发生的新机制。收集的数据将使我们能够更好地理解人类在不完美的系统周围做出的权衡，并指导理论、模型和系统开发。研究结果有望改善许多应用程序的用户界面，这些应用程序每天都有数百万人在使用。因此，即使看似很小的改进也会产生重大影响。总之，这些工作将产生更好的用户界面，更好地理解围绕不完美系统的人类行为和理论，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.**