Better User Interfaces for Occasionally Failing Systems

为偶尔出现故障的系统提供更好的用户界面

• 批准号: RGPIN-2019-07213
• 负责人: Stuerzlinger, Wolfgang
• 金额: $ 4.01万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690517
• 关键词: Better; User; Interfaces; Occasionally; Failing

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.**

技术越来越多地利用不可靠的系统来解释输入。常见的示例包括移动设备中的语音和文本输入，以及汽车中的车道出发检测。这种依赖暴露了一个基本问题，人们通常不了解潜在的系统，看似很小的系统或人类错误可能会导致潜在的灾难性后果。尽管技术的改进部分解决了这一问题，但拟议的研究计划通过更好的理解，理论，与不可靠的系统的新用户界面（UI）技术（UI）技术（UI）技术进行补充方法。***拟议程序的第一部分分析偶尔失败系统的人类行为。我以前曾研究过此类行为，并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，该计划将通过人群研究收集较大的数据集，并分析它们的行为模式。以前，我在两个任务域中确定了由于挫败感，通常约为95％的可靠性。这种变化和人类对失败的适应性将被系统地研究任务领域，任务关键性，用户和上下文的不同组合。***基于获得的见解，第二个目标着眼于新的UI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，减少用户挫败感。其他工作将调查新的解决方案，这些解决方案通过寻找低预测置信度和高校正的实例来自动识别失败，例如在眼睛跟踪中。然后，将鼓励用户切换到其他UI模式或应用其他修复程序。另一个线程将调查感应技术（例如脑电图）是否可以通过提供不同的预测/反应来触发更好的系统响应。用户更正表达了有关基础技术工作不够好的事件的有价值信息。我将设计不同的方式，以适当的匿名方式将这些实例传达给系统开发人员的用户更正。收集到的数据将使我们能够更好地理解人类围绕不完美系统的权衡，并指导理论，模型和系统开发。预计结果将改善许多应用程序，每天数以百万计的人使用。因此，即使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UI，并对不完美系统的人类行为有更好的理解和理论，同时为系统改进提供了信息。**********