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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716029
• 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。