CRII: CHS: Techniques for Helping Domain Experts Understand and Improve Models Underlying Intelligent Systems

许多计算系统包括智能组件，采用算法或计算模型来处理数据并根据数据做出决策或建议。这些模型通常起到“黑箱”的作用：它们的内部机制对用户来说通常是不可见的，当它们可见时，它们通常以一种需要计算机科学培训才能理解和修改的形式存在。它们构成了许多对社会有用的功能的基础，从风险评分到自动驾驶汽车，这使得它们的用户（通常是特定使用领域的专家，但不是计算方面的专家）能够使用和改进模型变得非常重要。该项目的目标是开发技术，帮助没有受过计算机科学家训练的人类用户使用基于特定领域和手头问题的概念和交互技术（而不是底层算法）与这些模型进行交互。这将导致可解释人工智能这一更广泛领域的科学进步，并提高影响日常生活的智能系统的透明度和实用性。该项目还将支持一些课程开发和推广活动，并开发可供其他研究人员和开发人员使用的免费工具包。该项目的长期目标是通过计算生成可视化，向非计算知识工作者揭示智能系统是如何工作的。为了实现这一目标，项目团队将专注于三个主要的研究活动。首先，他们将对多个研究领域（人工智能、机器学习、计算机科学教育、人机交互和可视化）的文献进行自下而上的四步分析，以总结可视化智能系统的关键设计维度。其次，他们将通过观察医疗专业人员如何与数据交互并理解特定领域的智能系统来收集这些系统目前如何在医疗领域使用和理解的经验证据。第三，基于前两项活动，他们将开发计算方法，通过可视化用户采样数据如何转换为最终结果来说明这些系统是如何工作的，同时为领域专家提供与模型交互的控制。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A number of computing systems include intelligent components, employing algorithms or computational models that process data and make decisions or recommendations based on the data. These models often function as 'black boxes': their internal mechanisms are often not visible to their users, and when they are, they are usually in a form that requires training in computer science to understand and modify them. They underlie many socially useful functions, ranging from risk scoring to autonomous vehicles, which makes it important for their users -- who are often experts in the specific domain of use, but not in computing -- to be able to use and improve the models. This project's goal is to develop techniques that will help human users who are not trained computer scientists interact with these models using concepts and interaction techniques based on the specific domain and problem at hand rather than the underlying algorithms. This will lead to scientific advances in the broader area of explainable artificial intelligence, as well as increasing the transparency and usefulness of intelligent systems that affect everyday life. The project will also support a number of course development and outreach activities and develop freely available toolkits for other researchers and developers to use. The long-term goal of this project is to computationally generate visualizations to reveal how intelligent systems work to non-computing knowledge workers. To progress toward this goal, the project team will focus on three major research activities. First, they will conduct a bottom-up, four-pass analysis of literature spanning multiple research communities (Artificial Intelligence, Machine Learning, Computer Science Education, Human-Computer Interaction, and Visualization) to summarize key design dimensions of visualizing intelligent systems. Second, they will collect empirical evidence of how these systems are currently used and understood in the medical domain by observing how medical professionals interact with data and make sense of domain-specific intelligent systems. Third, based on the first two activities, they will develop computational methods to illustrate how these systems work by visualizing how user-sampled data is transformed into final results, while providing controls for domain experts to interact with the model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.