III: Medium: Visually Interactive Neural Probabilistic Models of Language

机器学习用于自动化日常任务的应用正变得越来越普遍。虽然自动化有可能产生更高的效率和更好的结果，但它可能会导致难以分析和纠正的不可预测的错误。机器学习算法的用户需要对预测和做出的选择做出更好的解释。此外，为了防止危害，用户应该能够干预和控制算法的决策过程。该奖项主要涉及神经语言模型的应用，即使用自然语言交流的机器学习系统。由于这些系统使用文本或语音与用户交互，因此必须避免来自自动化方法的错误信息，并保留人工代理。开发可解释和可控制的人工智能方法将使用户能够与自动化工具协作，获得效率和性能好处，同时防止危害和错误信息。这个项目的目标是开发方法和视觉交互工具，使研究人员能够开发、检查和纠正语言的概率神经模型。共同设计机器学习模型和可视化界面将是迈向可解释模型的必要一步，这些模型适用于常见用例，如语言摘要、翻译和数据到文本应用程序。为了实现这些交互和协作系统，需要开发具有潜在变量的新型概率神经网络模型，这些变量可以充当模型中的“钩子”。这些挂钩对应于模型必须采取的可解释决策，并使最终用户能够覆盖模型决策并与其交互。在第二步，该项目将开发查询和可视化方法，利用这些挂钩，允许用户通过交互式用户反馈来探索、调试和改进真实示例中的神经模型。将使用机器学习、定性和定量用户研究以及对用户参与度的长期纵向观察的量化方法来评估项目进展。该项目将产生一个可扩展的软件框架，用于神经序列模型的可视化交互分析，这将有助于其他研究人员和开发人员在其应用领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The application of machine learning to automate everyday tasks is becoming increasingly common. While automation has the potential of yielding higher efficiency and improved outcomes, it can lead to unpredictable mistakes that can be hard to analyze and correct. Users of machine learning algorithms need better explanations for predictions and choices that are made. Moreover, to prevent harm, a user should be able to intervene in and control the decision process of the algorithm. This award is primarily concerned with applications in neural language models, i.e., machine learning systems that communicate using natural language. Because these systems interact with users using text or speech, it is essential to avoid misinformation from automated approaches and to retain human agency. Developing explainable and controllable artificial intelligence methods will empower users to collaborate with automation tools and gain efficiency and performance benefits while at the same time preventing harm and misinformation. This project targets the development of methods and visually interactive tools that allow researchers to develop, examine, and correct probabilistic neural models of language. Co-designing machine learning models and visual interfaces will be a necessary step towards interpretable models for common use-cases such as language summarization, translation, and data-to-text applications. To achieve these interactive and collaborative systems requires developing novel probabilistic neural network models with latent variables that can act as "hooks" within the model. These hooks correspond to interpretable decisions that a model has to take and that enable end-users to overwrite and interact with model decisions. In a second step, the project will develop query and visualization methods that utilize these hooks to allow users to explore, debug, and improve neural models on real examples through interactive user feedback. The project progress will be evaluated using quantitative methods from machine learning, qualitative and quantitative user studies, and long-term longitudinal observations of user engagement. The project will result in an extensible software framework for visually interactive analysis of neural sequence models that will assist other researchers and developers in their application domains.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.