HCC-Medium: End-user debugging of machine-learned programs

这是一个让最终用户能够调试机器而不是人编写的程序的项目，特别是当用户不是专业程序员时。 这是一种新程序的用户所面临的问题，即由机器学习系统生成的程序。例如，智能用户界面、电子邮件和网站的分类器以及推荐系统使用机器学习来学习如何行为。这一系列习得的行为就是一个程序。在学习环境离开机器学习专家的手之前，学习程序不会存在，因为它们从用户的持续数据中学习。因此，如果这些程序犯了错误，唯一在场调试它们的是用户。 让最终用户能够调试这些程序可以提高这些系统的速度和准确性。具体来说，该项目设想了一个细粒度的，迭代的，交互式的调试过程。首先，用户注意到一个错误的分类（在系统的帮助下，基于对自身能力的推理），例如可能被错误归档的电子邮件。第二，用户要求解释。第三，使用系统的解释，用户提供推理约束，例如，声明“今天”不是一个重要的词，并且来自公司总裁的任何内容都应该进入“公司”文件夹。学习后的程序会重新评估能力模型，并重新进行推理，让用户有机会立即看到变化的结果。然后循环再次开始。因此，本项目的目标如下：1。帮助用户识别推理问题，并提供适合最终用户的机器学习程序的行为解释。2.从用户那里得到丰富的反馈，并将其纳入学习程序的推理中。3.通过将这种丰富的反馈整合到学习中来提高机器学习的速度和准确性。除了机器学习器的潜在速度和准确性提高外，用户可能会变得更有效率，并减少错误。提供学习程序推理的公开会产生信任，并增加使用系统的意愿。 因此，该项目有可能在各种新的现实世界应用中使用户接受机器学习方面取得重大进展。 将人类的约束和指导与统计学习相结合，可以从小数据集中实现高度准确的学习，这对于创建成功的智能用户界面至关重要。该项目还将导致学习系统的数据源和输入功能很容易改变，其行为很容易控制。 通过将人机交互原理与机器学习相结合，该项目为新的视角提供了机会，特别是在跨学科教育领域。 研究生将在这个混合研究领域接受培训，它的各个方面将被纳入人机交互和机器学习课程，以及本科生和高中生的其他教育经验。

This is a project to give the end user some ability to debug programs that were written by a machine instead of a person, especially when the users are not expert programmers. This is the problem faced by users of a new sort of program, namely, one generated by a machine learning system. For example, intelligent user interfaces, categorizers of email and web sites, and recommender systems use machine learning to learn how to behave. This learned set of behaviors is a program. Learned programs do not come into existence until the learning environment has left the hands of the machine learning specialist, because they learn from the user's ongoing data. Thus, if these programs make a mistake, the only one present to debug them is the user. Giving end users the ability to debug such programs can improve the speed and accuracy of these systems.Specifically, the project envisions a fine-grained, iterative, interactive debugging process. First, a user notices an erroneous classification (with the system's help, based on reasoning about its own competence), such as an email message that might be misfiled. Second, the user asks for an explanation. Third, using the system's explanation, the user provides reasoning constraints, declaring, for example, that "today" is not an important word, and that anything from the company president should go into the "company" folder. The learned program reevaluates competence models and redoes its reasoning, giving the user an opportunity to immediately see the result of the change. The loop then begins again. Thus, the goals of this project are the following: 1. To help users identify reasoning problems, and to provide explanations of the behavior of machine-learned programs suitable for end users. 2. To elicit rich feedback from the user, incorporating it into the reasoning of the learned program. 3. To improve the speed and accuracy of machine learning by integrating this rich feedback into learning.In addition to the potential speed and accuracy improvement in the machine learner, users may become more productive and make fewer errors. Providing disclosure of the learned programs' reasoning engenders trust, and with it, increased willingness to use the system. Thus, this project has the potential to make significant advances in the user acceptance of machine learning in a variety of new, real-world applications. Combining human constraints and guidance with statistical learning could enable highly accurate learning from small data sets, which is critical to creating successful intelligent user interfaces. The project will also result in learning systems whose data sources and input features are easy to change and whose behavior is easy to control. In combining human-computer interaction principles with machine learning, this project opens opportunities for novel perspectives, especially in the realm of interdisciplinary education. Graduate students will be trained in this blended research area, and aspects of it will be incorporated in classes in both human-computer interaction and machine learning, and in other educational experiences for undergraduates and high school students.