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

HCC-Medium：机器学习程序的最终用户调试

• 批准号: 0803487
• 负责人: Margaret Burnett
• 金额: $ 61.82万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803487&HistoricalAwards=false
• 关键词: HCC; Medium; End; user; debugging

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.

这是一个项目，可以使最终用户有一些能力调试由机器而不是人编写的程序，尤其是当用户不是专家程序员时。 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问题。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表现。这套学习的行为集是一个程序。在学习环境留下了机器学习专家的手之前，学到的程序才出现，因为它们从用户正在进行的数据中学习。因此，如果这些程序犯了一个错误，则唯一一个审议的程序是用户。 使最终用户能够调试此类程序的能力可以提高这些系统的速度和准确性。特别是，该项目设想了一个细粒度，迭代，交互式调试过程。首先，用户会注意到错误的分类（基于系统的帮助，基于其自身能力的推理），例如可能会被误解的电子邮件。其次，用户要求解释。第三，使用系统的解释，用户提供了推理限制，例如声明“今日”不是一个重要的词，并且公司总裁的任何内容都应该进入“公司”文件夹。学识渊博的程序重新评估了能力模型并重做其推理，使用户有机会立即查看变更的结果。然后循环再次开始。因此，该项目的目标是：1。帮助用户确定推理问题，并提供适合最终用户的机器学习程序的行为的解释。 2。为了引起用户的丰富反馈，将其纳入了学习计划的推理中。 3。为了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。除了在机器学习者的潜在速度和准确性提高的情况下，用户可能会变得更加生产力并造成更少的错误。提供学识渊博的计划的推理产生信任的披露，并提高使用该系统的意愿。 因此，该项目有可能在各种新的，现实世界中的应用程序中对用户接受机器学习的重大进展。 将人类约束和指导与统计学习结合起来可以从小型数据集中获得高度准确的学习，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，其数据源和输入功能易于更改并且其行为易于控制。 在将人类计算机的互动原理与机器学习相结合时，该项目为新颖的观点打开了机会，尤其是在跨学科教育领域。 研究生将接受该混合研究领域的培训，其各个方面将在人类计算机互动和机器学习以及其他针对本科生和高中生的教育经验中纳入课程。