FAI: Measuring and Mitigating Biases in Generic Image Representations

视觉识别是人类大脑执行的一项非凡任务。为了模拟这种能力而训练的计算方法依赖于观察数百万个与人类注释配对的视觉输入示例。这些计算方法已经取得了很大的进步，并越来越多地被采用在许多面向用户的应用程序，如图像搜索，自动图像标记，半自主导航系统，智能虚拟助手等，然而，在这些系统中的底层视觉识别模型往往会产生错误的社会意义的敏感变量与他们的预测。该项目的目标是以系统的方式测量和减轻此类错误。例如，如果一种方法能够识别“教室”等场景的图像，则该项目的目标是确保基于诸如白板、椅子、桌子和空间作为教室通常所需的其他元素的存在等线索而不是基于诸如教室中存在的人的特征或属性等偶然元素来获得此类预测。为此，本项目旨在更容易地确定计算视觉识别的方法在多大程度上依赖于与偶然元素的虚假关联。本项目将提供一个研究的社会偏见，目前的方法和模型计算视觉识别，被广泛用作通用的视觉表示的来源。开发的方法将基于从机器学习，计算机视觉和软件测试社区中获得的坚实基础。该项目引入了关联测试来探测在各种条件下训练的模型，以系统地解开在通用视觉表征学习过程中引入的偏见。该项目将1）开发一种通用的评估方法来测量通用视觉表征学习中的各种类型的偏见，2）提出减少现有通用视觉表征提取模型中这些偏见的影响的方法，以及3）测量这些偏见对一些关键下游任务的影响。这三个研究目标将辅之以一个全面的评价计划和扩大参与活动。该研究成果将为计算机视觉模型和方法的预测偏差来源带来新的见解，从而对此类模型的部署风险做出明智的决策。该奖项反映了NSF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。

Visual recognition is a remarkable task performed by the human brain. Computational methods trained to emulate this capability rely on observing millions of examples of visual input paired with human annotations. These computational methods have made great progress and are being increasingly adopted in many user-facing applications such as image search, automated image tagging, semi-autonomous navigation systems, smart virtual assistants, etc. However, the underlying visual recognition models in these systems often produce errors by associating sensitive variables of societal significance with their predictions. The goal of this project is to measure and mitigate such errors in a systematic fashion. For example, if a method is able to recognize images of scenes such as 'classroom', the goal of this project is to ensure that such predictions are obtained based on cues such as the presence of a whiteboard, chairs, desks, and other elements typically needed for a space to function as a classroom and not based on incidental elements such as the characteristics or attributes of people present in the classroom. To this end, this project aims to make it easier to determine to what extent methods for computational visual recognition rely on spurious associations with incidental elements.This project will provide a study of societal biases present in current methods and models for computational visual recognition that are widely used as a source of generic visual representations. The developed methods will be based on solid foundations drawn from both the machine learning, computer vision, and software testing communities. The project introduces association tests to probe models trained under a variety of conditions to systematically disentangle the biases introduced during generic visual representation learning. The project will be 1) developing a general assessment methodology to measure various types of biases in generic visual representation learning, 2) proposing methods to diminish the impact of these biases in existing generic visual representation extraction models, and 3) measuring the impact of these biases on some key downstream tasks. These three research aims will be complemented by a comprehensive evaluation plan and broadening participation activities. This research effort will bring novel insights into the sources of biases in the predictions of computer vision models and methodologies to make informed decisions about the risks in the deployment of such models.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.