FAI: Towards Holistic Bias Mitigation in Computer Vision Systems

FAI：迈向计算机视觉系统中的整体偏差缓解

基本信息

批准号：
2041009
负责人：
Nuno Vasconcelos
金额：
$ 37.5万
依托单位：
University of California-San Diego
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-02-01 至 2024-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041009&HistoricalAwards=false
关键词：
FAI Towards Holistic Bias Mitigation

项目摘要

With the increasing use of artificial intelligence (AI) systems in life-changing decisions, such as hiring or firing of individuals or the length of jail sentences, there has been an increasing concern about the fairness of these systems. There is a need to guarantee that AI systems are not biased against segments of the population. This project aims to mitigate AI bias in the domain of computer vision, a driving application for much of the recent advances in a popular form of AI known as deep learning. Computer vision systems are increasingly prevalent in areas of society ranging from healthcare to law enforcement: from apps that analyze skin pictures for melanoma detection to face recognition systems used in criminal investigations. These systems are subject to three major sources of bias: biased data, biased annotations, and biased models. Biased data follows from poor image collection practices, typically the under-representation of certain population groups. Biased annotation follows from the use of annotation platforms with untrained image labelers, who tend to produce annotations that reflect their own image interpretations, rather than objective labels. Biased models can ensue from either the existence of data or annotation biases on the datasets used to train the models, or the choice of biased model architectures. The three bias components have received different attention in the literature, with most previous work focusing on the mitigation of model bias. However, this usually boils down to downplaying groups for which there is a lot of data and promoting groups for which data is scarce. This practice can hurt overall system performance. The remaining sources of bias, datasets and annotation, have received very little algorithmic attention. The project aims to overcome this problem, by introducing a new framework to jointly address the three sources of bias within one unified bias mitigation architecture. This architecture aims to train fair classifiers by iterative optimization of three distinct modules: 1) Dataset bias mitigation algorithms that identify and downweigh biased examples and seek additional examples in a large pool of data to counterbalance the associated biases. 2) Label bias mitigation systems based on machine teaching algorithms that establish clear, replicable, and auditable procedures to teach annotators how to label images without label bias. 3) Model auditing techniques based on counterfactual visual explanations that enable the visualization of the factors contributing to model decisions and why they are biased. The three modules combine into an architecture for joint dataset, label, and model bias mitigation by iterative optimization of datasets, annotators, and models to minimize bias. The project will generate software for dataset bias mitigation, unbiased annotator training, explanations and visualizations, model auditing, and fair model training, which will be made available from the investigator website. This will be complemented with datasets for the design of various form of bias mitigation algorithms, and tools to help practitioners detect and combat bias. Several activities are also planned to broaden the participation of underrepresented K-12 and undergraduate students in the STEM field. They will include the participation of a team of such students, recruited from University of California San Diego programs that aim to increase the participation of these groups in STEM, and aim to provide these students with early exposure to the challenges of real-world engineering, fair machine learning, and deep learning systems.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.

随着人工智能（AI）系统在改变生活的决定中的越来越多，例如雇用或射击个人或监狱刑期，人们对这些系统的公平性越来越担心。有必要确保AI系统不会偏向人群的细分市场。该项目旨在减轻计算机视觉领域的AI偏差，这是一种以流行形式的AI形式的驱动应用程序，称为深度学习。从医疗保健到执法部门，计算机视觉系统越来越普遍：从分析皮肤图片的黑色素瘤检测的应用到面对刑事调查中使用的识别系统。这些系统受到三个主要偏见来源：偏见数据，偏见注释和有偏见的模型。有偏见的数据是从不良的图像收集实践中，通常是某些人群群体的代表性不足。有偏见的注释来自使用带有未经训练的图像标签的注释平台，这些平台倾向于产生反映自己的图像解释而不是客观标签的注释。可以从用于训练模型的数据集上的数据或注释偏差或选择有偏见的模型体系结构上的偏见模型。这三个偏见组成部分在文献中受到了不同的关注，大多数先前的工作都集中在减轻模型偏差上。但是，这通常归结为低调的群体，这些群体有很多数据和促进数据稀缺的群体。这种做法会损害整体系统性能。其余的偏见，数据集和注释的来源很少受到算法的关注。该项目旨在通过引入一个新框架来克服这个问题，以共同解决一种统一偏见缓解架构中的三个偏见来源。该体系结构旨在通过迭代的三个不同模块的迭代优化来训练公平的分类器：1）数据集偏置缓解算法，这些算法识别和降低了偏见的示例，并在大量数据中寻求其他示例以抵消相关偏见。 2）基于机器教学算法的标签偏差缓解系统，这些算法建立了清晰，可复制和可审计的程序，以教导注释者如何在没有标签偏差的情况下标记图像。 3）基于反事实视觉解释的模型审核技术，使这些因素能够可视化导致模型决策及其偏见的因素。这三个模块通过对数据集，注释器和模型的迭代优化来最大程度地减少偏差的迭代优化，将连接数据集，标签和模型偏差缓解的体系结合在一起。该项目将生成用于缓解数据集偏置，公正的注释培训，解释和可视化，模型审核和公平模型培训的软件，这些软件将从研究者网站提供。这将与数据集进行补充，以设计各种形式的偏置缓解算法，以及帮助从业者检测和打击偏见的工具。还计划开展几项活动，以扩大代表性不足的K-12和本科生在STEM领域的参与。它们将包括从加州大学圣地亚哥分校招募的一组学生的参与，旨在增加这些小组的STEM参与，并旨在使这些学生尽早接触现实世界工程，公平的机器学习和深度学习系统的挑战。该奖项奖旨在通过评估NSF的法定任务，反映了对众所周知的Infectia intiftia的支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Towards Professional Level Crowd Annotation of Expert Domain Data

DOI：
10.1109/cvpr52729.2023.00309
发表时间：
2023-06
期刊：
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Pei Wang;N. Vasconcelos
通讯作者：
Pei Wang;N. Vasconcelos

SCOUT: Self-Aware Discriminant Counterfactual Explanations

DOI：
10.1109/cvpr42600.2020.00900
发表时间：
2020-04
期刊：
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Pei Wang;N. Vasconcelos
通讯作者：
Pei Wang;N. Vasconcelos

VALHALLA: Visual Hallucination for Machine Translation

DOI：
10.1109/cvpr52688.2022.00515
发表时间：
2022-05
期刊：
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
0
作者：
Yi Li-;Rameswar Panda;Yoon Kim;Chun-Fu Chen;R. Feris;David Cox;N. Vasconcelos
通讯作者：
Yi Li-;Rameswar Panda;Yoon Kim;Chun-Fu Chen;R. Feris;David Cox;N. Vasconcelos

Toward Unsupervised Realistic Visual Question Answering

走向无监督的现实视觉问答