FAI: Advancing Deep Learning Towards Spatial Fairness

FAI：推进深度学习迈向空间公平

基本信息

批准号：
2147195
负责人：
Xiaowei Jia
金额：
$ 75.51万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147195&HistoricalAwards=false
关键词：
FAI Advancing Deep Learning Towards

项目摘要

The goal of spatial fairness is to reduce biases that have significant linkage to the locations or geographical areas of data samples. Such biases, if left unattended, can cause or exacerbate unfair distribution of resources, social division, spatial disparity, and weaknesses in resilience or sustainability. Spatial fairness is urgently needed for the use of artificial intelligence in a large variety of real-world problems such as agricultural monitoring and disaster management. Agricultural products, including crop maps and acreage estimates, are used to inform important decisions such as the distribution of subsidies and providing farm insurance. Inaccuracies and inequities produced by spatial biases adversely affect these decisions. Similarly, effective and fair mapping of natural disasters such as floods or fires is critical to inform live-saving actions and quantify damages and risks to public infrastructures, which is related to insurance estimation. Machine learning, in particular deep learning, has been widely adopted for spatial datasets with promising results. However, straightforward applications of machine learning have found limited success in preserving spatial fairness due to the variation of data distribution, data quantity, and data quality. The goal of this project is to develop a new generation of learning frameworks to explicitly preserve spatial fairness. The results and code will be made freely available and integrated into existing geospatial software. The methods will also be tested for incorporation in existing real systems (crop and water monitoring). This project aims to advance deep learning methods toward spatial fairness via four innovations. First, new statistical formulations of spatial fairness will be investigated to address unique challenges brought by the continuous spatial domain, particularly due to a variety of ways to partition the space and create location-groups for fairness evaluation, and the fact that statistical conclusions are sensitive to changes in space-partitionings. Second, new network architectures will be developed to improve the spatial fairness by mitigating the conflicts amongst different locations due to the shift of data distribution over space. Third, new fairness-driven adversarial learning strategies will be used to guide the training to converge to parameters that can maintain a high overall solution quality while maximizing spatial fairness across locations. Finally, a knowledge-enhanced approach will be proposed, which integrates general physical relationships to mitigate data-inequality incurred spatial biases, and simulates relevant variables and parameters in underlying physical processes to enhance knowledge-based interpretability of spatial fairness.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.

空间公平的目标是减少与数据样本的位置或地理区域有显着联系的偏差。如果不加注意，这种偏见可能会导致或加剧资源分配不公平、社会分化、空间差异以及复原力或可持续性方面的弱点。人工智能在农业监测和灾害管理等各种现实问题中的应用迫切需要空间公平。农产品，包括农作物地图和面积估算，用于为重要决策提供信息，例如补贴分配和提供农业保险。空间偏差造成的不准确和不公平会对这些决策产生不利影响。同样，有效和公平地绘制洪水或火灾等自然灾害地图对于为救生行动提供信息并量化公共基础设施的损害和风险至关重要，这与保险估算有关。机器学习，特别是深度学习，已广泛应用于空间数据集，并取得了可喜的结果。然而，由于数据分布、数据数量和数据质量的变化，机器学习的直接应用在保持空间公平性方面取得的成功有限。该项目的目标是开发新一代学习框架，以明确维护空间公平性。结果和代码将免费提供并集成到现有的地理空间软件中。这些方法还将进行测试，以便将其纳入现有的实际系统（作物和水监测）。该项目旨在通过四项创新推动深度学习方法实现空间公平。首先，将研究空间公平性的新统计公式，以解决连续空间域带来的独特挑战，特别是由于划分空间和创建用于公平性评估的位置组的多种方式，以及统计结论敏感的事实空间分区的变化。其次，将开发新的网络架构，通过减轻由于数据分布在空间上的转移而导致的不同位置之间的冲突来提高空间公平性。第三，新的公平驱动的对抗性学习策略将用于指导训练收敛到可以保持较高整体解决方案质量的参数，同时最大化跨位置的空间公平性。最后，将提出一种知识增强方法，该方法集成一般物理关系，以减轻数据不平等引起的空间偏差，并模拟底层物理过程中的相关变量和参数，以增强基于知识的空间公平性的可解释性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sailing in the location-based fairness-bias sphere

在基于位置的公平偏见领域航行

DOI：
10.1145/3557915.3560976
发表时间：
2022
期刊：
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
He, Erhu;Xie, Yiqun;Jia, Xiaowei;Chen, Weiye;Bao, Han;Zhou, Xun;Jiang, Zhe;Ghosh, Rahul;Ravirathinam, Praveen
通讯作者：
Ravirathinam, Praveen

Meta-Transfer Learning: An application to Streamflow modeling in River-streams

DOI：
10.1109/icdm54844.2022.00026
发表时间：
2022-11
期刊：
2022 IEEE International Conference on Data Mining (ICDM)
影响因子：
0
作者：
Rahul Ghosh;Bangyan Li;Kshitij Tayal;Vipin Kumar;X. Jia
通讯作者：
Rahul Ghosh;Bangyan Li;Kshitij Tayal;Vipin Kumar;X. Jia

Physics-guided machine learning from simulated data with different physical parameters

DOI：
10.1007/s10115-023-01864-z
发表时间：
2023-03-31
期刊：
KNOWLEDGE AND INFORMATION SYSTEMS
影响因子：
2.7
作者：
Chen, Shengyu;Kalanat, Nasrin;Jia, Xiaowei
通讯作者：
Jia, Xiaowei

Deep semantic segmentation for building detection using knowledge-informed features from LiDAR point clouds

使用 LiDAR 点云的知识信息特征进行建筑物检测的深度语义分割

DOI：
10.1145/3557915.3565985
发表时间：
2022
期刊：
SIGSPATIAL '22: Proceedings of the 30th International Conference on Advances in Geographic Information Systems
影响因子：
0
作者：
Chen, Weiye;Wang, Zhihao;Li, Zhili;Xie, Yiqun;Jia, Xiaowei;Li, Anlin
通讯作者：
Li, Anlin

Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks

DOI：
10.1145/3534678.3539115
发表时间：
2022-08
期刊：
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Shengyu Chen;Jacob Aaron Zwart;X. Jia
通讯作者：
Shengyu Chen;Jacob Aaron Zwart;X. Jia