FAI: Advancing Deep Learning Towards Spatial Fairness

空间公平的目标是减少与数据样本的位置或地理区域有重大联系的偏差。这种偏见如果不加以重视，可能会导致或加剧资源分配不公平、社会分化、空间差距以及复原力或可持续性的弱点。在农业监测和灾害管理等大量现实问题中使用人工智能迫切需要空间公平性。农业产品，包括作物地图和种植面积估计，被用来为重要决策提供信息，如补贴的分配和提供农业保险。空间偏差造成的不准确和不公平对这些决定产生不利影响。同样，有效和公平地绘制洪水或火灾等自然灾害的地图对于为拯救生命行动提供信息以及量化公共基础设施的损失和风险至关重要，这与保险估算有关。机器学习，特别是深度学习，已被广泛用于空间数据集，并取得了可喜的成果。 然而，由于数据分布、数据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，并纳入现有的地理空间软件。还将对这些方法进行测试，以便将其纳入现有的真实的系统（作物和水监测）。该项目旨在通过四项创新来推进深度学习方法的空间公平性。首先，新的统计公式的空间公平性将被调查，以解决独特的挑战所带来的连续空间域，特别是由于各种方式来划分空间，并创建位置组的公平性评估，以及事实上，统计结论是敏感的变化空间分割。第二，将开发新的网络架构，以改善空间公平性，通过减轻由于空间上的数据分布的移动而引起的不同位置之间的冲突。第三，新的公平驱动的对抗学习策略将用于指导训练收敛到可以保持高整体解决方案质量的参数，同时最大限度地提高跨位置的空间公平性。最后，一个知识增强的方法将被提出，它集成了一般的物理关系，以减轻数据不平等引起的空间偏差，并模拟相关的变量和参数，在底层的物理过程，以提高基于知识的解释性的空间fairness.This奖项反映了NSF的法定使命，并已被认为是值得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的支持。

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.