FAI: Advancing Deep Learning Towards Spatial Fairness

FAI：推进深度学习迈向空间公平

• 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; FAI; Advancing; Deep; Learning; Towards

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.

空间公平的目的是减少与数据样本的位置或地理区域有显着联系的偏见。如果无人看管，这种偏见可能会导致或加剧不公平的资源分配，社会划分，空间差异以及弹性或可持续性的弱点。在各种各样的现实问题（例如农业监测和灾难管理）中使用人工智能迫切需要空间公平。农产品（包括农作物地图和面积估算值）用于为重要决策提供信息，例如分配补贴和提供农场保险。空间偏见产生的不准确性和不平等会对这些决定产生不利影响。同样，对自然灾害（例如洪水或火灾）的有效和公平的制图对于为挽救行动和量化公共基础设施的损害和风险而言至关重要，这与保险估算有关。机器学习，尤其是深度学习，已被广泛用于空间数据集中，结果令人鼓舞。 但是，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的成功有限。该项目的目的是开发新一代的学习框架，以明确保留空间公平。结果和代码将免费提供并集成到现有的地理空间软件中。该方法还将进行测试以掺入现有的实际系统（作物和水监测）中。该项目旨在通过四项创新来推进空间公平的深度学习方法。首先，将研究新的空间公平统计表述，以解决连续空间领域带来的独特挑战，尤其是由于各种方法来分配空间并创建位置组以进行公平评估，以及统计结论对太空分区的变化敏感的事实。其次，将开发新的网络体系结构，以改善空间公平性，通过减轻由于数据分布向空间的转移而减轻不同位置之间的冲突。第三，新的公平驱动的对抗性学习策略将用于指导培训以融合到可以保持高整体解决方案质量的参数，同时最大程度地提高整个位置的空间公平性。最后，将提出一种知识增强的方法，该方法将一般的身体关系整合到减轻数据质量的空间偏见，并模拟基本过程中的相关变量和参数，以增强基于知识的可解释能力的空间公平性，这反映了NSF的法定任务和审查的范围。