When dealing with data from distinct locations, machine learning algorithms tend to demonstrate an implicit preference of some locations over the others, which constitutes biases that sabotage the spatial fairness of the algorithm. This unfairness can easily introduce biases in subsequent decision-making given broad adoptions of learning-based solutions in practice. However, locational biases in AI are largely understudied. To mitigate biases over locations, we propose a locational meta-referee (Meta-Ref) to oversee the few-shot meta-training and meta-testing of a deep neural network. Meta-Ref dynamically adjusts the learning rates for training samples of given locations to advocate a fair performance across locations, through an explicit consideration of locational biases and the characteristics of input data. We present a three-phase training framework to learn both a meta-learning-based predictor and an integrated Meta-Ref that governs the fairness of the model. Once trained with a distribution of spatial tasks, Meta-Ref is applied to samples from new spatial tasks (i.e., regions outside the training area) to promote fairness during the fine-tune step. We carried out experiments with two case studies on crop monitoring and transportation safety, which show Meta-Ref can improve locational fairness while keeping the overall prediction quality at a similar level.

翻译：处理来自不同位置的数据时，机器学习算法往往表现出对某些位置相对于其他位置的隐性偏好，这种偏差破坏了算法的空间公平性。由于基于学习的解决方案在实践中被广泛采用，这种不公平性很容易在后续决策中引入偏见。然而，人工智能中的位置偏差在很大程度上尚未得到充分研究。为了减轻位置偏差，我们提出了一种位置元裁判（Meta-Ref），用于监督深度神经网络的少样本元训练和元测试。通过明确考虑位置偏差和输入数据的特征，Meta-Ref动态调整给定位置训练样本的学习率，以促进跨位置的公平性能。我们提出了一个三阶段训练框架，用于同时学习基于元学习的预测器和控制模型公平性的集成Meta-Ref。一旦通过空间任务的分布进行训练，Meta-Ref便应用于来自新空间任务（即训练区域之外的区域）的样本，以在微调步骤中促进公平性。我们通过两个案例研究（作物监测和交通安全）进行了实验，结果表明，Meta-Ref能够在保持整体预测质量大致相同的同时，提高位置公平性。