Sailing in the location-based fairness-bias sphere

As the adoption of machine learning continues to thrive, fairness of the algorithms has become a key factor determining their long-term success and sustainability. Among them, location-based fairness-or spatial fairness-is critical for a variety of essential societal applications that commonly rely on spatial data, including agriculture, disaster response, urban planning, etc. Spatial biases incurred by learning, if left unattended, may cause or exacerbate unfair distribution of resources, spatial disparity, social division, etc. However, very limited understanding has been developed on location-based fairness and bias in machine learning. Compared to traditional fairness-preserving techniques, the spatial consideration introduces two major layers of complication: (1) Space is continuous with no well-defined categories (e.g., categories by race or gender); and (2) Categorizations given by space-partitionings are known to be subject to high statistical sensitivity (e.g., gerrymandering). Under these challenges, we formally explore and demonstrate the fragility of learning methods in the spatial fairness-bias sphere. Specifically, we present a set of techniques that can maneuver the training process towards various targeted fairness-bias outcomes, while maintaining the same level of overall prediction performance (i.e., for "free"). Extensive experiments are carried out on two real-world problems: crop monitoring in the US and palm oil plantation mapping in Indonesia. The results demonstrate the effectiveness of the manipulation algorithms and the importance of explicitly regulating location-based fairness using a diverse set of criteria.