Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction

TL;DR

This paper audits large-scale mobility prediction models for demographic disparities, revealing hidden biases, and proposes a fairness-guided sampling method that reduces group inequity by up to 40% with minimal accuracy loss.

Contribution

It introduces FGIS, a group-aware incremental sampling strategy using SAKM to generate proxy demographic labels, effectively reducing bias in mobility prediction models.

Findings

Disparities in model accuracy are linked to dataset biases.

FGIS reduces demographic performance gaps by up to 40%.

Fairness improvements are most notable in early sampling stages.

Abstract

Next location prediction underpins a growing number of mobility, retail, and public-health applications, yet its societal impacts remain largely unexplored. In this paper, we audit state-of-the-art mobility prediction models trained on a large-scale dataset, highlighting hidden disparities based on user demographics. Drawing from aggregate census data, we compute the difference in predictive performance on racial and ethnic user groups and show a systematic disparity resulting from the underlying dataset, resulting in large differences in accuracy based on location and user groups. To address this, we propose Fairness-Guided Incremental Sampling (FGIS), a group-aware sampling strategy designed for incremental data collection settings. Because individual-level demographic labels are unavailable, we introduce Size-Aware K-Means (SAKM), a clustering method that partitions users in latent mobility space while enforcing census-derived group proportions. This yields proxy racial labels for the four largest groups in the state: Asian, Black, Hispanic, and White. Built on these labels, our sampling algorithm prioritizes users based on expected performance gains and current group representation. This method incrementally constructs training datasets that reduce demographic performance gaps while preserving overall accuracy. Our method reduces total disparity between groups by up to 40\% with minimal accuracy trade-offs, as evaluated on a state-of-art MetaPath2Vec model and a transformer-encoder model. Improvements are most significant in early sampling stages, highlighting the potential for fairness-aware strategies to deliver meaningful gains even in low-resource settings. Our findings expose structural inequities in mobility prediction pipelines and demonstrate how lightweight, data-centric interventions can improve fairness with little added complexity, especially for low-data applications.