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

arXiv: https://arxiv.org/abs/2510.26940 · HTML: https://mlsanity.com/paper/2510.26940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Adaptive Sampling for Minimax Fair Classification](https://mlsanity.com/paper/2103.00755/) (2021)
- [FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems](https://mlsanity.com/paper/1907.03827/) (2019)
- [How do you go where? Improving next location prediction by learning travel mode information using transformers](https://mlsanity.com/paper/2210.04095/) (2022)
- [Fairness Without Harm: An Influence-Guided Active Sampling Approach](https://mlsanity.com/paper/2402.12789/) (2024)
- [Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models](https://mlsanity.com/paper/2109.12422/) (2021)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [Travel Demand Forecasting: A Fair AI Approach](https://mlsanity.com/paper/2303.01692/) (2023)
- [Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization](https://mlsanity.com/paper/1705.07837/) (2017)
- [MobilityDL: A Review of Deep Learning From Trajectory Data](https://mlsanity.com/paper/2402.00732/) (2024)
- [Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems](https://mlsanity.com/paper/2303.14332/) (2023)

## Abstract (arXiv)

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.