FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction

TL;DR

FairDRL-ST introduces an unsupervised disentangled representation learning framework to improve fairness in spatio-temporal mobility prediction, reducing bias without significantly sacrificing accuracy.

Contribution

It presents a novel unsupervised approach using adversarial and disentangled learning to enhance fairness in mobility forecasting, outperforming existing supervised fairness methods.

Findings

Reduces fairness gaps in mobility demand prediction

Maintains competitive accuracy compared to state-of-the-art methods

Demonstrates effectiveness on real-world urban datasets

Abstract

As deep spatio-temporal neural networks are increasingly utilised in urban computing contexts, the deployment of such methods can have a direct impact on users of critical urban infrastructure, such as public transport, emergency services, and traffic management systems. While many spatio-temporal methods focus on improving accuracy, fairness has recently gained attention due to growing evidence that biased predictions in spatio-temporal applications can disproportionately disadvantage certain demographic or geographic groups, thereby reinforcing existing socioeconomic inequalities and undermining the ethical deployment of AI in public services. In this paper, we propose a novel framework, FairDRL-ST, based on disentangled representation learning, to address fairness concerns in spatio-temporal prediction, with a particular focus on mobility demand forecasting. By leveraging adversarial learning and disentangled representation learning, our framework learns to separate attributes that contain sensitive information. Unlike existing methods that enforce fairness through supervised learning, which may lead to overcompensation and degraded performance, our framework achieves fairness in an unsupervised manner with minimal performance loss. We apply our framework to real-world urban mobility datasets and demonstrate its ability to close fairness gaps while delivering competitive predictive performance compared to state-of-the-art fairness-aware methods.