Assessing Predictive Models for Fairness Based on Movement Patterns

Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated with certain geographical locations. Literature on this topic makes the fundamental assumption that each individual is assigned to a single geographical location (e.g., place of residence). However, fairness with respect to the set of locations where one has been, i.e., their movement patterns over different regions, also matters when fairness is considered. Consequently, we argue that it is necessary to generalize the notion of spatial fairness to also include movement patterns, leading to the novel problem of assessing predictive models for fairness relative to the movements of individuals. To deal with this problem, we propose an approach that first associates the movements of individuals to certain geographic regions, considering multiple spatial partitions with different resolutions and alignments, and then employs a suitable spatial scan statistic to assess whether a predictive model is fair based on movement patterns. In the experimental evaluation, we study the performance of our approach over thousands of synthetic unfair datasets, showing that it is effective at detecting this new type of unfairness and at retrieving the set of objects treated unfairly, while localization performance exhibits a consistent multi-resolution trade-off.

Urban Region Embeddings from Service-Specific Mobile Traffic Data

With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present a methodology for creating urban region embeddings from service-specific mobile traffic data, employing a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to capture key urban features. In the extensive experimental evaluation conducted using a real-world dataset, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. Specifically, our embeddings are compared against those of a state-of-the-art competitor across two downstream tasks. Additionally, through clustering techniques, we investigate how well the embeddings produced by our methodology capture the temporal dynamics and characteristics of the underlying urban regions. Overall, this work highlights the potential of service-specific mobile traffic data for urban research and emphasizes the importance of making such data accessible to support public innovation.