# Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models

arXiv: https://arxiv.org/abs/2406.04382 · HTML: https://mlsanity.com/paper/2406.04382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems](https://mlsanity.com/paper/1907.03827/) (2019)
- [Mining large-scale human mobility data for long-term crime prediction](https://mlsanity.com/paper/1806.01400/) (2018)
- [Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities](https://mlsanity.com/paper/2004.05822/) (2020)
- [Leveraging Mobility Flows from Location Technology Platforms to Test Crime Pattern Theory in Large Cities](https://mlsanity.com/paper/2004.08263/) (2020)
- [A fairness assessment of mobility-based COVID-19 case prediction models](https://mlsanity.com/paper/2210.03901/) (2022)
- [A Graph-Prediction-Based Approach for Debiasing Underreported Data](https://mlsanity.com/paper/2307.07898/) (2023)

## Built on by (most cited first, 2 of 2)

- [Unmasking Algorithmic Bias in Predictive Policing: A GAN-Based Simulation Framework with Multi-City Temporal Analysis](https://mlsanity.com/paper/2603.18987/) (2026)
- [FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing](https://mlsanity.com/paper/2604.18644/) (2026)

## Abstract (arXiv)

Deep learning crime predictive tools use past crime data and additional behavioral datasets to forecast future crimes. Nevertheless, these tools have been shown to suffer from unfair predictions across minority racial and ethnic groups. Current approaches to address this unfairness generally propose either pre-processing methods that mitigate the bias in the training datasets by applying corrections to crime counts based on domain knowledge or in-processing methods that are implemented as fairness regularizers to optimize for both accuracy and fairness. In this paper, we propose a novel deep learning architecture that combines the power of these two approaches to increase prediction fairness. Our results show that the proposed model improves the fairness of crime predictions when compared to models with in-processing de-biasing approaches and with models without any type of bias correction, albeit at the cost of reducing accuracy.