# The effect of differential victim crime reporting on predictive policing systems

arXiv: https://arxiv.org/abs/2102.00128 · HTML: https://mlsanity.com/paper/2102.00128/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 62 references that are in the map.
- First posted on arXiv 30 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications](https://mlsanity.com/paper/1708.02647/) (2017)

## Built on by (most cited first, 15 of 15)

- [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Algorithmic Fairness in Business Analytics: Directions for Research and Practice](https://mlsanity.com/paper/2207.10991/) (2022)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing](https://mlsanity.com/paper/2204.08620/) (2022)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025)
- [Limits of Safe AI Deployment: Differentiating Oversight and Control](https://mlsanity.com/paper/2507.03525/) (2025)
- [Mitigating Label Bias via Decoupled Confident Learning](https://mlsanity.com/paper/2307.08945/) (2023)
- [Likelihood-Free Estimation for Spatiotemporal Hawkes processes with missing data and application to predictive policing](https://mlsanity.com/paper/2502.07111/) (2025)
- [A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City](https://mlsanity.com/paper/2602.02566/) (2026)
- [A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems](https://mlsanity.com/paper/2402.14959/) (2024)
- ["Even explanations will not help in trusting [this] fundamentally biased system": A Predictive Policing Case-Study](https://mlsanity.com/paper/2504.11020/) (2025)
- [Bias-Aware Mislabeling Detection via Decoupled Confident Learning](https://mlsanity.com/paper/2507.07216/) (2025)
- [Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scales](https://mlsanity.com/paper/2509.20913/) (2025)

## Abstract (arXiv)

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot'a, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing.