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Unmasking Algorithmic Bias in Predictive Policing: A GAN-Based Simulation Framework with Multi-City Temporal Analysis

Pronob Kumar Barman, Pronoy Kumar Barman

Abstract

Predictive policing systems that direct patrol resources based on algorithmically generated crime forecasts have been widely deployed across US cities, yet their tendency to encode and amplify racial disparities remains poorly understood in quantitative terms. We present a reproducible simulation framework that couples a Generative Adversarial Network GAN with a Noisy OR patrol detection model to measure how racial bias propagates through the full enforcement pipeline from crime occurrence to police contact. Using 145000 plus Part 1 crime records from Baltimore 2017 to 2019 and 233000 plus records from Chicago 2022, augmented with US Census ACS demographic data, we compute four monthly bias metrics across 264 city year mode observations: the Disparate Impact Ratio DIR, Demographic Parity Gap, Gini Coefficient, and a composite Bias Amplification Score. Our experiments reveal extreme and year variant bias in Baltimores detected mode, with mean annual DIR up to 15714 in 2019, moderate under detection of Black residents in Chicago DIR equals 0.22, and persistent Gini coefficients of 0.43 to 0.62 across all conditions. We further demonstrate that a Conditional Tabular GAN CTGAN debiasing approach partially redistributes detection rates but cannot eliminate structural disparity without accompanying policy intervention. Socioeconomic regression analysis confirms strong correlations between neighborhood racial composition and detection likelihood Pearson r equals 0.83 for percent White and r equals negative 0.81 for percent Black. A sensitivity analysis over patrol radius, officer count, and citizen reporting probability reveals that outcomes are most sensitive to officer deployment levels. The code and data are publicly available at this repository.

Unmasking Algorithmic Bias in Predictive Policing: A GAN-Based Simulation Framework with Multi-City Temporal Analysis

Abstract

Predictive policing systems that direct patrol resources based on algorithmically generated crime forecasts have been widely deployed across US cities, yet their tendency to encode and amplify racial disparities remains poorly understood in quantitative terms. We present a reproducible simulation framework that couples a Generative Adversarial Network GAN with a Noisy OR patrol detection model to measure how racial bias propagates through the full enforcement pipeline from crime occurrence to police contact. Using 145000 plus Part 1 crime records from Baltimore 2017 to 2019 and 233000 plus records from Chicago 2022, augmented with US Census ACS demographic data, we compute four monthly bias metrics across 264 city year mode observations: the Disparate Impact Ratio DIR, Demographic Parity Gap, Gini Coefficient, and a composite Bias Amplification Score. Our experiments reveal extreme and year variant bias in Baltimores detected mode, with mean annual DIR up to 15714 in 2019, moderate under detection of Black residents in Chicago DIR equals 0.22, and persistent Gini coefficients of 0.43 to 0.62 across all conditions. We further demonstrate that a Conditional Tabular GAN CTGAN debiasing approach partially redistributes detection rates but cannot eliminate structural disparity without accompanying policy intervention. Socioeconomic regression analysis confirms strong correlations between neighborhood racial composition and detection likelihood Pearson r equals 0.83 for percent White and r equals negative 0.81 for percent Black. A sensitivity analysis over patrol radius, officer count, and citizen reporting probability reveals that outcomes are most sensitive to officer deployment levels. The code and data are publicly available at this repository.
Paper Structure (31 sections, 7 equations, 9 figures, 5 tables)

This paper contains 31 sections, 7 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Bias in Predictive Policing
  4. Fairness in Spatial and Temporal Crime Prediction
  5. Generative Models for Fairness
  6. Measuring Algorithmic Fairness
  7. Datasets
  8. Baltimore Part 1 Crime Data (2017--2019)
  9. Chicago Crime Data (2022)
  10. Demographic Data
  11. Citizen Reporting Rate
  12. Methodology
  13. GAN Architecture for Patrol Location Generation
  14. Noisy-OR Detection Model
  15. Bias Metrics
  16. ...and 16 more sections

Figures (9)

  • Figure 1: Monthly detection rates by racial group for Baltimore (2017--2019) in detected mode. The extreme spike in 2019 Black detection rates reflects the GAN learning patrol patterns concentrated in majority-Black neighbourhoods.
  • Figure 2: Monthly Disparate Impact Ratio (DIR) for Baltimore 2017--2019 across both simulation modes. The detected mode exhibits extreme year-to-year variance (DIR range: 0.04--35,582), while the reported mode remains more stable (DIR range: 0.24--1.66).
  • Figure 3: Monthly Demographic Parity Gap (Black detection rate minus White detection rate) for Baltimore across all years and modes. Values above zero indicate higher per-capita detection of Black residents. The 2019 detected mode is the only configuration where the gap is consistently positive.
  • Figure 4: Cross-city comparison of monthly DIR values across all city-year configurations. Baltimore 2019 detected mode (mean DIR = 15,714) is truncated for display; Chicago 2022 shows systematic under-detection of Black residents (mean DIR = 0.22).
  • Figure 5: Sensitivity analysis of DIR across patrol radius (400--1500 ft), officer count (30--120), and citizen reporting probability (0.30--0.80). Officer count has the largest effect on DIR magnitude.
  • ...and 4 more figures