The Progression of Disparities within the Criminal Justice System: Differential Enforcement and Risk Assessment Instruments

• URL: http://arxiv.org/abs/2305.07575v1
• Date: Fri, 12 May 2023 16:06:40 GMT
• Title: The Progression of Disparities within the Criminal Justice System: Differential Enforcement and Risk Assessment Instruments
• Authors: Miri Zilka, Riccardo Fogliato, Jiri Hron, Bradley Butcher, Carolyn Ashurst, and Adrian Weller
• Abstract summary: Algorithmic risk assessment instruments (RAIs) increasingly inform decision-making in criminal justice. Problematically, the extent to which arrests reflect overall offending can vary with the person's characteristics. We examine how the disconnect between crime and arrest rates impacts RAIs and their evaluation.
• Score: 26.018802058292614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Algorithmic risk assessment instruments (RAIs) increasingly inform decision-making in criminal justice. RAIs largely rely on arrest records as a proxy for underlying crime. Problematically, the extent to which arrests reflect overall offending can vary with the person's characteristics. We examine how the disconnect between crime and arrest rates impacts RAIs and their evaluation. Our main contribution is a method for quantifying this bias via estimation of the amount of unobserved offenses associated with particular demographics. These unobserved offenses are then used to augment real-world arrest records to create part real, part synthetic crime records. Using this data, we estimate that four currently deployed RAIs assign 0.5--2.8 percentage points higher risk scores to Black individuals than to White individuals with a similar \emph{arrest} record, but the gap grows to 4.5--11.0 percentage points when we match on the semi-synthetic \emph{crime} record. We conclude by discussing the potential risks around the use of RAIs, highlighting how they may exacerbate existing inequalities if the underlying disparities of the criminal justice system are not taken into account. In light of our findings, we provide recommendations to improve the development and evaluation of such tools.

Related papers

• Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition [56.69875958980474]
  This work considers approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations. We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training. We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups.
  arXiv  Detail & Related papers  (2023-02-17T16:19:26Z)
• D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias [57.87117733071416]
  We propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases. A user can detect the presence of bias against a group by identifying unfair causal relationships in the causal network. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset.
  arXiv  Detail & Related papers  (2022-08-10T03:41:48Z)
• Flipping the Script on Criminal Justice Risk Assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants [0.0]
  algorithmic risk assessment instruments are used to predict the risk a defendant poses to society. We develop a risk assessment instrument that "flips the script" Our instrument achieves comparable predictive accuracy to risk assessment instruments used in pretrial and parole contexts.
  arXiv  Detail & Related papers  (2022-05-26T17:17:13Z)
• Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction [60.508960752148454]
  This work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework to tackle the label scarcity issue in crime prediction. We propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. We also design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination.
  arXiv  Detail & Related papers  (2022-04-18T23:46:01Z)
• Deep Interpretable Criminal Charge Prediction and Algorithmic Bias [2.3347476425292717]
  This paper addresses bias issues with post-hoc explanations to provide a trustable prediction of whether a person will receive future criminal charges. Our approach shows consistent and reliable prediction precision and recall on a real-life dataset.
  arXiv  Detail & Related papers  (2021-06-25T07:00:13Z)
• The effect of differential victim crime reporting on predictive policing systems [84.86615754515252]
  We show how differential victim crime reporting rates can lead to outcome disparities in common crime hot spot prediction models. 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.
  arXiv  Detail & Related papers  (2021-01-30T01:57:22Z)
• Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets [0.0]
  We adopt a framework from conformal prediction sets to remove unfairness from risk algorithms. From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness. We see our work as a demonstration of concept for application in a wide variety of criminal justice decisions.
  arXiv  Detail & Related papers  (2020-08-26T16:47:02Z)
• Fairness Evaluation in Presence of Biased Noisy Labels [84.12514975093826]
  We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.
  arXiv  Detail & Related papers  (2020-03-30T20:47:00Z)
• Crime Prediction Using Spatio-Temporal Data [8.50468505606714]
  Supervised learning technique is used to predict crimes with better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
  arXiv  Detail & Related papers  (2020-03-11T16:19:19Z)
• Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction [48.1813701535167]
  We perform crime prediction exploiting the cross-type and-temporal correlations of urban crimes. We propose a coherent framework to mathematically model these correlations for crime prediction. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
  arXiv  Detail & Related papers  (2020-01-20T00:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.