Racial and Neighborhood Disparities in Legal Financial Obligations in Jefferson County, Alabama

Legal financial obligations (LFOs) such as court fees and fines are commonly levied on individuals who are convicted of crimes. It is expected that LFO amounts should be similar across social, racial, and geographic subpopulations convicted of the same crime. This work analyzes the distribution of LFOs in Jefferson County, Alabama and highlights disparities across different individual and neighborhood demographic characteristics. Data-driven discovery methods are used to detect subpopulations that experience higher LFOs than the overall population of offenders. Critically, these discovery methods do not rely on pre-specified groups and can assist scientists and researchers investigate socially-sensitive hypotheses in a disciplined way. Some findings, such as individuals who are Black, live in Black-majority neighborhoods, or live in low-income neighborhoods tending to experience higher LFOs, are commensurate with prior expectation. However others, such as high LFO amounts in worthless instrument (bad check) cases experienced disproportionately by individuals living in affluent majority-white neighborhoods, are more surprising. More broadly than the specific findings, the methodology is shown to identify structural weaknesses that undermine the goal of equal justice under law that can be addressed through policy interventions.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Published
2024-10-16
DOI
https://doi.org/10.1609/aies.v7i1.31682
Primary Topic
Healthcare Policy and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Racial and Neighborhood Disparities in Legal Financial Obligations in Jefferson County, Alabama

Andrew Martinez, Kush R. Varshney, Skyler Speakman, Jason Bond et al.
Proceedings of the AAAI/ACM Conference on AI Ethics and Society
Healthcare Policy and Management
article

Racial and Neighborhood Disparities in Legal Financial Obligations in Jefferson County, Alabama

Andrew Martinez, Kush R. Varshney, Skyler Speakman, Jason Bond, Leah Nelson, Arvind K. Joshi, Yuki S. Nishimura, K.G. Thompson, Óscar Lara Yejas
article en

Abstract

Legal financial obligations (LFOs) such as court fees and fines are commonly levied on individuals who are convicted of crimes. It is expected that LFO amounts should be similar across social, racial, and geographic subpopulations convicted of the same crime. This work analyzes the distribution of LFOs in Jefferson County, Alabama and highlights disparities across different individual and neighborhood demographic characteristics. Data-driven discovery methods are used to detect subpopulations that experience higher LFOs than the overall population of offenders. Critically, these discovery methods do not rely on pre-specified groups and can assist scientists and researchers investigate socially-sensitive hypotheses in a disciplined way. Some findings, such as individuals who are Black, live in Black-majority neighborhoods, or live in low-income neighborhoods tending to experience higher LFOs, are commensurate with prior expectation. However others, such as high LFO amounts in worthless instrument (bad check) cases experienced disproportionately by individuals living in affluent majority-white neighborhoods, are more surprising. More broadly than the specific findings, the methodology is shown to identify structural weaknesses that undermine the goal of equal justice under law that can be addressed through policy interventions.

Proceedings of the AAAI/ACM Conference on AI Ethics and SocietyVol. 7
Apple (Israel) (IL), IBM Research - Thomas J. Watson Research Center (US), IBK-Innovation (Germany) (DE), Silicon Valley University (US), IBM Research - Africa (ZA)
Openalex Percentile: Top 75%
Healthcare Policy and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.