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Incident 257: Police Reportedly Deployed ShotSpotter Sensors Disproportionately in Neighborhoods of Color

Description: Police departments disproportionately placed ShotSpotter sensors in black and brown neighborhoods, which is denounced by communities for allegedly creating dangerous situations, such as one involving in Adam Toledo's death.

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Entities

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Alleged: ShotSpotter developed an AI system deployed by Kansas City Police Department , Cleveland Division of Police , Chicago Police Department and Atlanta Police Department, which harmed neighborhoods of color , Brown communities , Black communities and Adam Toledo.

Incident Stats

Incident ID
257
Report Count
4
Incident Date
2012-05-04
Editors
Khoa Lam
Applied Taxonomies
GMF, MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

1.1. Unfair discrimination and misrepresentation

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. Discrimination and Toxicity

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Post-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Incident Occurrence+1
ShotSpotter Generated Over 40,000 Dead-End Police Deployments in Chicago in 21 Months, According to New Study
+1
Report: ShotSpotter wastes officers time, provides little help in court, targets overpoliced communities
ShotSpotter Generated Over 40,000 Dead-End Police Deployments in Chicago in 21 Months, According to New Study

ShotSpotter Generated Over 40,000 Dead-End Police Deployments in Chicago in 21 Months, According to New Study

macarthurjustice.org

Gunshot-Detecting Tech Is Summoning Armed Police to Black Neighborhoods

Gunshot-Detecting Tech Is Summoning Armed Police to Black Neighborhoods

vice.com

Report: ShotSpotter wastes officers time, provides little help in court, targets overpoliced communities

Report: ShotSpotter wastes officers time, provides little help in court, targets overpoliced communities

syracuse.com

Report Evaluates Efficiency of Gunshot Detection Technology

Report Evaluates Efficiency of Gunshot Detection Technology

govtech.com

ShotSpotter Generated Over 40,000 Dead-End Police Deployments in Chicago in 21 Months, According to New Study
macarthurjustice.org · 2021

MacArthur Justice Center study finds City’s use of ShotSpotter is inaccurate, expensive and dangerous

CHICAGO – A new study of Chicago’s use of ShotSpotter, a surveillance system designed to detect gunfire, finds that the vast majority of a…

Gunshot-Detecting Tech Is Summoning Armed Police to Black Neighborhoods
vice.com · 2021

The right angle formed by Belmont and North Central Park avenues forms an invisible borderline in Chicago. 

To the south and east, the residents are primarily Black and brown. To the north and west, the neighborhoods are almost all majority…

Report: ShotSpotter wastes officers time, provides little help in court, targets overpoliced communities
syracuse.com · 2022

Syracuse, N.Y. – A report came out last week that criticizes gunshot detection technology used by cities across the country - including Syracuse - as ineffective, wasting officers’ time and targeting overpoliced communities.

The Surveillan…

Report Evaluates Efficiency of Gunshot Detection Technology
govtech.com · 2022

(TNS) — A report came out last week that criticizes gunshot detection technology used by cities across the country - including Syracuse - as ineffective, wasting officers’ time and targeting overpoliced communities.

The Surveillance Technol…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.
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