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Incident 274: Virginia Courts’ Algorithmic Recidivism Risk Assessment Failed to Lower Incarceration Rates

Description: Virginia courts’ use of algorithmic predictions of future offending risks were found by researchers failing to reduce incarceration rates, showed racial and age disparities in risk scores and its application, and neither exacerbated or ameliorated historical racial differences in sentencing.

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Alleged: Virginia Department of Criminal Justice Services developed an AI system deployed by Virginia courts, which harmed Virginia convicted felons , Virginia Black offenders and Virginia young offenders.

Incident Stats

Incident ID
274
Report Count
2
Incident Date
2003-07-01
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
 

Other

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
Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated.
Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated.

Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated.

washingtonpost.com

Algorithmic Risk Assessment in the Hands of Humans

Algorithmic Risk Assessment in the Hands of Humans

papers.ssrn.com

Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated.
washingtonpost.com · 2019

We tend to assume the near-term future of automation will be built on man-machine partnerships. Our robot sidekicks will compensate for the squishy inefficiencies of the human brain, while human judgment will sand down their cold, mechanica…

Algorithmic Risk Assessment in the Hands of Humans
papers.ssrn.com · 2019

We evaluate the impacts of adopting algorithmic risk assessments as an aid to judicial discretion in felony sentencing. We find that judges' decisions are influenced by the risk score, leading to longer sentences for defendants with higher …

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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