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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Khoa Lam
Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated. · 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 · 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 …


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.