OOF-AII-221499

Northpointe Risk Models

An algorithm developed by Northpointe and used in the penal system is two times more likely to incorrectly label a black person as a high-risk re-offender and is two times more likely to incorrectly label a white person as low-risk for reoffense according to a ProPublica review.

Event date
2016-05-23
Country
United States of America
Severity
Low
Evidence state
Single-source

Assessment status

Generated from available public incident evidence using OOF® governance methodology. It is not an official investigation, legal determination, regulatory finding, certification, or OOF® Approved Assessment.

View source record from AI Incident Database (Responsible AI Collaborative)

Assessment confidence dimensions

Source corroboration
Single-source

Measures source-group corroboration only; it does not establish factual truth.

Extraction completeness
High

Measures populated source fields after deterministic normalization; it does not measure correctness.

Methodological assignment confidence
Moderate

Measures automated assignment support; substantive correctness requires authorized human review.

Known facts from the incident record

  • The incident record identifies the event date as 2016-05-23.
  • The incident record identifies the location as United States of America.

Reported claims

  • An algorithm developed by Northpointe and used in the penal system is two times more likely to incorrectly label a black person as a high-risk re-offender and is two times more likely to incorrectly label a white person as low-risk for reoffense according to a ProPublica review.

Material unknowns

  • Independent corroboration is not established in the available incident record.

Primary governance problem

The incident appears to expose a governance problem involving models, support, enforcement. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.

Architecture intelligence

PGA™ — Prediction Governance Architecture

Primary architecture

The reported models, support, enforcement concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.

Governance focus: How should models, support, enforcement be governed in the conditions described by the available incident evidence?

Deeper methodology is withheld because the available evidence does not justify a Parent Standard or Core Module.

Preliminary governance questions

  • How should models, support, enforcement be governed in the conditions described by the available incident evidence?
  • What operational authority and controls applied to Northpointe at the relevant time?
  • What evidence would establish how Ai Enabled Decision Support Systems produced or enabled the reported outcome?
  • What additional independent evidence is required to resolve the material evidence gaps?

Evidence gaps and limitations

  • Only one independent source group is represented; corroboration is not established.
  • AI technology is not specified in the source record.

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