OOF-AII-916812
Amazon's Rekognition Falsely Matched Members of Congress to Mugshots
Rekognition's face comparison feature was shown by the ACLU to have misidentified members of congress, and particularly members of colors, as other people who have been arrested using a mugshot database built on publicly available arrest photos.
- Event date
- 2018-07-26
- 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
- Extraction completeness
- High
- Methodological assignment confidence
- High
Measures source-group corroboration only; it does not establish factual truth.
Measures populated source fields after deterministic normalization; it does not measure correctness.
Measures automated assignment support; substantive correctness requires authorized human review.
Known facts from the incident record
- The incident record identifies the event date as 2018-07-26.
- The incident record identifies the location as United States of America.
Reported claims
- Rekognition's face comparison feature was shown by the ACLU to have misidentified members of congress, and particularly members of colors, as other people who have been arrested using a mugshot database built on publicly available arrest photos.
Material unknowns
- Independent corroboration is not established in the available incident record.
Primary governance problem
The incident appears to expose a governance problem involving communication, information, enforcement, other, available. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.
Architecture intelligence
PGA™ — Prediction Governance Architecture
Primary architecture
The reported communication, information, enforcement concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.
Governance focus: What information, data, observations, evidence references, and signals legitimately form the basis of the prediction?
Canonical methodology (ParentStandard):
VFM® — Value Flow Mechanisms Architecture
Contributing architecture
The reported recognition, enforcement, classification concern falls within the governed questions represented by VFM® — Value Flow Mechanisms Architecture.
Governance focus: When can existing value be recognized as value within its defined governance context?
Canonical methodology (ParentStandard):
Preliminary governance questions
- What information, data, observations, evidence references, and signals legitimately form the basis of the prediction?
- When can existing value be recognized as value within its defined governance context?
- What operational authority and controls applied to Amazon at the relevant time?
- What evidence would establish how yes 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.