OOF-AII-349642
SN Technologies Reportedly Lied to a New York State School District about Its Facial and Weapon Detection Systems’ Performance
SN Technologies allegedly misled Lockport City Schools about the performance of its AEGIS face and weapons detection systems, downplaying error rates for Black faces and weapon misidentification.
- Event date
- 2020-01-02
- 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 2020-01-02.
- The incident record identifies the location as United States of America.
Reported claims
- SN Technologies allegedly misled Lockport City Schools about the performance of its AEGIS face and weapons detection systems, downplaying error rates for Black faces and weapon misidentification.
Material unknowns
- Independent corroboration is not established in the available incident record.
Primary governance problem
The incident appears to expose a governance problem involving performance, state, object, 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 performance, state, object concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.
Governance focus: How must a prediction and its complete history remain identifiable, reconstructable, versioned, and historically continuous across formation, revision, supersession, outcome, performance assessment, retirement, and lifecycle re-entry?
Canonical methodology (ParentStandard):
INTEGROS® — Integrity Governance Architecture
Contributing architecture
The reported detection, state, enforcement concern falls within the governed questions represented by INTEGROS® — Integrity Governance Architecture.
Governance focus: How should detection, state, enforcement be governed in the conditions described by the available incident evidence?
Canonical methodology (ParentStandard):
VFM® — Value Flow Mechanisms Architecture
Contributing architecture
The reported recognition, state, enforcement 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):
AIG® - Governance Architecture
Contributing architecture
The reported detection, enforcement concern falls within the governed questions represented by AIG® - Governance Architecture.
Governance focus: How should detection, 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.
GOA™ - Governance Architecture
Contributing architecture
The reported detection, enforcement concern falls within the governed questions represented by GOA™ - Governance Architecture.
Governance focus: How should detection, 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.
LIGA™ — Liability Governance Architecture
Contributing architecture
The reported state, performance, enforcement concern falls within the governed questions represented by LIGA™ — Liability Governance Architecture.
Governance focus: What is the current governed state of Liability, and how may that state legitimately change?
Canonical methodology (ParentStandard):
Preliminary governance questions
- How must a prediction and its complete history remain identifiable, reconstructable, versioned, and historically continuous across formation, revision, supersession, outcome, performance assessment, retirement, and lifecycle re-entry?
- How should detection, state, enforcement be governed in the conditions described by the available incident evidence?
- When can existing value be recognized as value within its defined governance context?
- How should detection, enforcement be governed in the conditions described by the available incident evidence?
- What is the current governed state of Liability, and how may that state legitimately change?
Evidence gaps and limitations
- Only one independent source group is represented; corroboration is not established.
- AI technology is not specified in the source record.