OOF-AII-032495
IRS Audited Black Taxpayers More Frequently Reportedly Due to Algorithm
The IRS was auditing Black taxpayers more frequently than other groups allegedly due to the design of their algorithms, focusing on easier-to-conduct audits which inadvertently correlated with the group's pattern of tax filing errors.
- Event date
- 2008-07-18
- 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
- Limited
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 2008-07-18.
- The incident record identifies the location as United States of America.
Reported claims
- The IRS was auditing Black taxpayers more frequently than other groups allegedly due to the design of their algorithms, focusing on easier-to-conduct audits which inadvertently correlated with the group's pattern of tax filing errors.
Material unknowns
- Independent corroboration is not established in the available incident record.
Primary governance problem
The incident appears to expose a governance problem involving support, other. Available reporting supports preliminary methodological interpretation, not a finding of fault or non-compliance.
Architecture intelligence
PGA™ — Prediction Governance Architecture
Primary architecture
The reported support, other concern falls within the governed questions represented by PGA™ — Prediction Governance Architecture.
Governance focus: How should support, other 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 other, conduct, insurance concern falls within the governed questions represented by LIGA™ — Liability Governance Architecture.
Governance focus: What sufficiently governed connection exists between the relevant Liability Subject, Liability Object, conditions, conduct, and other Liability-relevant inputs?
Canonical methodology (ParentStandard):
CLIA® — Cognitive Governance Intelligence Architecture
Contributing architecture
The reported decision concern falls within the governed questions represented by CLIA® — Cognitive Governance Intelligence Architecture.
Governance focus: How should decision 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 support, other be governed in the conditions described by the available incident evidence?
- What sufficiently governed connection exists between the relevant Liability Subject, Liability Object, conditions, conduct, and other Liability-relevant inputs?
- How should decision be governed in the conditions described by the available incident evidence?
- What operational authority and controls applied to Internal Revenue Service at the relevant time?
- What evidence would establish how Ai Enabled Decision Support Systems produced or enabled the reported outcome?
Evidence gaps and limitations
- Only one independent source group is represented; corroboration is not established.
- AI technology is not specified in the source record.