AiOS→HR / People→Reward & Govern→HR18
Bias / Consistency Review of HR Artifacts
Review HR artifacts for biased language and inconsistent standards, then produce redline corrections and a bias audit report.
Systematic bias review across HR artifacts catches inconsistent standards and biased language before they compound into inequitable decisions across performance, promotion, and compensation cycles.
GenAI Impact
44%
Faster
4.5
Hours saved
10.2
Hours without AI
Based on: 4 upstream HR artifact sets (policy, performance, promotion, compensation) reviewed for bias and consistency
The structured three-phase analysis — language bias scan, cross-artifact consistency assessment, then correction drafting — ensures every finding is categorised across five defined bias types and validated for cross-artifact patterns before any redlines are proposed, eliminating the inconsistent ad-hoc flagging common in manual reviews.
The governed workflow prevents exposure of individual employee performance ratings and compensation figures to unapproved AI tools by enforcing summary-level inputs in the correction and compilation steps, eliminating the PII leakage risk inherent in uncontrolled bias reviews.
Before You Start
This workflow processes performance review narratives, promotion outcomes, compensation rationale, and policy drafts containing sensitive employee and organisational data. Do not paste these inputs into public or unapproved GenAI tools.
GenAI may over-flag neutral language as biased or miss subtle bias patterns. Have a governance reviewer verify every flagged finding and proposed correction against the source artifact context before finalising.
Who's Involved
HR Governance Analyst
Assembles upstream artifacts, runs the bias and consistency review workflow, and coordinates corrections through to approval.
Diversity Reviewer
Validates flagged bias findings against organisational equity standards and confirms correction appropriateness.
HR Director
Approves the final bias audit report and authorises redlined corrections for implementation.
Execution Steps
Before you start
Inputs
Prompt
Scan HR artifacts for biased or exclusionary language
CONTEXT You will be provided with the following source documents: 1. Policy Draft Redlines 2. Performance Review Narratives 3. Promotion Panel Outcomes 4. Compensation Recommendation Rationale 5. Bias Review Criteria 6. Consistency Standards Checklist TASK Scan each artifact for biased language, including gendered wording, subjective qualifiers without evidence, culturally loaded phrases, and exclusionary terminology. For each finding, identify the source artifact, the specific passage, the bias type, and a brief explanation of why the language is problematic. OUTPUT FORMAT Return a markdown table with the following columns: | # | Source Artifact | Passage | Bias Type | Explanation | |---|---|---|---|---| Bias Type must be one of: Gendered Language, Subjective Qualifier, Cultural Bias, Exclusionary Term, Vague Justification. After the table, include a section titled "Prevalent Patterns" with a one-paragraph summary of the most common bias patterns across all artifacts. CONSTRAINTS Do not suggest replacement language in this step — focus on identification only. Do not flag language that is factual and evidence-backed merely because it uses strong terms. Only flag issues supported by the specific passage text provided.
Outputs
Verification: Verify the AI did not over-flag neutral professional language as biased or miss context-dependent bias patterns that require domain understanding.
Before you start
Inputs
Prompt
Identify inconsistent standards across HR artifacts
CONTEXT You will be provided with the following source documents: 1. Policy Draft Redlines 2. Performance Review Narratives 3. Promotion Panel Outcomes 4. Compensation Recommendation Rationale 5. Bias Review Criteria 6. Consistency Standards Checklist 7. Language Bias Scan Results TASK Compare the standards, criteria, and justification patterns used across the four artifact types. Identify where the same role, decision category, or evaluation criterion is described using inconsistent standards, conflicting justifications, or mismatched language. For each inconsistency, state the two conflicting artifacts, the discrepancy, and its potential impact on equitable decision-making. OUTPUT FORMAT Return a markdown table: | # | Artifact A | Artifact B | Discrepancy | Potential Impact | |---|---|---|---|---| After the table, include a section titled "Cross-Cutting Patterns" with 2–4 bullet points summarising the systemic consistency issues found across artifacts. CONSTRAINTS Do not evaluate the correctness of any individual artifact’s conclusion — focus on cross-artifact alignment only. Do not fabricate connections between artifacts where the source data does not overlap. Only flag discrepancies supported by direct evidence in both artifacts.
Outputs
Verification: Verify the AI did not fabricate cross-artifact links where the source documents address unrelated topics or roles.
Before you start
Data Handling: Do not paste raw employee performance ratings or individual compensation figures into the prompt; use the summary-level findings from the scan and assessment only.
Inputs
Prompt
Outputs
Verification: Verify the AI did not alter factual performance conclusions or introduce new language that shifts the meaning of the original assessment beyond the identified bias or inconsistency.
Before you start
Inputs
Prompt
Outputs
Before you start
Inputs
Prompt
Outputs
Verification: Verify the AI did not inflate severity counts or fabricate systemic patterns not supported by the underlying findings data.
Before you start
Inputs
Outputs
Reference
Guardrails
- Evidence-Based Flagging Only — Only flag bias or inconsistency issues supported by specific passages in the source artifacts; do not rely on AI inference or assumed organisational context.
- Cross-Artifact Comparison Required — Review all four artifact types together to identify cross-cutting inconsistencies; do not assess each artifact in isolation.
- Scan Before Correction — Complete the full bias scan and consistency assessment before drafting any redline corrections to prevent premature edits that miss systemic patterns.
Pitfalls
- Accepting AI bias flags at face value without verifying them against the original artifact language and surrounding context
- Pasting full employee performance narratives or individual compensation figures into the prompt instead of using summary-level inputs
- Allowing the AI to apply a single bias definition uniformly across all artifact types without accounting for differences in document purpose and audience
- Skipping the consistency assessment and proceeding directly to redlines based on the language bias scan alone
Definition of Done
- Every flagged bias or inconsistency issue maps to a specific passage in one of the four source artifacts with a cited finding reference
- The consistency assessment covers all four artifact types and identifies cross-artifact patterns, not just within-document issues
- The bias audit report includes a severity classification for every flagged item with supporting evidence from the validated redline package
- No personally identifiable information or specific employee data appears in any generated artifact
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AGASI AiOS · HR18 v1.0 · Apr 8, 2026