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AI BIAS ASSESSMENT TEMPLATE

ASSESSMENT INFORMATION

Field Information
Organization [ORGANIZATION NAME]
AI System Name [SYSTEM NAME]
Assessment ID [ASSESSMENT-ID]
Assessment Date [DATE]
Assessor(s) [NAME(S) AND ROLE(S)]
Assessment Type ☐ Pre-deployment ☐ Periodic ☐ Post-incident ☐ Regulatory
Version [VERSION]

EXECUTIVE SUMMARY

Overall Bias Risk Assessment

Risk Level Assessment
☐ Low Minimal bias concerns identified; proceed with standard monitoring
☐ Medium Some bias concerns; implement recommended mitigations before/during deployment
☐ High Significant bias concerns; require remediation before deployment
☐ Critical Severe bias concerns; do not deploy without substantial changes

Summary of Key Findings

Bias Concerns Identified:

  1. [CONCERN 1]
  2. [CONCERN 2]
  3. [CONCERN 3]

Recommended Actions:

  1. [ACTION 1]
  2. [ACTION 2]
  3. [ACTION 3]

Approval Status

Decision Approver Date
☐ Approved for deployment
☐ Approved with conditions
☐ Requires remediation
☐ Not approved

SECTION 1: SYSTEM OVERVIEW

1.1 System Description

System Name: [NAME]

System Purpose:
[Describe the primary purpose and intended use of the AI system]

System Type:
☐ Classification
☐ Prediction/Regression
☐ Recommendation
☐ Natural Language Processing
☐ Computer Vision
☐ Generative AI
☐ Anomaly Detection
☐ Other: [SPECIFY]

Deployment Context:
☐ Customer-facing
☐ Employee-facing
☐ Internal operations
☐ B2B service
☐ Other: [SPECIFY]

1.2 Decision Impact

Decision Types Made/Supported:

Decision Type Fully Automated Human Oversight Impact Level
[DECISION 1] ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low
[DECISION 2] ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low
[DECISION 3] ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low

Consequential Decisions:
☐ Employment (hiring, firing, promotion)
☐ Credit and lending
☐ Insurance underwriting/claims
☐ Housing access
☐ Healthcare access/treatment
☐ Educational opportunities
☐ Criminal justice
☐ Government benefits
☐ Essential services
☐ Other: [SPECIFY]

1.3 Stakeholders

Stakeholder Group How They Are Affected
Direct users [DESCRIPTION]
Decision subjects [DESCRIPTION]
Affected communities [DESCRIPTION]
Employees [DESCRIPTION]
Regulators [DESCRIPTION]
Other: [GROUP] [DESCRIPTION]

1.4 Regulatory Context

Applicable Regulations:

☐ EU AI Act (Regulation (EU) 2024/1689)
☐ Colorado AI Act (SB 24-205)
☐ NYC Local Law 144
☐ Illinois AI employment laws
☐ Equal Credit Opportunity Act (ECOA)
☐ Fair Housing Act
☐ Title VII / Employment discrimination laws
☐ GDPR Article 22
☐ Other: [SPECIFY]

Risk Classification:
☐ High-Risk AI System (EU AI Act)
☐ High-Risk AI System (Colorado AI Act)
☐ Automated Employment Decision Tool (NYC LL144)
☐ Other regulated category: [SPECIFY]

SECTION 2: PROTECTED CHARACTERISTICS AND SENSITIVE ATTRIBUTES

2.1 Protected Characteristics

Identify protected characteristics relevant to this system's deployment context:

Characteristic Relevant Direct Data Proxy Risk Testing Priority
Race/Ethnicity ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Gender/Sex ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Age ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Disability ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Religion ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
National Origin ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Sexual Orientation ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Gender Identity ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Marital Status ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Veteran Status ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Pregnancy ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Genetic Information ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low
Other: [SPECIFY] ☐ Yes ☐ No ☐ Yes ☐ No ☐ High ☐ Med ☐ Low ☐ High ☐ Med ☐ Low

2.2 Proxy Variables

Identify features that may serve as proxies for protected characteristics:

Feature Potential Proxy For Risk Assessment Mitigation
Zip code/Location Race, Income ☐ High ☐ Med ☐ Low [MITIGATION]
Name Race, Gender, Ethnicity ☐ High ☐ Med ☐ Low [MITIGATION]
Education institution Race, Socioeconomic ☐ High ☐ Med ☐ Low [MITIGATION]
Employment gaps Gender, Disability ☐ High ☐ Med ☐ Low [MITIGATION]
Language patterns Ethnicity, Education ☐ High ☐ Med ☐ Low [MITIGATION]
[FEATURE] [PROXY FOR] ☐ High ☐ Med ☐ Low [MITIGATION]

2.3 Intersectionality Considerations

Identify intersectional groups that may face compounded disadvantage:

Intersectional Group Assessment Priority
[GROUP 1, e.g., Black women] ☐ High ☐ Med ☐ Low
[GROUP 2, e.g., Older workers with disabilities] ☐ High ☐ Med ☐ Low
[GROUP 3] ☐ High ☐ Med ☐ Low

SECTION 3: DATA BIAS ASSESSMENT

3.1 Training Data Overview

Attribute Details
Data Source(s) [DESCRIBE SOURCES]
Collection Period [DATE RANGE]
Total Records [COUNT]
Geographic Scope [REGIONS]
Collection Method [METHOD]

3.2 Data Representativeness

Demographic Composition of Training Data:

Group Training Data % Target Population % Gap Assessment
[GROUP 1] [%] [%] [%] ☐ OK ☐ Concern
[GROUP 2] [%] [%] [%] ☐ OK ☐ Concern
[GROUP 3] [%] [%] [%] ☐ OK ☐ Concern
[GROUP 4] [%] [%] [%] ☐ OK ☐ Concern

Underrepresentation Concerns:

☐ None identified
☐ Concerns identified:

  • [CONCERN 1]
  • [CONCERN 2]

3.3 Historical Bias in Data

Question Assessment Notes
Does data reflect historical discrimination? ☐ Yes ☐ No ☐ Unknown [NOTES]
Were labels/outcomes influenced by past bias? ☐ Yes ☐ No ☐ Unknown [NOTES]
Does data exclude populations due to past practices? ☐ Yes ☐ No ☐ Unknown [NOTES]
Was data collected with biased processes? ☐ Yes ☐ No ☐ Unknown [NOTES]

Historical Bias Assessment:
[DESCRIBE ANY HISTORICAL BIAS CONCERNS AND HOW THEY WERE ADDRESSED]

3.4 Label/Outcome Bias

Question Assessment Notes
How were labels/ground truth determined? [METHOD]
Were human judgments involved in labeling? ☐ Yes ☐ No [IF YES, BIAS RISK]
Is the target variable a good proxy for the concept? ☐ Yes ☐ Partial ☐ No [NOTES]
Do labels reflect biased outcomes to be perpetuated? ☐ Yes ☐ No ☐ Unknown [NOTES]

3.5 Data Quality by Group

Group Completeness Accuracy Consistency Overall Quality
[GROUP 1] [%] [ASSESSMENT] [ASSESSMENT] ☐ Good ☐ Fair ☐ Poor
[GROUP 2] [%] [ASSESSMENT] [ASSESSMENT] ☐ Good ☐ Fair ☐ Poor
[GROUP 3] [%] [ASSESSMENT] [ASSESSMENT] ☐ Good ☐ Fair ☐ Poor

3.6 Data Bias Mitigation

Mitigation Measures Applied:

☐ Resampling to balance representation
☐ Reweighting to adjust for imbalances
☐ Removal of biased historical outcomes
☐ Exclusion of protected attributes and proxies
☐ Data augmentation for underrepresented groups
☐ Collection of additional representative data
☐ Other: [SPECIFY]

SECTION 4: MODEL/ALGORITHM BIAS ASSESSMENT

4.1 Model Overview

Attribute Details
Model Type [TYPE]
Algorithm [ALGORITHM]
Features Used [COUNT] key features listed below
Training Approach [APPROACH]
Last Trained [DATE]

Key Features:

  1. [FEATURE 1]
  2. [FEATURE 2]
  3. [FEATURE 3]
  4. [FEATURE 4]
  5. [FEATURE 5]

4.2 Algorithmic Fairness Approach

Fairness Constraints/Approach:

☐ No explicit fairness constraints
☐ Pre-processing fairness (data-level interventions)
☐ In-processing fairness (fairness-aware learning)
☐ Post-processing fairness (output adjustments)
☐ Multiple approaches: [SPECIFY]

Fairness Definition(s) Optimized:

☐ Demographic parity (equal selection rates)
☐ Equalized odds (equal TPR and FPR)
☐ Equal opportunity (equal TPR)
☐ Calibration (equal precision across groups)
☐ Individual fairness (similar treatment for similar individuals)
☐ Other: [SPECIFY]

4.3 Feature Importance and Bias Risk

Feature Importance Rank Proxy Risk Bias Concern
[FEATURE 1] [RANK] ☐ High ☐ Med ☐ Low [CONCERN]
[FEATURE 2] [RANK] ☐ High ☐ Med ☐ Low [CONCERN]
[FEATURE 3] [RANK] ☐ High ☐ Med ☐ Low [CONCERN]
[FEATURE 4] [RANK] ☐ High ☐ Med ☐ Low [CONCERN]
[FEATURE 5] [RANK] ☐ High ☐ Med ☐ Low [CONCERN]

4.4 Model Bias Mitigation

Mitigation Techniques Applied:

☐ Feature selection to remove biased features
☐ Fairness constraints during training
☐ Adversarial debiasing
☐ Regularization for fairness
☐ Threshold adjustment by group
☐ Ensemble methods for bias reduction
☐ Other: [SPECIFY]

SECTION 5: FAIRNESS METRICS AND TESTING

5.1 Testing Methodology

Test Dataset:

Attribute Details
Source [SOURCE]
Size [COUNT]
Period [DATE RANGE]
Independence from training ☐ Independent ☐ Holdout ☐ Cross-validation

Testing Approach:

☐ Static test dataset evaluation
☐ A/B testing in production
☐ Shadow mode testing
☐ Simulation testing
☐ Other: [SPECIFY]

5.2 Overall Performance Metrics

Metric Value Threshold Assessment
Accuracy [VALUE] [THRESHOLD] ☐ Pass ☐ Fail
Precision [VALUE] [THRESHOLD] ☐ Pass ☐ Fail
Recall [VALUE] [THRESHOLD] ☐ Pass ☐ Fail
F1 Score [VALUE] [THRESHOLD] ☐ Pass ☐ Fail
AUC-ROC [VALUE] [THRESHOLD] ☐ Pass ☐ Fail

5.3 Group-Level Performance

Performance by Protected Group:

Group Accuracy Precision Recall F1 Selection Rate
Overall [VALUE] [VALUE] [VALUE] [VALUE] [VALUE]
[GROUP 1] [VALUE] [VALUE] [VALUE] [VALUE] [VALUE]
[GROUP 2] [VALUE] [VALUE] [VALUE] [VALUE] [VALUE]
[GROUP 3] [VALUE] [VALUE] [VALUE] [VALUE] [VALUE]
[GROUP 4] [VALUE] [VALUE] [VALUE] [VALUE] [VALUE]

5.4 Fairness Metrics

Demographic Parity (Equal Selection Rates):

Comparison Selection Rate Ratio Threshold Assessment
[GROUP A] vs [GROUP B] [RATIO] 0.80-1.25 ☐ Pass ☐ Fail
[GROUP C] vs [GROUP D] [RATIO] 0.80-1.25 ☐ Pass ☐ Fail

Four-fifths rule: Ratio should be at least 0.80 (80%)

Equalized Odds (Equal Error Rates):

Group True Positive Rate False Positive Rate TPR Ratio FPR Ratio
[REFERENCE] [VALUE] [VALUE] 1.00 1.00
[GROUP 1] [VALUE] [VALUE] [RATIO] [RATIO]
[GROUP 2] [VALUE] [VALUE] [RATIO] [RATIO]

Equal Opportunity (Equal TPR):

Group True Positive Rate Ratio to Reference Assessment
[REFERENCE] [VALUE] 1.00 Reference
[GROUP 1] [VALUE] [RATIO] ☐ Pass ☐ Fail
[GROUP 2] [VALUE] [RATIO] ☐ Pass ☐ Fail

Disparate Impact Ratio:

Comparison Impact Ratio Legal Threshold Assessment
[COMPARISON 1] [RATIO] 0.80 ☐ Pass ☐ Fail
[COMPARISON 2] [RATIO] 0.80 ☐ Pass ☐ Fail

Calibration:

Group Predicted Positive Rate Actual Positive Rate Calibration Gap
[GROUP 1] [VALUE] [VALUE] [GAP]
[GROUP 2] [VALUE] [VALUE] [GAP]

5.5 Intersectional Analysis

Intersectional Group Selection Rate Disparity Assessment
[GROUP A + B] [VALUE] [VS OVERALL] ☐ Concern ☐ OK
[GROUP C + D] [VALUE] [VS OVERALL] ☐ Concern ☐ OK

5.6 Fairness Testing Summary

Fairness Dimension Assessment Key Findings
Demographic Parity ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]
Equalized Odds ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]
Equal Opportunity ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]
Disparate Impact ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]
Calibration ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]
Intersectional ☐ Pass ☐ Fail ☐ Marginal [FINDINGS]

SECTION 6: DEPLOYMENT AND OPERATIONAL BIAS

6.1 Deployment Context Risks

Risk Factor Assessment Mitigation
User population differs from training ☐ High ☐ Med ☐ Low [MITIGATION]
Different geographic deployment ☐ High ☐ Med ☐ Low [MITIGATION]
Different time period ☐ High ☐ Med ☐ Low [MITIGATION]
Different use patterns ☐ High ☐ Med ☐ Low [MITIGATION]
Integration with biased processes ☐ High ☐ Med ☐ Low [MITIGATION]

6.2 Human-AI Interaction Bias

Question Assessment Notes
Do humans have tendency to over-rely on AI? ☐ Yes ☐ No ☐ Unknown [NOTES]
Are human reviewers trained on bias? ☐ Yes ☐ No ☐ Partial [NOTES]
Can humans effectively override AI? ☐ Yes ☐ No ☐ Partial [NOTES]
Are overrides tracked and analyzed? ☐ Yes ☐ No [NOTES]

6.3 Feedback Loop Risks

Risk Assessment Mitigation
Biased outputs influence future training data ☐ High ☐ Med ☐ Low [MITIGATION]
Self-reinforcing discrimination ☐ High ☐ Med ☐ Low [MITIGATION]
Lack of outcome data for rejected cases ☐ High ☐ Med ☐ Low [MITIGATION]

6.4 Monitoring Plan

Ongoing Bias Monitoring:

Metric Frequency Threshold Alert Process
[METRIC 1] [FREQUENCY] [THRESHOLD] [PROCESS]
[METRIC 2] [FREQUENCY] [THRESHOLD] [PROCESS]
[METRIC 3] [FREQUENCY] [THRESHOLD] [PROCESS]

SECTION 7: FINDINGS AND RECOMMENDATIONS

7.1 Bias Findings Summary

Finding # Description Severity Category
1 [DESCRIPTION] ☐ Critical ☐ High ☐ Medium ☐ Low [DATA/MODEL/DEPLOYMENT]
2 [DESCRIPTION] ☐ Critical ☐ High ☐ Medium ☐ Low [DATA/MODEL/DEPLOYMENT]
3 [DESCRIPTION] ☐ Critical ☐ High ☐ Medium ☐ Low [DATA/MODEL/DEPLOYMENT]

7.2 Detailed Findings

Finding 1: [TITLE]

Aspect Details
Description [DETAILED DESCRIPTION]
Affected Groups [GROUPS]
Evidence [EVIDENCE/METRICS]
Root Cause [CAUSE]
Impact [IMPACT]
Recommendation [RECOMMENDATION]
Priority ☐ Immediate ☐ Short-term ☐ Medium-term

Finding 2: [TITLE]

Aspect Details
Description [DETAILED DESCRIPTION]
Affected Groups [GROUPS]
Evidence [EVIDENCE/METRICS]
Root Cause [CAUSE]
Impact [IMPACT]
Recommendation [RECOMMENDATION]
Priority ☐ Immediate ☐ Short-term ☐ Medium-term

[REPEAT FOR ADDITIONAL FINDINGS]

7.3 Recommendations

# Recommendation Priority Owner Target Date
1 [RECOMMENDATION] ☐ High ☐ Med ☐ Low [OWNER] [DATE]
2 [RECOMMENDATION] ☐ High ☐ Med ☐ Low [OWNER] [DATE]
3 [RECOMMENDATION] ☐ High ☐ Med ☐ Low [OWNER] [DATE]

7.4 Risk Acceptance (If Applicable)

For any residual bias risks that cannot be fully mitigated:

Risk Justification for Acceptance Accepted By Date
[RISK] [JUSTIFICATION] [NAME/ROLE] [DATE]

SECTION 8: ATTESTATION AND APPROVAL

8.1 Assessment Attestation

I/We attest that this bias assessment was conducted in accordance with applicable standards and organizational policies, and the findings accurately represent the assessment results.

Lead Assessor:

Name: _________________________________

Title: _________________________________

Signature: _________________________________

Date: _________________________________

Additional Assessors:

Name Role Signature Date

8.2 Review and Approval

Technical Review:

Name: _________________________________

Title: _________________________________

Assessment: ☐ Approved ☐ Approved with Comments ☐ Not Approved

Comments: _________________________________

Signature: _________________________________ Date: _____________

Legal/Compliance Review:

Name: _________________________________

Title: _________________________________

Assessment: ☐ Approved ☐ Approved with Comments ☐ Not Approved

Comments: _________________________________

Signature: _________________________________ Date: _____________

Business Owner Approval:

Name: _________________________________

Title: _________________________________

Decision: ☐ Proceed ☐ Proceed with Conditions ☐ Do Not Proceed

Conditions (if applicable): _________________________________

Signature: _________________________________ Date: _____________

APPENDIX A: TESTING DETAILS

[Include detailed testing methodology, code/tools used, and raw results]

APPENDIX B: DATA DOCUMENTATION

[Include data dictionaries, sampling methodology, and data quality reports]

APPENDIX C: FAIRNESS METRIC CALCULATIONS

[Include detailed calculations and statistical analysis]

APPENDIX D: REMEDIATION TRACKING

Finding Remediation Action Status Completion Date Verification
[#] [ACTION] ☐ Open ☐ In Progress ☐ Complete [DATE] [METHOD]

This AI Bias Assessment Template is provided for informational purposes. Organizations should customize based on their specific systems, regulatory requirements, and legal counsel advice.

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About This Template

Compliance documents are what regulated businesses use to prove they follow the rules that apply to their industry, whether that is privacy, anti-money-laundering, consumer protection, or sector-specific requirements. Regulators look for consistent policies, up-to-date records, and clear evidence of employee training. The cost of getting compliance paperwork right is almost always smaller than the cost of an enforcement action, fine, or public disclosure.

Important Notice

This template is provided for informational purposes. It is not legal advice. We recommend having an attorney review any legal document before signing, especially for high-value or complex matters.

Last updated: February 2026

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