Bias Assessments
Review AI bias testing results and fairness measures.
Bias Assessments
Bias assessments help ensure AI operates fairly across different organizations, industries, and compliance scenarios. This page explains PartnerAlly's approach to AI fairness.
Why Bias Assessment Matters
AI systems can inadvertently:
- Favor certain document types
- Miss gaps in specific industries
- Under/over-weight certain frameworks
- Produce inconsistent results
Bias assessment is a continuous process, not a one-time check. We regularly evaluate and improve AI fairness.
Types of Bias We Monitor
Document Bias
Ensuring AI works well across:
- Different document formats
- Various writing styles
- Multiple languages (where supported)
- Different policy structures
Industry Bias
Preventing favoritism toward:
- Specific industries
- Organization sizes
- Business models
- Geographic regions
Framework Bias
Ensuring balanced treatment of:
- All supported frameworks
- Different control types
- Various compliance maturity levels
Confidence Calibration
Ensuring confidence scores are:
- Accurately calibrated
- Consistent across scenarios
- Not systematically over/under-confident
Viewing Bias Assessments
Go to AI Governance
Navigate to the AI Governance section.
Click "Bias Assessments"
Opens the assessment dashboard.
Review Latest Results
See current assessment status.
Assessment Dashboard
Overall Fairness Score
A summary metric showing:
- Overall bias assessment result
- Trend over time
- Areas of concern (if any)
Category Breakdown
| Category | What's Measured |
|---|---|
| Document Fairness | Performance across document types |
| Industry Fairness | Consistency across industries |
| Framework Fairness | Balance across frameworks |
| Confidence Accuracy | Calibration of confidence scores |
Trend Charts
See how fairness evolves:
- Historical scores
- Improvement trends
- Issue identification
Understanding Results
Score Interpretation
| Score Range | Meaning |
|---|---|
| 90-100% | Excellent fairness |
| 75-89% | Good, minor areas to watch |
| 50-74% | Moderate, improvements underway |
| Below 50% | Concerns, active mitigation |
Detailed Findings
For each category:
- Specific findings
- Data supporting the assessment
- Mitigation measures in place
- Improvement roadmap
Testing Methodology
How We Test
| Method | Purpose |
|---|---|
| Benchmark Datasets | Standard test cases across scenarios |
| A/B Testing | Compare model versions |
| User Feedback | Incorporate real-world corrections |
| Statistical Analysis | Identify systematic patterns |
Testing Frequency
- Major assessments: Quarterly
- Continuous monitoring: Ongoing
- After model updates: Always
- Ad-hoc investigations: As needed
Mitigation Measures
When Bias Is Found
If bias is identified:
Detection
Bias is identified through testing or feedback.
Analysis
Root cause is determined.
Mitigation
Corrective measures are implemented.
Validation
Improvement is verified.
Monitoring
Ongoing tracking ensures resolution.
Mitigation Types
| Type | Description |
|---|---|
| Training Data | Adjust data balance |
| Model Tuning | Modify model parameters |
| Post-Processing | Apply fairness corrections |
| Human Review | Add oversight for affected areas |
Bias mitigation is an ongoing process. As new scenarios emerge, continued monitoring and adjustment is required.
Your Organization's Data
How Your Data Is Used
Your organization's data:
- Is not used to train models without consent
- May contribute to anonymized statistics
- Is protected by data handling policies
- Remains under your control
Opt-Out Options
You can choose to:
- Exclude data from aggregate analysis
- Receive additional AI disclosures
- Request manual review of AI outputs
Reporting Bias Concerns
If You Suspect Bias
Report potential bias issues:
Document the Concern
Note what seems biased and why.
Report to Support
Use the feedback mechanism or contact support.
We Investigate
Reports are reviewed by our AI team.
Resolution
You're informed of findings and actions.
What to Include
When reporting:
- Specific examples
- Expected vs. actual behavior
- Patterns you've noticed
- Impact on your work
Fairness Commitments
Our Principles
We commit to:
- Regular, transparent bias assessment
- Prompt response to identified issues
- Continuous improvement
- Clear communication about limitations
Ongoing Work
Current focus areas:
- Expanding industry coverage
- Improving cross-framework consistency
- Enhancing confidence calibration
- Reducing document format sensitivity
Documentation for Audits
Available Documentation
For audit purposes:
- Latest bias assessment reports
- Testing methodology documentation
- Mitigation history
- Fairness monitoring approach
Requesting Documentation
To request detailed documentation:
- Contact your account team
- Specify audit requirements
- Receive relevant materials
- Schedule technical discussion if needed
Common Questions
How often are assessments performed?
- Full assessments: Quarterly
- Continuous monitoring: Always on
- After changes: Every model update
Can I see assessments specific to my industry?
Where sufficient data exists, industry-specific fairness metrics may be available. Contact support for details.
What if AI consistently gets something wrong for my organization?
Report it:
- We investigate organization-specific patterns
- May indicate unique factors
- Helps improve for similar organizations
- You may receive adjusted recommendations
Are assessment results audited externally?
We pursue independent reviews:
- Annual third-party assessments
- Results inform improvements
- Summary available on request
Next Steps
- Model Registry - Understand AI models
- Audit Trail - View AI activity
- Oversight Settings - Configure controls