HIP-220: Bias Detection & Mitigation

Abstract

This HIP establishes the framework for detecting, measuring, and mitigating bias in Hanzo AI systems. It defines bias categories, evaluation methodologies, fairness metrics, and remediation processes to ensure AI systems treat all users equitably.

Bias Framework

Definitions

Algorithmic bias: Systematic and repeatable errors in a computer system that create unfair outcomes for certain groups.

Fairness: The principle that AI systems should treat individuals and groups equitably, without discrimination based on protected characteristics.

Protected Characteristics

Bias Types

Pre-existing Bias

Technical Bias

Emergent Bias

Bias Detection

Detection Methods

Quantitative Analysis

Qualitative Analysis

Evaluation Datasets

Standard Benchmarks

Custom Evaluation Sets

Audit Process

Pre-Deployment Audit

1. Define evaluation scope (groups, use cases)
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2. Select appropriate metrics
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3. Run quantitative evaluation
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4. Conduct qualitative review
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5. Document findings
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6. Determine if thresholds met

Ongoing Monitoring

Fairness Metrics

Group Fairness Metrics

Demographic Parity

Definition: Prediction rates equal across groups

Formula:

P(Ŷ=1|A=a) = P(Ŷ=1|A=b) for all groups a, b

Threshold: <10% difference across groups

Equalized Odds

Definition: Equal true positive and false positive rates across groups

Formulas:

P(Ŷ=1|Y=1,A=a) = P(Ŷ=1|Y=1,A=b)  (Equal TPR)
P(Ŷ=1|Y=0,A=a) = P(Ŷ=1|Y=0,A=b)  (Equal FPR)

Threshold: <15% difference in TPR, <10% difference in FPR

Predictive Parity

Definition: Equal precision across groups

Formula:

P(Y=1|Ŷ=1,A=a) = P(Y=1|Ŷ=1,A=b)

Threshold: <10% difference across groups

Individual Fairness Metrics

Consistency

Definition: Similar individuals receive similar treatment

Formula:

|f(x) - f(x')| ≤ d(x, x') for similar x, x'

Language Model Metrics

Stereotype Score

Definition: Tendency to associate groups with stereotypes

Measurement: Compare likelihood of stereotypical vs. anti-stereotypical completions

Target: Score ≈ 50% (no preference)

Representation Metrics

Intersectional Analysis

Analyze bias at intersection of multiple characteristics:

Mitigation Strategies

Pre-Training Mitigations

Training Mitigations

Post-Training Mitigations

Deployment Mitigations

Implementation

Bias Review Process

New Model Review

1. Complete bias assessment questionnaire
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2. Run standard benchmark suite
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3. Conduct demographic analysis
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4. Document known limitations
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5. Implement mitigations
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6. Verify mitigation effectiveness
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7. Obtain fairness sign-off

Periodic Review

Documentation Requirements

Fairness Card

Threshold Standards

Governance

Fairness Oversight

Escalation Process

Continuous Improvement

• Track bias metrics over model versions
• Research new evaluation methods
• Engage with fairness research community
• Update thresholds based on capabilities

Related HIPs

• HIP-200: Responsible AI Principles
• HIP-201: Model Risk Management
• HIP-210: Safety Evaluation Framework
• HIP-230: AI Transparency & Explainability
• HIP-240: AI Incident Response

Changelog

Copyright

Copyright and related rights waived via CC0.