hips/HIPs/hip-0220-bias-detection-mitigation.md

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hip	title	tags	description	author	discussions-to	status	type	created	requires
220	Bias Detection & Mitigation	ai-ethics fairness bias evaluation	Framework for detecting, measuring, and mitigating bias in AI systems.	Hanzo AI Team (@hanzoai)	https://github.com/hanzoai/hips/discussions	Living	Meta	2025-12-17	HIP-0200, HIP-0201

HIP-0220: 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

Category	Characteristics
Demographic	Race, ethnicity, nationality, religion
Personal	Age, gender, sexual orientation, disability
Socioeconomic	Income, education, occupation
Geographic	Region, urban/rural, language

Bias Types

Pre-existing Bias

Type	Source	Example
Historical	Past discrimination in data	Hiring data reflecting past discrimination
Representation	Underrepresentation in data	Fewer examples of certain groups
Measurement	How data is collected	Sensors less accurate for certain skin tones

Technical Bias

Type	Source	Example
Aggregation	One-size-fits-all models	Medical model trained primarily on one population
Learning	Algorithm amplification	Feedback loops reinforcing initial bias
Evaluation	Biased benchmarks	Test sets not representative

Emergent Bias

Type	Source	Example
Deployment	Context mismatch	Model used in unintended population
Interaction	User behavior patterns	Different usage across groups
Temporal	Changing contexts	Society changes, model doesn't

Bias Detection

Detection Methods

Quantitative Analysis

Method	Application	Tools
Demographic parity	Equal prediction rates	Statistical analysis
Equalized odds	Equal TPR/FPR across groups	Fairlearn
Calibration	Consistent probability meaning	Reliability diagrams
Individual fairness	Similar treatment for similar individuals	Distance metrics

Qualitative Analysis

Method	Application	Approach
Output auditing	Review generated content	Human evaluation
Prompt testing	Test with demographic markers	Structured prompts
User studies	Perception of fairness	Surveys, interviews

Evaluation Datasets

Standard Benchmarks

Benchmark	Bias Type	Metrics
WinoBias	Gender	Accuracy parity
StereoSet	Stereotype	Language model score
CrowS-Pairs	Multiple	Preference score
BBQ	Social bias	Accuracy in ambiguous contexts

Custom Evaluation Sets

Dataset	Coverage	Size
Hanzo-Bias-1K	Multi-category bias probes	1,000+
Regional-Fairness	Geographic/cultural bias	500+
Intersectional-Set	Intersecting identities	300+

Audit Process

Pre-Deployment Audit

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

Ongoing Monitoring

Activity	Frequency	Scope
Automated metrics	Weekly	Key fairness metrics
Sample audits	Monthly	Human review sample
Full audit	Quarterly	Comprehensive evaluation

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

Metric	Definition	Target
Mention parity	Equal mention rates	Within 20%
Sentiment parity	Equal sentiment	No significant difference
Association	Co-occurrence patterns	No stereotypical clustering

Intersectional Analysis

Analyze bias at intersection of multiple characteristics:

Intersection	Analysis
Gender × Race	Check for compounded bias
Age × Disability	Assess unique patterns
Region × Language	Cultural intersections

Mitigation Strategies

Pre-Training Mitigations

Strategy	Application
Data balancing	Ensure representative training data
Data augmentation	Add underrepresented examples
Data filtering	Remove biased content
Source diversification	Include diverse data sources

Training Mitigations

Strategy	Application
Debiasing techniques	RLHF, constitutional AI
Regularization	Fairness constraints in loss
Adversarial training	Train against bias detectors
Multi-task learning	Include fairness objectives

Post-Training Mitigations

Strategy	Application
Output filtering	Flag biased outputs
Calibration	Adjust outputs for fairness
Prompt engineering	Include fairness instructions
Human review	Manual review of sensitive outputs

Deployment Mitigations

Strategy	Application
Use case restrictions	Limit high-risk applications
User warnings	Inform users of limitations
Feedback loops	Collect bias reports
A/B testing	Test mitigation effectiveness

Implementation

Bias Review Process

New Model Review

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

Periodic Review

Activity	Frequency	Owner
Metric monitoring	Weekly	MRM Team
Benchmark re-run	Monthly	Safety Team
Full audit	Quarterly	External + Internal
Process review	Annual	ESG Committee

Documentation Requirements

Fairness Card

Section	Contents
Groups evaluated	Demographic breakdown
Metrics used	Fairness metrics applied
Results	Metric values by group
Known limitations	Identified bias patterns
Mitigations applied	Steps taken to address

Threshold Standards

Model Risk Tier	Demographic Parity	Equalized Odds
Critical	<5% difference	<10% difference
High	<10% difference	<15% difference
Medium	<15% difference	<20% difference
Low	<20% difference	<25% difference

Governance

Fairness Oversight

Body	Role
Fairness Lead	Day-to-day oversight
Safety Team	Evaluation execution
ESG Committee	Policy and escalations
External Board	Independent review

Escalation Process

Trigger	Escalation
Threshold violation	Fairness Lead → MRM Lead
User complaint	Support → Fairness Lead
External report	Communications → ESG Committee
Regulatory inquiry	Legal → Board

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

Version	Date	Changes
1.0	2025-12-17	Initial draft

Copyright

Copyright and related rights waived via CC0.