NoBias

Comprehensive AI fairness testing across datasets, models, and LLM agents. Detect bias before it reaches production.

What This Does

NoBias provides three auditing systems that cover the entire AI lifecycle:

1. Dataset Auditor - Scan training data for statistical biases (2-10 seconds, $0)
2. Model Auditor - Test trained models for fairness violations (15-60 seconds, $0)
3. Agent Auditor - Audit LLM-based agents for demographic bias (2-30 minutes, $0.03-$0.27)

All processing runs locally. Your data never leaves your machine unless you explicitly enable cloud LLM calls for Agent Auditor interpretation.

Installation

pip install unbiased

For full functionality including Agent Auditor:

pip install unbiased[all]

Quick Start

Dataset Auditor

Analyzes raw training data before model development. Catches representation imbalances, label disparities, proxy features, and intersectional bias.

from unbiased import audit_dataset

report = audit_dataset(
    data='hiring_data.csv',
    protected_attributes=['gender', 'race'],
    target_column='hired',
    positive_value=1
)

print(f"Severity: {report.overall_severity}")
print(f"EEOC Compliant: {report.eeoc_compliant}")

# Check specific findings
for finding in report.findings:
    if finding.severity == 'CRITICAL':
        print(f"⚠️ {finding.title}")
        print(f"   {finding.description}")

What it detects:

• Representation bias (underrepresented groups)
• Label bias (outcome disparities across demographics)
• Proxy features (ZIP code, names encoding protected attributes)
• Missing data patterns (systematic missingness by group)
• Intersectional disparities (compounded underrepresentation)
• Distribution shifts (KL divergence across groups)

Model Auditor

Tests trained ML models using counterfactual testing. Creates identical inputs differing only in demographics to see if predictions change.

from unbiased.model_audit import audit_model

report = audit_model(
    model='trained_model.pkl',
    test_data='test.csv',
    protected_attributes=['gender', 'race'],
    target_column='hired',
    positive_value=1
)

print(f"Flip Rate: {report.counterfactual_result.flip_rate:.2%}")
print(f"Disparate Impact Ratio: {report.scorecard['disparate_impact_gender'].value:.2f}")

# Check EEOC compliance
if report.scorecard['disparate_impact_gender'].value < 0.80:
    print("⚠️ EEOC VIOLATION: Disparate impact below 80% threshold")

What it computes:

• Counterfactual flip rate (individual fairness)
• Demographic parity (equal approval rates)
• Disparate impact ratio (EEOC 80% rule)
• Equalized odds (equal TPR/FPR across groups)
• Predictive parity (equal precision)
• Calibration (predicted probabilities match outcomes)

Agent Auditor

Tests LLM-based AI agents through persona-based counterfactual testing. Detects bias in hiring assistants, loan evaluators, content moderators, etc.

from unbiased.agent_audit import audit_agent

report = await audit_agent(
    system_prompt="You are a hiring assistant. Evaluate candidates objectively.",
    seed_case="Evaluate: Name: Jordan, Age: 29, Experience: 5 years, Skills: Python, ML",
    api_key="gsk_...",  # Groq API key
    mode="standard"  # quick | standard | full
)

print(f"CFR: {report.overall_cfr:.1%}")
print(f"EEOC Compliant: {report.eeoc_compliant}")
print(f"Severity: {report.overall_severity}")

# Get remediation suggestions
print(report.remediation_summary)

What it detects:

• Explicit demographic bias (agent sees "gender: Female" and changes decision)
• Implicit proxy bias (agent infers race from names like "Lakisha" vs "Emily")
• Contextual priming (historical context activates stereotypes)
• Reasoning inconsistencies (same decision, different justifications)

Three Connection Modes for Agent Auditor

Mode 1: System Prompt (Development)

Test agents during development by providing the system prompt directly.

from unbiased.agent_audit import AgentAuditor

auditor = AgentAuditor.from_prompt(
    system_prompt="You are a loan approval assistant...",
    api_key="gsk_...",
    model="llama-3.1-70b-versatile"
)

report = await auditor.run(
    seed_case="Evaluate: Income $55k, Credit 720, Employment 5yr"
)

Mode 2: API Endpoint (Production)

Test deployed production agents via their API.

auditor = AgentAuditor.from_api(
    endpoint_url="https://api.yourcompany.com/agent/evaluate",
    auth_header={"Authorization": "Bearer TOKEN"},
    request_template={"input": "{input}", "mode": "evaluation"},
    response_path="$.result.decision"
)

report = await auditor.run(seed_case="...")

Mode 3: Log Replay (Historical Analysis)

Audit past behavior from interaction logs. Zero API calls, complete privacy.

auditor = AgentAuditor.from_logs(
    log_file="interactions.jsonl",
    input_field="input",
    output_field="output"
)

report = await auditor.run()

Audit Tiers

Agent Auditor offers three tiers balancing cost and coverage:

Tier	Test Cases	Duration	Cost (Groq)	Cost (Anthropic)	Use Case
Quick	14 personas	2 min	$0.03	$0.11	Development testing
Standard	80 personas	5 min	$0.05	$0.17	Production validation
Full	430 personas	30 min	$0.07	$0.27	Legal compliance

# Quick scan for development
report = await audit_agent(..., mode="quick")

# Standard audit for production (default)
report = await audit_agent(..., mode="standard")

# Full investigation for legal compliance
report = await audit_agent(..., mode="full")

Cost Optimization

Agent Auditor includes 82% token reduction through four optimizations:

1. Compressed JSON Output - Structured responses instead of verbose text (85% reduction)
2. Prompt Caching - Reuse system prompts across calls (65% reduction after first call)
3. Two-Pass Evaluation - Only re-test ambiguous cases (50% fewer API calls)
4. Smart Sampling - Prioritize high-signal test cases

Before optimization: 240,000 tokens, $1.87 per audit After optimization: 43,400 tokens, $0.28 per audit

Local LLM Support

For complete privacy, use local LLMs via Ollama (zero cost, zero external calls):

from unbiased.agent_audit import audit_agent

report = await audit_agent(
    system_prompt="...",
    seed_case="...",
    backend="ollama",
    model="llama3.1:8b",
    mode="standard"
)

Requires Ollama installed locally. No data leaves your machine.

Understanding the Metrics

CFR (Counterfactual Flip Rate)

How often decisions flip when only demographics change. Research benchmarks (Mayilvaghanan et al. 2025):

• 0-5%: Negligible bias
• 5-10%: Below best-in-class, monitor
• 10-15%: Moderate, remediation recommended
• 15%+: Critical, immediate action required

MASD (Mean Absolute Score Difference)

Score shifts when only demographics change. Catches sub-threshold bias where decisions don't flip but scores systematically differ.

• 0.00-0.03: Perfect consistency
• 0.03-0.08: Detectable but minor
• 0.08-0.15: Meaningful difference
• 0.15+: Large systematic shifts

EEOC AIR (Adverse Impact Ratio)

Legal compliance with EEOC 80% rule. Formula: (lowest group approval rate) / (highest group approval rate)

• < 0.80: Legal violation (prima facie discrimination)
• 0.80-0.85: Borderline, review recommended

• 0.85: Compliant

Disparate Impact Ratio (DIR)

Same as EEOC AIR but for model/dataset audits. Measures approval rate ratios across demographic groups.

Actionable Insights

All audits generate plain-English summaries and prioritized remediation steps:

# Get actionable insights
insights = report.actionable_insights

print(insights['plain_english']['one_liner'])
print(insights['plain_english']['biggest_problem'])
print(insights['plain_english']['quickest_fix'])

# Prioritized actions
for action in insights['action_priority']:
    print(f"{action['rank']}. {action['action']}")
    print(f"   Effort: {action['effort']} | Impact: {action['impact']}")
    print(f"   {action['reason']}")

Export Formats

# JSON (machine-readable)
report.export('audit_report.json', format='json')

# PDF (human-readable)
report.export('audit_report.pdf', format='pdf')

# Text summary
report.export('audit_report.txt', format='text')

# CAFFE test suite (reproducible)
report.export('test_suite.json', format='caffe')

Advanced Usage

Compare Before/After

from unbiased.agent_audit import AgentAuditor

auditor = AgentAuditor.from_prompt(
    system_prompt="Original prompt...",
    api_key="..."
)

# Run initial audit
report_before = await auditor.run(seed_case="...")

# Update prompt with suggested fix
auditor.update_prompt("Improved prompt with fairness instructions...")

# Re-run audit
report_after = await auditor.run(seed_case="...")

# Compare
comparison = auditor.compare(report_before, report_after)
print(f"CFR improvement: {comparison.cfr_delta:.1%}")

Custom Protected Attributes

report = audit_dataset(
    data='data.csv',
    protected_attributes=['gender', 'race', 'age', 'disability_status'],
    target_column='approved',
    positive_value=1
)

Intersectional Analysis

# Automatically detects compounded bias at intersections
# e.g., Black women may face worse treatment than Black or women individually

for intersection in report.intersectional_findings:
    print(f"{intersection.groups}: {intersection.severity}")
    print(f"Expected rate: {intersection.expected_rate:.2%}")
    print(f"Actual rate: {intersection.actual_rate:.2%}")

Custom Personas for Agent Audit

from unbiased.agent_audit import audit_agent_with_personas

custom_personas = [
    {"name": "John Smith", "age": 35, "experience": 5},
    {"name": "Lakisha Washington", "age": 35, "experience": 5},
    {"name": "Wei Chen", "age": 35, "experience": 5},
]

report = await audit_agent_with_personas(
    system_prompt="...",
    personas=custom_personas,
    api_key="..."
)

Supported LLM Providers

Agent Auditor supports multiple providers:

• Groq - Fast and affordable (llama-3.1-70b-versatile, mixtral-8x7b)
• OpenAI - GPT-4o, GPT-4-turbo, GPT-3.5-turbo
• Anthropic - Claude 3.5 Sonnet, Claude 3 Opus
• Google Gemini - Gemini 1.5 Pro, Gemini Pro, Gemini Flash
• Ollama - Local models (llama3.1, mistral, etc.)

# Groq (default, fastest)
report = await audit_agent(..., backend="groq", model="llama-3.1-70b-versatile")

# OpenAI
report = await audit_agent(..., backend="openai", model="gpt-4o")

# Anthropic
report = await audit_agent(..., backend="anthropic", model="claude-3-5-sonnet-20241022")

# Gemini
report = await audit_agent(..., backend="gemini", model="gemini-1.5-pro")

# Ollama (local)
report = await audit_agent(..., backend="ollama", model="llama3.1:8b")

Integration Examples

CI/CD Pipeline

def validate_before_deployment():
    # Dataset check
    dataset_report = audit_dataset('training_data.csv', ...)
    if dataset_report.overall_severity == 'CRITICAL':
        raise ValueError("Dataset has critical bias - blocking deployment")
    
    # Model check
    model_report = audit_model('model.pkl', 'test.csv', ...)
    if not model_report.eeoc_compliant:
        raise ValueError("Model fails EEOC compliance - blocking deployment")
    
    # Agent check
    agent_report = await audit_agent(system_prompt="...", ...)
    if agent_report.overall_cfr > 0.15:
        raise ValueError("Agent CFR exceeds 15% - blocking deployment")

Monitoring Dashboard

import schedule

def daily_audit():
    report = await audit_agent(...)
    
    # Log metrics
    metrics = {
        'cfr': report.overall_cfr,
        'eeoc_compliant': report.eeoc_compliant,
        'severity': report.overall_severity,
        'timestamp': datetime.now()
    }
    
    log_to_dashboard(metrics)
    
    if report.overall_severity in ['CRITICAL', 'MODERATE']:
        send_alert(report)

schedule.every().day.at("02:00").do(daily_audit)

Performance

Auditor	Input Size	Duration	Cost	Resources
Dataset	10k rows	2-10 sec	$0	CPU, 200MB RAM
Model	2k samples	15-60 sec	$0	CPU, 500MB RAM
Agent (Quick)	14 personas	2 min	$0.03-$0.11	CPU, 300MB RAM
Agent (Standard)	80 personas	5 min	$0.05-$0.17	CPU, 300MB RAM
Agent (Full)	430 personas	30 min	$0.07-$0.27	CPU, 300MB RAM

Research Foundation

NoBias implements methods from peer-reviewed fairness research:

• CAFFE Framework (Cruciani et al. 2025) - Counterfactual fairness evaluation
• CFR/MASD Metrics (Mayilvaghanan et al. 2025) - Statistical bias detection
• Structured Reasoning (Nguyen et al. 2025) - Multi-agent bias interpretation
• Equality of Opportunity (Hardt et al. 2016) - Group fairness metrics
• EEOC Guidelines - 80% rule for disparate impact

Documentation

• Complete Presentation Guide
• Agent Audit Implementation
• Token Optimization
• API Reference
• Examples

Requirements

• Python 3.8+
• pandas >= 2.0.0
• numpy >= 1.24.0
• scipy >= 1.10.0
• scikit-learn >= 1.3.0

Optional dependencies installed with unbiased[all]:

• groq, openai, anthropic, google-generativeai (LLM providers)
• aiohttp (async HTTP)
• reportlab, matplotlib (PDF reports)
• sentence-transformers (embeddings)

License

MIT License

Citation

@software{unbiased2025,
  title={NoBias: Comprehensive AI Fairness Testing Platform},
  author={NoBias Team},
  year={2025},
  url={https://github.com/nobias/unbiased}
}