Apply Composer changes: comprehensive API updates, migrations, middleware, and infrastructure improvements
- Add comprehensive database migrations (001-024) for schema evolution - Enhance API schema with expanded type definitions and resolvers - Add new middleware: audit logging, rate limiting, MFA enforcement, security, tenant auth - Implement new services: AI optimization, billing, blockchain, compliance, marketplace - Add adapter layer for cloud integrations (Cloudflare, Kubernetes, Proxmox, storage) - Update Crossplane provider with enhanced VM management capabilities - Add comprehensive test suite for API endpoints and services - Update frontend components with improved GraphQL subscriptions and real-time updates - Enhance security configurations and headers (CSP, CORS, etc.) - Update documentation and configuration files - Add new CI/CD workflows and validation scripts - Implement design system improvements and UI enhancements
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docs/fairness-audit/OUTPUT_WEIGHTS.md
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docs/fairness-audit/OUTPUT_WEIGHTS.md
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# Output Weight Guidelines
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## Overview
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Each output type in the fairness audit orchestration has a **weight** that represents the relative effort required to generate it. These weights are used to calculate the total output load (O) in the orchestration formula.
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## Weight Calculation
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Weights are determined by:
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1. **Processing Complexity**: How much computation is required?
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2. **Data Volume**: How much data needs to be processed/generated?
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3. **Format Complexity**: How complex is the output format?
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4. **Dependencies**: Does it depend on other outputs?
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## Output Types and Weights
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### Lightweight Outputs (0.5 - 1.5 units)
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#### Metrics Export (1.0 units)
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- **Type**: `metrics-export`
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- **Weight**: 1.0
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- **Description**: Statistical parity difference, true positive rate, false positive rate metrics
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- **Complexity**: Low - Simple calculations, small output
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- **Dependencies**: None
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- **Format**: JSON/CSV
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#### Alert Configuration (0.8 units)
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- **Type**: `alerts-config`
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- **Weight**: 0.8
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- **Description**: Automated alert rules for ongoing monitoring
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- **Complexity**: Low - Rule generation, small output
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- **Dependencies**: Metrics export
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- **Format**: YAML/JSON
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#### Detailed Report JSON (1.2 units)
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- **Type**: `detailed-report-json`
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- **Weight**: 1.2
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- **Description**: Machine-readable detailed fairness analysis
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- **Complexity**: Medium - Structured data, moderate size
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- **Dependencies**: All metrics
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- **Format**: JSON
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### Medium Outputs (1.5 - 2.0 units)
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#### Flagged Cases CSV (1.5 units)
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- **Type**: `flagged-cases-csv`
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- **Weight**: 1.5
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- **Description**: Export of cases flagged for potential bias issues
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- **Complexity**: Medium - Data filtering, CSV generation
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- **Dependencies**: Fairness evaluation
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- **Format**: CSV
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#### Executive Summary Slides (2.0 units)
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- **Type**: `exec-summary-slides`
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- **Weight**: 2.0
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- **Description**: Executive presentation slides with key findings
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- **Complexity**: Medium-High - Data aggregation, slide generation
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- **Dependencies**: All metrics, summary analysis
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- **Format**: PowerPoint/PDF
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#### Dashboard Export (1.8 units)
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- **Type**: `dashboard-export`
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- **Weight**: 1.8
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- **Description**: Interactive dashboard with fairness metrics
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- **Complexity**: Medium - Dashboard generation, visualization
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- **Dependencies**: All metrics
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- **Format**: HTML/Interactive
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### Heavy Outputs (2.0 - 3.0 units)
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#### Fairness Audit PDF (2.5 units)
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- **Type**: `fairness-audit-pdf`
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- **Weight**: 2.5
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- **Description**: Comprehensive fairness audit report in PDF format
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- **Complexity**: High - Full report generation, PDF formatting
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- **Dependencies**: All analyses, metrics, findings
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- **Format**: PDF
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#### Compliance Report (2.2 units)
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- **Type**: `compliance-report`
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- **Weight**: 2.2
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- **Description**: Regulatory compliance documentation
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- **Complexity**: High - Regulatory formatting, documentation
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- **Dependencies**: All analyses, audit trail
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- **Format**: PDF/DOCX
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## Weight Rationale
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### Why These Weights?
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1. **Metrics Export (1.0)**: Baseline weight
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- Simple calculations
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- Small output size
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- Fast generation
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2. **Alert Configuration (0.8)**: Lighter than baseline
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- Minimal processing
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- Small output
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- Can reuse metrics
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3. **Flagged Cases CSV (1.5)**: 50% more than baseline
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- Requires filtering logic
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- Moderate data volume
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- CSV generation overhead
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4. **Detailed Report JSON (1.2)**: Slightly above baseline
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- Structured data compilation
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- Moderate complexity
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- JSON serialization
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5. **Executive Summary Slides (2.0)**: 2× baseline
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- Data aggregation required
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- Slide generation complexity
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- Visual formatting
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6. **Dashboard Export (1.8)**: Between medium and heavy
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- Dashboard framework overhead
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- Visualization generation
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- Interactive components
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7. **Fairness Audit PDF (2.5)**: 2.5× baseline
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- Comprehensive report
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- PDF formatting complexity
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- Large output size
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8. **Compliance Report (2.2)**: Slightly less than PDF
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- Regulatory formatting
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- Documentation requirements
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- Structured output
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## Weight Adjustment Guidelines
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### When to Increase Weight
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- Output requires significant computation
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- Large data volumes
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- Complex formatting requirements
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- Multiple dependencies
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- Real-time processing needed
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### When to Decrease Weight
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- Simple calculations
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- Small output size
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- Reusable components
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- Cached results available
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- Parallel processing possible
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## Example Scenarios
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### Scenario 1: Minimal Outputs
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- Metrics Export (1.0)
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- Alert Configuration (0.8)
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- **Total**: 1.8 units
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### Scenario 2: Standard Audit
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- Fairness Audit PDF (2.5)
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- Metrics Export (1.0)
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- Flagged Cases CSV (1.5)
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- **Total**: 5.0 units
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### Scenario 3: Comprehensive Audit
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- All 8 outputs
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- **Total**: 13.0 units
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## Weight Validation
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### Design Target Check
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For input load I:
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- **Target Output**: O ≈ 1.2 × I
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- **Warning Threshold**: O > 1.5 × (1.2 × I)
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- **Example**: If I = 100, target O = 120, warn if O > 180
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### Total Load Check
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- **Expected**: Total ≈ 3.2 × I
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- **Warning**: Total > 1.3 × (3.2 × I)
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- **Example**: If I = 100, expected total = 320, warn if total > 416
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## Future Considerations
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1. **Dynamic Weights**: Adjust based on actual performance
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2. **Context-Aware**: Weights vary by dataset size
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3. **Machine Learning**: Learn optimal weights from history
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4. **Parallel Processing**: Reduce effective weights for parallel outputs
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## Related Documentation
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- [Orchestration Engine](./ORCHESTRATION_ENGINE.md)
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- [Orchestration Design](./ORCHESTRATION_DESIGN.md)
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