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feat:Enhanced the Bias Analyzer
This commit is contained in:
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# Enhanced Bias & Fairness Analysis Guide
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## Overview
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The Nordic Privacy AI platform now includes a comprehensive, adaptive bias and fairness analysis system that works accurately across **all types of datasets**, including:
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- Small datasets (< 100 samples)
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- Imbalanced groups
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- Multiple protected attributes
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- Binary and multi-class targets
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- High-cardinality features
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- Missing data
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## Key Enhancements
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### 1. **Adaptive Fairness Thresholds**
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The system automatically adjusts fairness thresholds based on dataset characteristics:
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- **Sample Size Factor**: Relaxes thresholds for small sample sizes
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- **Group Imbalance Factor**: Adjusts for unequal group sizes
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- **Dynamic Thresholds**:
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- Disparate Impact: 0.7-0.8 (adapts to data)
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- Statistical Parity: 0.1-0.15 (adapts to data)
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- Equal Opportunity: 0.1-0.15 (adapts to data)
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### 2. **Comprehensive Fairness Metrics**
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#### Individual Metrics (6 types analyzed):
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1. **Disparate Impact Ratio** (4/5ths rule)
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- Measures: min_rate / max_rate across all groups
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- Fair range: 0.8 - 1.25 (or adaptive)
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- Higher weight in overall score
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2. **Statistical Parity Difference**
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- Measures: Absolute difference in positive rates
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- Fair threshold: < 0.1 (or adaptive)
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- Ensures equal selection rates
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3. **Equal Opportunity** (TPR equality)
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- Measures: Difference in True Positive Rates
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- Fair threshold: < 0.1 (or adaptive)
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- Ensures equal recall across groups
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4. **Equalized Odds** (TPR + FPR equality)
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- Measures: Both TPR and FPR differences
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- Fair threshold: < 0.1 (or adaptive)
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- Most comprehensive fairness criterion
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5. **Predictive Parity** (Precision equality)
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- Measures: Difference in precision across groups
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- Fair threshold: < 0.1
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- Ensures positive predictions are equally accurate
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6. **Calibration** (FNR equality)
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- Measures: Difference in False Negative Rates
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- Fair threshold: < 0.1
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- Ensures balanced error rates
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#### Group-Level Metrics (per demographic group):
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- Positive Rate
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- Selection Rate
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- True Positive Rate (TPR/Recall/Sensitivity)
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- False Positive Rate (FPR)
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- True Negative Rate (TNR/Specificity)
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- False Negative Rate (FNR)
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- Precision (PPV)
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- F1 Score
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- Accuracy
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- Sample Size & Distribution
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### 3. **Weighted Bias Scoring**
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The overall bias score (0-1, higher = more bias) is calculated using:
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```python
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Overall Score = Weighted Average of:
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- Disparate Impact (weight: 1.5x sample_weight)
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- Statistical Parity (weight: 1.0x sample_weight)
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- Equal Opportunity (weight: 1.0x sample_weight)
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- Equalized Odds (weight: 0.8x sample_weight)
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- Predictive Parity (weight: 0.7x sample_weight)
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- Calibration (weight: 0.7x sample_weight)
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```
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Sample weight = min(1.0, total_samples / 100)
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### 4. **Intelligent Violation Detection**
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Violations are categorized by severity:
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- **CRITICAL**: di_value < 0.5, or deviation > 50%
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- **HIGH**: di_value < 0.6, or deviation > 30%
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- **MEDIUM**: di_value < 0.7, or deviation > 15%
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- **LOW**: Minor deviations
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Each violation includes:
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- Affected groups
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- Specific measurements
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- Actionable recommendations
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- Context-aware severity assessment
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### 5. **Robust Data Handling**
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#### Missing Values:
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- Numerical: Filled with median
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- Categorical: Filled with mode or 'Unknown'
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- Comprehensive logging
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#### Data Type Detection:
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- Binary detection (0/1, Yes/No)
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- Small discrete values (< 10 unique)
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- High cardinality warnings (> 50 categories)
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- Mixed type handling
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#### Target Encoding:
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- Automatic categorical → numeric conversion
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- Binary value normalization
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- Clear encoding maps printed
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#### Class Imbalance:
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- Stratified splitting when appropriate
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- Minimum class size validation
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- Balanced metrics calculation
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### 6. **Enhanced Reporting**
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Each analysis includes:
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```json
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{
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"overall_bias_score": 0.954,
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"fairness_metrics": {
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"Gender": {
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"disparate_impact": {
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"value": 0.276,
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"threshold": 0.8,
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"fair": false,
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"min_group": "Female",
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"max_group": "Male",
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"min_rate": 0.25,
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"max_rate": 0.906
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},
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"statistical_parity_difference": {...},
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"equal_opportunity_difference": {...},
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"equalized_odds": {...},
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"predictive_parity": {...},
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"calibration": {...},
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"attribute_fairness_score": 0.89,
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"group_metrics": {
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"Male": {
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"positive_rate": 0.906,
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"tpr": 0.95,
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"fpr": 0.03,
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"precision": 0.92,
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"f1_score": 0.93,
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"sample_size": 450
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},
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"Female": {...}
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},
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"sample_statistics": {
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"total_samples": 500,
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"min_group_size": 50,
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"max_group_size": 450,
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"imbalance_ratio": 0.11,
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"num_groups": 2
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}
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}
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},
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"fairness_violations": [
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{
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"attribute": "Gender",
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"metric": "Disparate Impact",
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"severity": "CRITICAL",
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"value": 0.276,
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"affected_groups": ["Female", "Male"],
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"message": "...",
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"recommendation": "CRITICAL: Group 'Female' has less than half the approval rate..."
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}
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]
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}
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```
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## Usage Examples
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### Basic Analysis
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```python
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from ai_governance import AIGovernanceAnalyzer
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# Initialize
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analyzer = AIGovernanceAnalyzer()
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# Analyze with protected attributes
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report = analyzer.analyze(
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df=your_dataframe,
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target_column='ApprovalStatus',
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protected_attributes=['Gender', 'Age', 'Race']
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)
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# Check bias score
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print(f"Bias Score: {report['bias_analysis']['overall_bias_score']:.1%}")
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# Review violations
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for violation in report['bias_analysis']['fairness_violations']:
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print(f"{violation['severity']}: {violation['message']}")
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```
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### With Presidio (Enhanced PII Detection)
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```python
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# Enable Presidio for automatic demographic detection
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analyzer = AIGovernanceAnalyzer(use_presidio=True)
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```
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### API Usage
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```bash
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curl -X POST http://localhost:8000/api/analyze \
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-F "file=@dataset.csv" \
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-F "target_column=Outcome" \
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-F "protected_attributes=Gender,Age"
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```
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## Interpreting Results
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### Overall Bias Score
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- **< 0.3**: Low bias - Excellent fairness ✅
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- **0.3 - 0.5**: Moderate bias - Monitor recommended ⚠️
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- **> 0.5**: High bias - Action required ❌
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### Disparate Impact
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- **0.8 - 1.25**: Fair (4/5ths rule satisfied)
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- **< 0.8**: Disadvantaged group exists
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- **> 1.25**: Advantaged group exists
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### Statistical Parity
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- **< 0.1**: Fair (similar positive rates)
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- **> 0.1**: Groups receive different treatment
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### Recommendations by Severity
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#### CRITICAL
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- **DO NOT DEPLOY** without remediation
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- Investigate systemic bias sources
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- Review training data representation
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- Implement fairness constraints
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- Consider re-collection if necessary
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#### HIGH
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- Address before deployment
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- Use fairness-aware training methods
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- Implement threshold optimization
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- Regular monitoring required
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#### MEDIUM
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- Monitor closely
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- Consider mitigation strategies
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- Regular fairness audits
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- Document findings
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#### LOW
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- Continue monitoring
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- Maintain fairness standards
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- Periodic reviews
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## Best Practices
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### 1. Data Collection
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- Ensure representative sampling
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- Balance protected groups when possible
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- Document data sources
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- Check for historical bias
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### 2. Feature Engineering
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- Avoid proxy features for protected attributes
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- Check feature correlations with demographics
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- Use feature importance analysis
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- Consider fairness-aware feature selection
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### 3. Model Training
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- Use fairness-aware algorithms
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- Implement fairness constraints
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- Try multiple fairness definitions
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- Cross-validate with fairness metrics
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### 4. Post-Processing
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- Threshold optimization per group
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- Calibration techniques
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- Reject option classification
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- Regular bias audits
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### 5. Monitoring
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- Track fairness metrics over time
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- Monitor for fairness drift
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- Regular re-evaluation
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- Document all findings
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## Technical Details
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### Dependencies
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```
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numpy>=1.21.0
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pandas>=1.3.0
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scikit-learn>=1.0.0
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presidio-analyzer>=2.2.0 # Optional
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spacy>=3.0.0 # Optional for Presidio
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```
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### Performance
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- Handles datasets from 50 to 1M+ rows
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- Adaptive algorithms scale with data size
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- Memory-efficient group comparisons
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- Parallel metric calculations
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### Limitations
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- Requires at least 2 groups per protected attribute
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- Minimum 10 samples per group recommended
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- Binary classification focus (multi-class supported)
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- Assumes independent test set
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## Troubleshooting
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### "Insufficient valid groups"
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- Check protected attribute has at least 2 non-null groups
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- Ensure groups appear in test set
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- Increase test_size parameter
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### "High cardinality warning"
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- Feature has > 50 unique values
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- Consider grouping categories
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- May need feature engineering
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### "Sample size too small"
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- System adapts automatically
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- Results may be less reliable
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- Consider collecting more data
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### "Presidio initialization failed"
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- Install: `pip install presidio-analyzer spacy`
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- Download model: `python -m spacy download en_core_web_sm`
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- Or use `use_presidio=False`
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## References
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- [Fairness Definitions Explained](https://fairware.cs.umass.edu/papers/Verma.pdf)
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- [4/5ths Rule (EEOC)](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)
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- [Equalized Odds](https://arxiv.org/abs/1610.02413)
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- [Fairness Through Awareness](https://arxiv.org/abs/1104.3913)
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## Support
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For issues or questions:
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- Check logs for detailed diagnostic messages
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- Review sample statistics in output
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- Consult violation recommendations
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- Contact: support@nordicprivacyai.com
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@@ -98,13 +98,14 @@ class AIGovernanceAnalyzer:
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)
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)
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bias_results = self.bias_analyzer.analyze()
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bias_results = self.bias_analyzer.analyze()
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# Step 4: Assess risks
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# Step 4: Assess risks with Presidio-enhanced detection
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self.risk_analyzer = RiskAnalyzer(
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self.risk_analyzer = RiskAnalyzer(
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self.processor.df,
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self.processor.df,
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self.trainer.results,
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self.trainer.results,
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bias_results,
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bias_results,
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self.processor.protected_attributes,
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self.processor.protected_attributes,
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self.processor.target_column
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self.processor.target_column,
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use_presidio=False # Set to True after installing: python -m spacy download en_core_web_sm
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)
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)
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risk_results = self.risk_analyzer.analyze()
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risk_results = self.risk_analyzer.analyze()
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@@ -49,26 +49,43 @@ class BiasAnalyzer:
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try:
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try:
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print("⏳ Initializing Presidio analyzer (first time only)...")
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print("⏳ Initializing Presidio analyzer (first time only)...")
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# Check if spaCy model is available
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# Check if spaCy and model are available
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try:
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try:
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import spacy
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import spacy
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try:
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spacy.load("en_core_web_sm")
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# Check if model exists WITHOUT loading it first
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except OSError:
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model_name = "en_core_web_sm"
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print("⚠️ spaCy model 'en_core_web_sm' not found. Run: python -m spacy download en_core_web_sm")
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if not spacy.util.is_package(model_name):
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print(f"⚠️ spaCy model '{model_name}' not found.")
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print(f" To enable Presidio, install the model with:")
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print(f" python -m spacy download {model_name}")
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print(" Continuing without Presidio-enhanced detection...")
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BiasAnalyzer._presidio_init_failed = True
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BiasAnalyzer._presidio_init_failed = True
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return
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return
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# Model exists, now load it
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print(f"✓ spaCy model '{model_name}' found, loading...")
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nlp = spacy.load(model_name)
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except ImportError:
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except ImportError:
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print("⚠️ spaCy not installed. Install with: pip install spacy")
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print("⚠️ spaCy not installed. Install with: pip install spacy")
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BiasAnalyzer._presidio_init_failed = True
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BiasAnalyzer._presidio_init_failed = True
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return
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return
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except Exception as e:
|
||||||
|
print(f"⚠️ Error loading spaCy model: {e}")
|
||||||
|
print(" Continuing without Presidio-enhanced detection...")
|
||||||
|
BiasAnalyzer._presidio_init_failed = True
|
||||||
|
return
|
||||||
|
|
||||||
# Create NLP engine
|
# Create NLP engine configuration (prevent auto-download)
|
||||||
provider = NlpEngineProvider()
|
from presidio_analyzer.nlp_engine import NlpEngineProvider
|
||||||
nlp_configuration = {
|
|
||||||
|
configuration = {
|
||||||
"nlp_engine_name": "spacy",
|
"nlp_engine_name": "spacy",
|
||||||
"models": [{"lang_code": "en", "model_name": "en_core_web_sm"}]
|
"models": [{"lang_code": "en", "model_name": model_name}],
|
||||||
}
|
}
|
||||||
|
|
||||||
|
provider = NlpEngineProvider(nlp_configuration=configuration)
|
||||||
nlp_engine = provider.create_engine()
|
nlp_engine = provider.create_engine()
|
||||||
|
|
||||||
# Initialize analyzer
|
# Initialize analyzer
|
||||||
|
|||||||
File diff suppressed because it is too large
Load Diff
@@ -715,28 +715,305 @@ export function CenterPanel({ tab, onAnalyze }: CenterPanelProps) {
|
|||||||
);
|
);
|
||||||
case "risk-analysis":
|
case "risk-analysis":
|
||||||
return (
|
return (
|
||||||
<div className="space-y-4">
|
<div className="space-y-6">
|
||||||
<h2 className="text-xl font-semibold">Risk Analysis</h2>
|
<h2 className="text-2xl font-bold text-slate-800">🔒 Risk Analysis</h2>
|
||||||
{analyzeResult ? (
|
{analyzeResult ? (
|
||||||
<div className="space-y-4">
|
<div className="space-y-6">
|
||||||
<div className="p-4 bg-white rounded-lg border">
|
{/* Overall Risk Score Card */}
|
||||||
<div className="text-sm text-slate-600">Overall Risk Score</div>
|
<div className="relative overflow-hidden rounded-xl border-2 border-slate-200 bg-gradient-to-br from-slate-50 via-white to-slate-50 p-6 shadow-lg">
|
||||||
<div className="text-2xl font-bold">{(analyzeResult.risk_assessment.overall_risk_score * 100).toFixed(1)}%</div>
|
<div className="absolute top-0 right-0 w-32 h-32 bg-gradient-to-br from-red-500/5 to-orange-500/5 rounded-full blur-3xl"></div>
|
||||||
|
<div className="relative">
|
||||||
|
<div className="flex items-center justify-between mb-4">
|
||||||
|
<div>
|
||||||
|
<div className="text-sm font-medium text-slate-600 mb-1">Overall Risk Score</div>
|
||||||
|
<div className="text-5xl font-bold bg-gradient-to-r from-red-600 to-orange-600 bg-clip-text text-transparent">
|
||||||
|
{(analyzeResult.risk_assessment.overall_risk_score * 100).toFixed(1)}%
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<div className={`px-4 py-2 rounded-full text-sm font-bold ${
|
||||||
|
analyzeResult.risk_assessment.risk_level === 'CRITICAL' ? 'bg-red-100 text-red-700' :
|
||||||
|
analyzeResult.risk_assessment.risk_level === 'HIGH' ? 'bg-orange-100 text-orange-700' :
|
||||||
|
analyzeResult.risk_assessment.risk_level === 'MEDIUM' ? 'bg-yellow-100 text-yellow-700' :
|
||||||
|
'bg-green-100 text-green-700'
|
||||||
|
}`}>
|
||||||
|
{analyzeResult.risk_assessment.risk_level} RISK
|
||||||
|
</div>
|
||||||
</div>
|
</div>
|
||||||
|
|
||||||
{cleanResult && (
|
{analyzeResult.risk_assessment.presidio_enabled && (
|
||||||
<div className="p-4 bg-white rounded-lg border">
|
<div className="inline-flex items-center gap-2 px-3 py-1.5 bg-blue-50 border border-blue-200 rounded-lg text-xs font-medium text-blue-700">
|
||||||
<h3 className="font-semibold mb-2">PII Detection Results</h3>
|
<span className="w-2 h-2 bg-blue-500 rounded-full animate-pulse"></span>
|
||||||
<div className="text-sm space-y-1">
|
Presidio-Enhanced Detection
|
||||||
<div>Cells Anonymized: <span className="font-medium">{cleanResult.summary.total_cells_affected}</span></div>
|
</div>
|
||||||
<div>Columns Removed: <span className="font-medium">{cleanResult.summary.columns_removed.length}</span></div>
|
)}
|
||||||
<div>Columns Anonymized: <span className="font-medium">{cleanResult.summary.columns_anonymized.length}</span></div>
|
</div>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
{/* Risk Categories Grid */}
|
||||||
|
<div className="grid grid-cols-2 md:grid-cols-3 gap-4">
|
||||||
|
{Object.entries(analyzeResult.risk_assessment.risk_categories || {}).map(([category, score]: [string, any]) => {
|
||||||
|
const riskPct = (score * 100);
|
||||||
|
const riskLevel = riskPct >= 70 ? 'CRITICAL' : riskPct >= 50 ? 'HIGH' : riskPct >= 30 ? 'MEDIUM' : 'LOW';
|
||||||
|
const categoryIcons: Record<string, string> = {
|
||||||
|
privacy: '🔒',
|
||||||
|
ethical: '⚖️',
|
||||||
|
compliance: '📋',
|
||||||
|
security: '🛡️',
|
||||||
|
operational: '⚙️',
|
||||||
|
data_quality: '📊'
|
||||||
|
};
|
||||||
|
|
||||||
|
return (
|
||||||
|
<div key={category} className={`relative overflow-hidden rounded-lg border-2 p-4 transition-all hover:shadow-md ${
|
||||||
|
riskLevel === 'CRITICAL' ? 'border-red-200 bg-gradient-to-br from-red-50 to-white' :
|
||||||
|
riskLevel === 'HIGH' ? 'border-orange-200 bg-gradient-to-br from-orange-50 to-white' :
|
||||||
|
riskLevel === 'MEDIUM' ? 'border-yellow-200 bg-gradient-to-br from-yellow-50 to-white' :
|
||||||
|
'border-green-200 bg-gradient-to-br from-green-50 to-white'
|
||||||
|
}`}>
|
||||||
|
<div className="flex items-start justify-between mb-2">
|
||||||
|
<div className="text-2xl">{categoryIcons[category] || '📌'}</div>
|
||||||
|
<span className={`text-xs font-bold px-2 py-1 rounded ${
|
||||||
|
riskLevel === 'CRITICAL' ? 'bg-red-100 text-red-700' :
|
||||||
|
riskLevel === 'HIGH' ? 'bg-orange-100 text-orange-700' :
|
||||||
|
riskLevel === 'MEDIUM' ? 'bg-yellow-100 text-yellow-700' :
|
||||||
|
'bg-green-100 text-green-700'
|
||||||
|
}`}>
|
||||||
|
{riskLevel}
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
<div className="text-sm font-semibold text-slate-700 capitalize mb-1">
|
||||||
|
{category.replace('_', ' ')}
|
||||||
|
</div>
|
||||||
|
<div className="text-2xl font-bold text-slate-800">
|
||||||
|
{riskPct.toFixed(0)}%
|
||||||
|
</div>
|
||||||
|
<div className="mt-2 h-1.5 bg-slate-200 rounded-full overflow-hidden">
|
||||||
|
<div
|
||||||
|
className={`h-full rounded-full transition-all ${
|
||||||
|
riskLevel === 'CRITICAL' ? 'bg-gradient-to-r from-red-500 to-red-600' :
|
||||||
|
riskLevel === 'HIGH' ? 'bg-gradient-to-r from-orange-500 to-orange-600' :
|
||||||
|
riskLevel === 'MEDIUM' ? 'bg-gradient-to-r from-yellow-500 to-yellow-600' :
|
||||||
|
'bg-gradient-to-r from-green-500 to-green-600'
|
||||||
|
}`}
|
||||||
|
style={{ width: `${Math.min(riskPct, 100)}%` }}
|
||||||
|
></div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
);
|
||||||
|
})}
|
||||||
|
</div>
|
||||||
|
|
||||||
|
{/* Privacy Risks - PII Detection */}
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks && (
|
||||||
|
<div className="bg-white rounded-xl border-2 border-slate-200 p-6 shadow-sm">
|
||||||
|
<div className="flex items-center gap-2 mb-4">
|
||||||
|
<span className="text-2xl">🔒</span>
|
||||||
|
<h3 className="text-lg font-bold text-slate-800">Privacy Risks</h3>
|
||||||
|
<span className="ml-auto px-3 py-1 bg-slate-100 text-slate-700 rounded-full text-xs font-semibold">
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks.pii_count} PII Types Detected
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
{/* PII Detections */}
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks.pii_detected &&
|
||||||
|
analyzeResult.risk_assessment.privacy_risks.pii_detected.length > 0 ? (
|
||||||
|
<div className="space-y-3">
|
||||||
|
<div className="grid grid-cols-1 md:grid-cols-2 gap-3">
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks.pii_detected.slice(0, 6).map((pii: any, idx: number) => (
|
||||||
|
<div key={idx} className={`p-3 rounded-lg border-2 ${
|
||||||
|
pii.severity === 'CRITICAL' ? 'bg-red-50 border-red-200' :
|
||||||
|
pii.severity === 'HIGH' ? 'bg-orange-50 border-orange-200' :
|
||||||
|
pii.severity === 'MEDIUM' ? 'bg-yellow-50 border-yellow-200' :
|
||||||
|
'bg-blue-50 border-blue-200'
|
||||||
|
}`}>
|
||||||
|
<div className="flex items-center justify-between mb-1">
|
||||||
|
<span className="text-xs font-bold text-slate-600">
|
||||||
|
{pii.column}
|
||||||
|
</span>
|
||||||
|
<span className={`text-xs font-bold px-2 py-0.5 rounded ${
|
||||||
|
pii.severity === 'CRITICAL' ? 'bg-red-100 text-red-700' :
|
||||||
|
pii.severity === 'HIGH' ? 'bg-orange-100 text-orange-700' :
|
||||||
|
pii.severity === 'MEDIUM' ? 'bg-yellow-100 text-yellow-700' :
|
||||||
|
'bg-blue-100 text-blue-700'
|
||||||
|
}`}>
|
||||||
|
{pii.severity}
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
<div className="text-sm font-semibold text-slate-800">
|
||||||
|
{pii.type}
|
||||||
|
</div>
|
||||||
|
<div className="text-xs text-slate-600 mt-1">
|
||||||
|
Detected via: {pii.detection_method}
|
||||||
|
{pii.confidence && ` (${(pii.confidence * 100).toFixed(0)}% confidence)`}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
))}
|
||||||
|
</div>
|
||||||
|
|
||||||
|
{/* Privacy Metrics */}
|
||||||
|
<div className="grid grid-cols-2 md:grid-cols-4 gap-3 pt-3 border-t border-slate-200">
|
||||||
|
<div className="text-center p-3 bg-slate-50 rounded-lg">
|
||||||
|
<div className="text-xs text-slate-600 mb-1">Re-ID Risk</div>
|
||||||
|
<div className="text-lg font-bold text-slate-800">
|
||||||
|
{(analyzeResult.risk_assessment.privacy_risks.reidentification_risk * 100).toFixed(0)}%
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<div className="text-center p-3 bg-slate-50 rounded-lg">
|
||||||
|
<div className="text-xs text-slate-600 mb-1">Data Minimization</div>
|
||||||
|
<div className="text-lg font-bold text-slate-800">
|
||||||
|
{(analyzeResult.risk_assessment.privacy_risks.data_minimization_score * 100).toFixed(0)}%
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<div className="text-center p-3 bg-slate-50 rounded-lg">
|
||||||
|
<div className="text-xs text-slate-600 mb-1">Anonymization</div>
|
||||||
|
<div className="text-sm font-bold text-slate-800">
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks.anonymization_level}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
<div className="text-center p-3 bg-slate-50 rounded-lg">
|
||||||
|
<div className="text-xs text-slate-600 mb-1">Detection</div>
|
||||||
|
<div className="text-sm font-bold text-slate-800">
|
||||||
|
{analyzeResult.risk_assessment.privacy_risks.detection_method}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
) : (
|
||||||
|
<div className="text-sm text-slate-600 bg-green-50 border border-green-200 rounded-lg p-3">
|
||||||
|
✓ No PII detected in the dataset
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
|
||||||
|
{/* Violations */}
|
||||||
|
{analyzeResult.risk_assessment.violations &&
|
||||||
|
analyzeResult.risk_assessment.violations.length > 0 && (
|
||||||
|
<div className="bg-white rounded-xl border-2 border-slate-200 p-6 shadow-sm">
|
||||||
|
<div className="flex items-center gap-2 mb-4">
|
||||||
|
<span className="text-2xl">⚠️</span>
|
||||||
|
<h3 className="text-lg font-bold text-slate-800">Risk Violations</h3>
|
||||||
|
<span className="ml-auto px-3 py-1 bg-red-100 text-red-700 rounded-full text-xs font-semibold">
|
||||||
|
{analyzeResult.risk_assessment.violations.length} Issues
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
<div className="space-y-3">
|
||||||
|
{analyzeResult.risk_assessment.violations.map((violation: any, idx: number) => (
|
||||||
|
<div key={idx} className={`p-4 rounded-lg border-2 ${
|
||||||
|
violation.severity === 'CRITICAL' ? 'bg-red-50 border-red-200' :
|
||||||
|
violation.severity === 'HIGH' ? 'bg-orange-50 border-orange-200' :
|
||||||
|
violation.severity === 'MEDIUM' ? 'bg-yellow-50 border-yellow-200' :
|
||||||
|
'bg-blue-50 border-blue-200'
|
||||||
|
}`}>
|
||||||
|
<div className="flex items-start justify-between gap-3">
|
||||||
|
<div className="flex-1">
|
||||||
|
<div className="flex items-center gap-2 mb-1">
|
||||||
|
<span className={`text-xs font-bold px-2 py-1 rounded ${
|
||||||
|
violation.severity === 'CRITICAL' ? 'bg-red-100 text-red-700' :
|
||||||
|
violation.severity === 'HIGH' ? 'bg-orange-100 text-orange-700' :
|
||||||
|
violation.severity === 'MEDIUM' ? 'bg-yellow-100 text-yellow-700' :
|
||||||
|
'bg-blue-100 text-blue-700'
|
||||||
|
}`}>
|
||||||
|
{violation.severity}
|
||||||
|
</span>
|
||||||
|
<span className="text-xs font-semibold text-slate-600 uppercase">
|
||||||
|
{violation.category}
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
<div className="text-sm font-semibold text-slate-800 mb-1">
|
||||||
|
{violation.message}
|
||||||
|
</div>
|
||||||
|
{violation.details && (
|
||||||
|
<div className="text-xs text-slate-600">
|
||||||
|
{violation.details}
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
))}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
|
||||||
|
{/* Key Insights */}
|
||||||
|
{analyzeResult.risk_assessment.insights &&
|
||||||
|
analyzeResult.risk_assessment.insights.length > 0 && (
|
||||||
|
<div className="bg-gradient-to-br from-blue-50 to-indigo-50 rounded-xl border-2 border-blue-200 p-6 shadow-sm">
|
||||||
|
<div className="flex items-center gap-2 mb-4">
|
||||||
|
<span className="text-2xl">💡</span>
|
||||||
|
<h3 className="text-lg font-bold text-slate-800">Key Insights</h3>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
<div className="space-y-2">
|
||||||
|
{analyzeResult.risk_assessment.insights.map((insight: string, idx: number) => (
|
||||||
|
<div key={idx} className="flex items-start gap-2 text-sm text-slate-700">
|
||||||
|
<span className="text-blue-600 mt-0.5">•</span>
|
||||||
|
<span>{insight}</span>
|
||||||
|
</div>
|
||||||
|
))}
|
||||||
|
</div>
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
|
||||||
|
{/* Compliance Status */}
|
||||||
|
{analyzeResult.risk_assessment.compliance_risks && (
|
||||||
|
<div className="bg-white rounded-xl border-2 border-slate-200 p-6 shadow-sm">
|
||||||
|
<div className="flex items-center gap-2 mb-4">
|
||||||
|
<span className="text-2xl">📋</span>
|
||||||
|
<h3 className="text-lg font-bold text-slate-800">Compliance Status</h3>
|
||||||
|
</div>
|
||||||
|
|
||||||
|
<div className="grid grid-cols-1 md:grid-cols-2 gap-4">
|
||||||
|
{Object.entries(analyzeResult.risk_assessment.compliance_risks)
|
||||||
|
.filter(([key]) => ['gdpr', 'ccpa', 'hipaa', 'ecoa'].includes(key))
|
||||||
|
.map(([regulation, data]: [string, any]) => {
|
||||||
|
if (!data || typeof data !== 'object') return null;
|
||||||
|
|
||||||
|
return (
|
||||||
|
<div key={regulation} className={`p-4 rounded-lg border-2 ${
|
||||||
|
data.status === 'COMPLIANT' ? 'bg-green-50 border-green-200' :
|
||||||
|
data.status === 'PARTIAL' ? 'bg-yellow-50 border-yellow-200' :
|
||||||
|
data.status === 'NOT_APPLICABLE' ? 'bg-slate-50 border-slate-200' :
|
||||||
|
'bg-red-50 border-red-200'
|
||||||
|
}`}>
|
||||||
|
<div className="flex items-center justify-between mb-2">
|
||||||
|
<span className="text-sm font-bold text-slate-800 uppercase">
|
||||||
|
{regulation}
|
||||||
|
</span>
|
||||||
|
<span className={`text-xs font-bold px-2 py-1 rounded ${
|
||||||
|
data.status === 'COMPLIANT' ? 'bg-green-100 text-green-700' :
|
||||||
|
data.status === 'PARTIAL' ? 'bg-yellow-100 text-yellow-700' :
|
||||||
|
data.status === 'NOT_APPLICABLE' ? 'bg-slate-100 text-slate-700' :
|
||||||
|
'bg-red-100 text-red-700'
|
||||||
|
}`}>
|
||||||
|
{data.status}
|
||||||
|
</span>
|
||||||
|
</div>
|
||||||
|
{data.score !== undefined && (
|
||||||
|
<div className="text-xs text-slate-600 mb-2">
|
||||||
|
Compliance Score: {(data.score * 100).toFixed(0)}%
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
{data.applicable === false && (
|
||||||
|
<div className="text-xs text-slate-600">
|
||||||
|
Not applicable to this dataset
|
||||||
|
</div>
|
||||||
|
)}
|
||||||
|
</div>
|
||||||
|
);
|
||||||
|
})}
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
)}
|
)}
|
||||||
</div>
|
</div>
|
||||||
) : (
|
) : (
|
||||||
<p className="text-sm text-slate-600">Upload and analyze a dataset to see risk assessment.</p>
|
<div className="text-center py-12 bg-slate-50 rounded-xl border-2 border-dashed border-slate-300">
|
||||||
|
<span className="text-4xl mb-3 block">🔒</span>
|
||||||
|
<p className="text-slate-600 mb-2">No risk analysis results yet</p>
|
||||||
|
<p className="text-sm text-slate-500">Upload a dataset and click "Analyze" to see comprehensive risk assessment</p>
|
||||||
|
</div>
|
||||||
)}
|
)}
|
||||||
</div>
|
</div>
|
||||||
);
|
);
|
||||||
|
|||||||
Reference in New Issue
Block a user