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BIAS_ANALYSIS_GUIDE.md
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# Enhanced Bias & Fairness Analysis Guide
## Overview
The Nordic Privacy AI platform now includes a comprehensive, adaptive bias and fairness analysis system that works accurately across **all types of datasets**, including:
- Small datasets (< 100 samples)
- Imbalanced groups
- Multiple protected attributes
- Binary and multi-class targets
- High-cardinality features
- Missing data
## Key Enhancements
### 1. **Adaptive Fairness Thresholds**
The system automatically adjusts fairness thresholds based on dataset characteristics:
- **Sample Size Factor**: Relaxes thresholds for small sample sizes
- **Group Imbalance Factor**: Adjusts for unequal group sizes
- **Dynamic Thresholds**:
- Disparate Impact: 0.7-0.8 (adapts to data)
- Statistical Parity: 0.1-0.15 (adapts to data)
- Equal Opportunity: 0.1-0.15 (adapts to data)
### 2. **Comprehensive Fairness Metrics**
#### Individual Metrics (6 types analyzed):
1. **Disparate Impact Ratio** (4/5ths rule)
- Measures: min_rate / max_rate across all groups
- Fair range: 0.8 - 1.25 (or adaptive)
- Higher weight in overall score
2. **Statistical Parity Difference**
- Measures: Absolute difference in positive rates
- Fair threshold: < 0.1 (or adaptive)
- Ensures equal selection rates
3. **Equal Opportunity** (TPR equality)
- Measures: Difference in True Positive Rates
- Fair threshold: < 0.1 (or adaptive)
- Ensures equal recall across groups
4. **Equalized Odds** (TPR + FPR equality)
- Measures: Both TPR and FPR differences
- Fair threshold: < 0.1 (or adaptive)
- Most comprehensive fairness criterion
5. **Predictive Parity** (Precision equality)
- Measures: Difference in precision across groups
- Fair threshold: < 0.1
- Ensures positive predictions are equally accurate
6. **Calibration** (FNR equality)
- Measures: Difference in False Negative Rates
- Fair threshold: < 0.1
- Ensures balanced error rates
#### Group-Level Metrics (per demographic group):
- Positive Rate
- Selection Rate
- True Positive Rate (TPR/Recall/Sensitivity)
- False Positive Rate (FPR)
- True Negative Rate (TNR/Specificity)
- False Negative Rate (FNR)
- Precision (PPV)
- F1 Score
- Accuracy
- Sample Size & Distribution
### 3. **Weighted Bias Scoring**
The overall bias score (0-1, higher = more bias) is calculated using:
```python
Overall Score = Weighted Average of:
- Disparate Impact (weight: 1.5x sample_weight)
- Statistical Parity (weight: 1.0x sample_weight)
- Equal Opportunity (weight: 1.0x sample_weight)
- Equalized Odds (weight: 0.8x sample_weight)
- Predictive Parity (weight: 0.7x sample_weight)
- Calibration (weight: 0.7x sample_weight)
```
Sample weight = min(1.0, total_samples / 100)
### 4. **Intelligent Violation Detection**
Violations are categorized by severity:
- **CRITICAL**: di_value < 0.5, or deviation > 50%
- **HIGH**: di_value < 0.6, or deviation > 30%
- **MEDIUM**: di_value < 0.7, or deviation > 15%
- **LOW**: Minor deviations
Each violation includes:
- Affected groups
- Specific measurements
- Actionable recommendations
- Context-aware severity assessment
### 5. **Robust Data Handling**
#### Missing Values:
- Numerical: Filled with median
- Categorical: Filled with mode or 'Unknown'
- Comprehensive logging
#### Data Type Detection:
- Binary detection (0/1, Yes/No)
- Small discrete values (< 10 unique)
- High cardinality warnings (> 50 categories)
- Mixed type handling
#### Target Encoding:
- Automatic categorical → numeric conversion
- Binary value normalization
- Clear encoding maps printed
#### Class Imbalance:
- Stratified splitting when appropriate
- Minimum class size validation
- Balanced metrics calculation
### 6. **Enhanced Reporting**
Each analysis includes:
```json
{
"overall_bias_score": 0.954,
"fairness_metrics": {
"Gender": {
"disparate_impact": {
"value": 0.276,
"threshold": 0.8,
"fair": false,
"min_group": "Female",
"max_group": "Male",
"min_rate": 0.25,
"max_rate": 0.906
},
"statistical_parity_difference": {...},
"equal_opportunity_difference": {...},
"equalized_odds": {...},
"predictive_parity": {...},
"calibration": {...},
"attribute_fairness_score": 0.89,
"group_metrics": {
"Male": {
"positive_rate": 0.906,
"tpr": 0.95,
"fpr": 0.03,
"precision": 0.92,
"f1_score": 0.93,
"sample_size": 450
},
"Female": {...}
},
"sample_statistics": {
"total_samples": 500,
"min_group_size": 50,
"max_group_size": 450,
"imbalance_ratio": 0.11,
"num_groups": 2
}
}
},
"fairness_violations": [
{
"attribute": "Gender",
"metric": "Disparate Impact",
"severity": "CRITICAL",
"value": 0.276,
"affected_groups": ["Female", "Male"],
"message": "...",
"recommendation": "CRITICAL: Group 'Female' has less than half the approval rate..."
}
]
}
```
## Usage Examples
### Basic Analysis
```python
from ai_governance import AIGovernanceAnalyzer
# Initialize
analyzer = AIGovernanceAnalyzer()
# Analyze with protected attributes
report = analyzer.analyze(
df=your_dataframe,
target_column='ApprovalStatus',
protected_attributes=['Gender', 'Age', 'Race']
)
# Check bias score
print(f"Bias Score: {report['bias_analysis']['overall_bias_score']:.1%}")
# Review violations
for violation in report['bias_analysis']['fairness_violations']:
print(f"{violation['severity']}: {violation['message']}")
```
### With Presidio (Enhanced PII Detection)
```python
# Enable Presidio for automatic demographic detection
analyzer = AIGovernanceAnalyzer(use_presidio=True)
```
### API Usage
```bash
curl -X POST http://localhost:8000/api/analyze \
-F "file=@dataset.csv" \
-F "target_column=Outcome" \
-F "protected_attributes=Gender,Age"
```
## Interpreting Results
### Overall Bias Score
- **< 0.3**: Low bias - Excellent fairness ✅
- **0.3 - 0.5**: Moderate bias - Monitor recommended ⚠️
- **> 0.5**: High bias - Action required ❌
### Disparate Impact
- **0.8 - 1.25**: Fair (4/5ths rule satisfied)
- **< 0.8**: Disadvantaged group exists
- **> 1.25**: Advantaged group exists
### Statistical Parity
- **< 0.1**: Fair (similar positive rates)
- **> 0.1**: Groups receive different treatment
### Recommendations by Severity
#### CRITICAL
- **DO NOT DEPLOY** without remediation
- Investigate systemic bias sources
- Review training data representation
- Implement fairness constraints
- Consider re-collection if necessary
#### HIGH
- Address before deployment
- Use fairness-aware training methods
- Implement threshold optimization
- Regular monitoring required
#### MEDIUM
- Monitor closely
- Consider mitigation strategies
- Regular fairness audits
- Document findings
#### LOW
- Continue monitoring
- Maintain fairness standards
- Periodic reviews
## Best Practices
### 1. Data Collection
- Ensure representative sampling
- Balance protected groups when possible
- Document data sources
- Check for historical bias
### 2. Feature Engineering
- Avoid proxy features for protected attributes
- Check feature correlations with demographics
- Use feature importance analysis
- Consider fairness-aware feature selection
### 3. Model Training
- Use fairness-aware algorithms
- Implement fairness constraints
- Try multiple fairness definitions
- Cross-validate with fairness metrics
### 4. Post-Processing
- Threshold optimization per group
- Calibration techniques
- Reject option classification
- Regular bias audits
### 5. Monitoring
- Track fairness metrics over time
- Monitor for fairness drift
- Regular re-evaluation
- Document all findings
## Technical Details
### Dependencies
```
numpy>=1.21.0
pandas>=1.3.0
scikit-learn>=1.0.0
presidio-analyzer>=2.2.0 # Optional
spacy>=3.0.0 # Optional for Presidio
```
### Performance
- Handles datasets from 50 to 1M+ rows
- Adaptive algorithms scale with data size
- Memory-efficient group comparisons
- Parallel metric calculations
### Limitations
- Requires at least 2 groups per protected attribute
- Minimum 10 samples per group recommended
- Binary classification focus (multi-class supported)
- Assumes independent test set
## Troubleshooting
### "Insufficient valid groups"
- Check protected attribute has at least 2 non-null groups
- Ensure groups appear in test set
- Increase test_size parameter
### "High cardinality warning"
- Feature has > 50 unique values
- Consider grouping categories
- May need feature engineering
### "Sample size too small"
- System adapts automatically
- Results may be less reliable
- Consider collecting more data
### "Presidio initialization failed"
- Install: `pip install presidio-analyzer spacy`
- Download model: `python -m spacy download en_core_web_sm`
- Or use `use_presidio=False`
## References
- [Fairness Definitions Explained](https://fairware.cs.umass.edu/papers/Verma.pdf)
- [4/5ths Rule (EEOC)](https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-common-interpretation-uniform-guidelines)
- [Equalized Odds](https://arxiv.org/abs/1610.02413)
- [Fairness Through Awareness](https://arxiv.org/abs/1104.3913)
## Support
For issues or questions:
- Check logs for detailed diagnostic messages
- Review sample statistics in output
- Consult violation recommendations
- Contact: support@nordicprivacyai.com

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ai_governance/__init__.py
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@@ -98,13 +98,14 @@ class AIGovernanceAnalyzer:
)
bias_results = self.bias_analyzer.analyze()
# Step 4: Assess risks
# Step 4: Assess risks with Presidio-enhanced detection
self.risk_analyzer = RiskAnalyzer(
self.processor.df,
self.trainer.results,
bias_results,
self.processor.protected_attributes,
self.processor.target_column
self.processor.target_column,
use_presidio=False # Set to True after installing: python -m spacy download en_core_web_sm
)
risk_results = self.risk_analyzer.analyze()

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ai_governance/bias_analyzer.py
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@@ -49,26 +49,43 @@ class BiasAnalyzer:
try:
print("⏳ Initializing Presidio analyzer (first time only)...")
# Check if spaCy model is available
# Check if spaCy and model are available
try:
import spacy
try:
spacy.load("en_core_web_sm")
except OSError:
print("⚠️ spaCy model 'en_core_web_sm' not found. Run: python -m spacy download en_core_web_sm")
# Check if model exists WITHOUT loading it first
model_name = "en_core_web_sm"
if not spacy.util.is_package(model_name):
print(f"⚠️ spaCy model '{model_name}' not found.")
print(f" To enable Presidio, install the model with:")
print(f" python -m spacy download {model_name}")
print(" Continuing without Presidio-enhanced detection...")
BiasAnalyzer._presidio_init_failed = True
return
# Model exists, now load it
print(f"✓ spaCy model '{model_name}' found, loading...")
nlp = spacy.load(model_name)
except ImportError:
print("⚠️ spaCy not installed. Install with: pip install spacy")
BiasAnalyzer._presidio_init_failed = True
return
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
provider = NlpEngineProvider()
nlp_configuration = {
# Create NLP engine configuration (prevent auto-download)
from presidio_analyzer.nlp_engine import NlpEngineProvider
configuration = {
"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()
# Initialize analyzer

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ai_governance/risk_analyzer.py
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frontend/components/try/CenterPanel.tsx
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@@ -751,28 +751,386 @@ export function CenterPanel({ tab, onAnalyze }: CenterPanelProps) {
);
case "risk-analysis":
return (
<div className="space-y-4">
<h2 className="text-xl font-semibold">Risk Analysis</h2>
<div className="space-y-6">
{analyzeResult ? (
<div className="space-y-4">
<div className="p-4 bg-white rounded-lg border">
<div className="text-sm text-slate-600">Overall Risk Score</div>
<div className="text-2xl font-bold">{(analyzeResult.risk_assessment.overall_risk_score * 100).toFixed(1)}%</div>
<div className="space-y-6">
{/* Header: RISK ANALYSIS SUMMARY */}
<div className="relative overflow-hidden rounded-xl border-2 border-slate-300 bg-gradient-to-br from-slate-800 via-slate-700 to-slate-900 p-8 shadow-2xl">
<div className="absolute top-0 right-0 w-64 h-64 bg-gradient-to-br from-blue-500/10 to-purple-500/10 rounded-full blur-3xl"></div>
<div className="relative">
<div className="flex items-center gap-3 mb-6 pb-4 border-b border-slate-600">
<span className="text-4xl">🔒</span>
<h2 className="text-3xl font-black text-white tracking-tight">RISK ANALYSIS SUMMARY</h2>
</div>
{cleanResult && (
<div className="p-4 bg-white rounded-lg border">
<h3 className="font-semibold mb-2">PII Detection Results</h3>
<div className="text-sm space-y-1">
<div>Cells Anonymized: <span className="font-medium">{cleanResult.summary.total_cells_affected}</span></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 className="grid grid-cols-1 md:grid-cols-3 gap-6">
{/* Overall Risk */}
<div className="bg-white/10 backdrop-blur-sm rounded-xl p-5 border border-white/20">
<div className="text-sm font-medium text-slate-300 mb-2">📊 Overall Risk</div>
<div className="text-5xl font-black text-white mb-2">
{(analyzeResult.risk_assessment.overall_risk_score * 100).toFixed(1)}%
</div>
<div className={`inline-flex px-3 py-1 rounded-full text-xs font-bold ${
analyzeResult.risk_assessment.risk_level === 'CRITICAL' ? 'bg-red-500 text-white' :
analyzeResult.risk_assessment.risk_level === 'HIGH' ? 'bg-orange-500 text-white' :
analyzeResult.risk_assessment.risk_level === 'MEDIUM' ? 'bg-yellow-500 text-slate-900' :
'bg-green-500 text-white'
}`}>
{analyzeResult.risk_assessment.risk_level}
</div>
</div>
{/* Presidio Status */}
<div className="bg-white/10 backdrop-blur-sm rounded-xl p-5 border border-white/20">
<div className="text-sm font-medium text-slate-300 mb-2">🔒 Detection Engine</div>
<div className="text-2xl font-bold text-white mb-2">
{analyzeResult.risk_assessment.presidio_enabled ? 'Presidio' : 'Regex'}
</div>
<div className={`inline-flex items-center gap-2 px-3 py-1 rounded-full text-xs font-bold ${
analyzeResult.risk_assessment.presidio_enabled
? 'bg-blue-500 text-white'
: 'bg-slate-600 text-slate-200'
}`}>
<span className={`w-2 h-2 rounded-full ${
analyzeResult.risk_assessment.presidio_enabled ? 'bg-white animate-pulse' : 'bg-slate-400'
}`}></span>
{analyzeResult.risk_assessment.presidio_enabled ? 'Enhanced' : 'Standard'}
</div>
</div>
{/* Violations */}
<div className="bg-white/10 backdrop-blur-sm rounded-xl p-5 border border-white/20">
<div className="text-sm font-medium text-slate-300 mb-2">⚠️ Violations</div>
<div className={`text-5xl font-black mb-2 ${
(analyzeResult.risk_assessment.violations?.length || 0) > 0
? 'text-red-400'
: 'text-green-400'
}`}>
{analyzeResult.risk_assessment.violations?.length || 0}
</div>
<div className="text-xs text-slate-300">
{(analyzeResult.risk_assessment.violations?.filter((v: any) => v.severity === 'CRITICAL').length || 0)} Critical Issues
</div>
</div>
</div>
</div>
</div>
{/* Risk Categories Grid with Enhanced Design */}
<div className="bg-white rounded-xl border-2 border-slate-200 p-6 shadow-lg">
<div className="flex items-center gap-2 mb-6">
<span className="text-2xl">📈</span>
<h3 className="text-xl font-bold text-slate-800">Category Scores</h3>
</div>
<div className="grid grid-cols-1 md:grid-cols-2 lg: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 categoryConfig: Record<string, { icon: string; label: string; color: string }> = {
privacy: { icon: '<27>', label: 'Privacy', color: 'blue' },
ethical: { icon: '🟠', label: 'Ethical', color: 'purple' },
compliance: { icon: '<27>', label: 'Compliance', color: 'indigo' },
security: { icon: '<27>', label: 'Security', color: 'cyan' },
operational: { icon: '🟠', label: 'Operational', color: 'orange' },
data_quality: { icon: '<27>', label: 'Data Quality', color: 'green' }
};
const config = categoryConfig[category] || { icon: '📌', label: category, color: 'slate' };
// Dynamic emoji based on risk level
const riskEmoji = riskPct < 25 ? '🟢' : riskPct < 50 ? '🟡' : '🟠';
return (
<div key={category} className={`relative overflow-hidden rounded-xl border-2 p-5 transition-all hover:shadow-xl hover:scale-105 ${
riskLevel === 'CRITICAL' ? 'border-red-300 bg-gradient-to-br from-red-50 via-white to-red-50' :
riskLevel === 'HIGH' ? 'border-orange-300 bg-gradient-to-br from-orange-50 via-white to-orange-50' :
riskLevel === 'MEDIUM' ? 'border-yellow-300 bg-gradient-to-br from-yellow-50 via-white to-yellow-50' :
'border-green-300 bg-gradient-to-br from-green-50 via-white to-green-50'
}`}>
<div className="absolute top-0 right-0 w-20 h-20 bg-gradient-to-br from-white/50 to-transparent rounded-full blur-2xl"></div>
<div className="relative">
<div className="flex items-start justify-between mb-3">
<span className="text-3xl">{riskEmoji}</span>
<span className={`text-xs font-black px-2.5 py-1 rounded-full shadow-sm ${
riskLevel === 'CRITICAL' ? 'bg-red-600 text-white' :
riskLevel === 'HIGH' ? 'bg-orange-600 text-white' :
riskLevel === 'MEDIUM' ? 'bg-yellow-600 text-white' :
'bg-green-600 text-white'
}`}>
{riskLevel}
</span>
</div>
<div className="text-sm font-bold text-slate-600 uppercase tracking-wide mb-2">
{config.label}
</div>
<div className="text-4xl font-black bg-gradient-to-r from-slate-800 to-slate-600 bg-clip-text text-transparent mb-3">
{riskPct.toFixed(1)}%
</div>
{/* Progress Bar */}
<div className="relative h-2 bg-slate-200 rounded-full overflow-hidden shadow-inner">
<div
className={`absolute inset-y-0 left-0 rounded-full transition-all duration-700 ease-out ${
riskLevel === 'CRITICAL' ? 'bg-gradient-to-r from-red-500 via-red-600 to-red-700' :
riskLevel === 'HIGH' ? 'bg-gradient-to-r from-orange-500 via-orange-600 to-orange-700' :
riskLevel === 'MEDIUM' ? 'bg-gradient-to-r from-yellow-500 via-yellow-600 to-yellow-700' :
'bg-gradient-to-r from-green-500 via-green-600 to-green-700'
}`}
style={{ width: `${Math.min(riskPct, 100)}%` }}
>
<div className="absolute inset-0 bg-gradient-to-r from-transparent via-white/20 to-transparent animate-pulse"></div>
</div>
</div>
</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">
{typeof analyzeResult.risk_assessment.privacy_risks === 'object' && !Array.isArray(analyzeResult.risk_assessment.privacy_risks)
? (analyzeResult.risk_assessment.privacy_risks.pii_count || 0)
: (Array.isArray(analyzeResult.risk_assessment.privacy_risks) ? analyzeResult.risk_assessment.privacy_risks.length : 0)} PII Types
</span>
</div>
{/* PII Detections - Handle both object and array formats */}
{(typeof analyzeResult.risk_assessment.privacy_risks === 'object' &&
!Array.isArray(analyzeResult.risk_assessment.privacy_risks) &&
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 */}
{typeof analyzeResult.risk_assessment.privacy_risks === 'object' &&
!Array.isArray(analyzeResult.risk_assessment.privacy_risks) && (
<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
? (analyzeResult.risk_assessment.privacy_risks.reidentification_risk * 100).toFixed(0)
: 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
? (analyzeResult.risk_assessment.privacy_risks.data_minimization_score * 100).toFixed(0)
: 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 || 'N/A'}
</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 || 'Auto'}
</div>
</div>
</div>
)}
</div>
) : (
<p className="text-sm text-slate-600">Upload and analyze a dataset to see risk assessment.</p>
<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 Section with Enhanced Design */}
{analyzeResult.risk_assessment.violations &&
analyzeResult.risk_assessment.violations.length > 0 && (
<div className="bg-gradient-to-br from-red-50 via-white to-orange-50 rounded-xl border-2 border-red-200 p-6 shadow-lg">
<div className="flex items-center gap-3 mb-5">
<span className="text-3xl">⚠️</span>
<h3 className="text-xl font-bold text-slate-800">Violations</h3>
<span className="ml-auto px-4 py-1.5 bg-red-600 text-white rounded-full text-sm font-black shadow-md">
{analyzeResult.risk_assessment.violations.length} Issues Found
</span>
</div>
<div className="space-y-3">
{analyzeResult.risk_assessment.violations.map((violation: any, idx: number) => (
<div key={idx} className={`group relative overflow-hidden p-5 rounded-xl border-2 transition-all hover:shadow-lg hover:scale-[1.02] ${
violation.severity === 'CRITICAL' ? 'bg-gradient-to-r from-red-50 to-red-100 border-red-300' :
violation.severity === 'HIGH' ? 'bg-gradient-to-r from-orange-50 to-orange-100 border-orange-300' :
violation.severity === 'MEDIUM' ? 'bg-gradient-to-r from-yellow-50 to-yellow-100 border-yellow-300' :
'bg-gradient-to-r from-blue-50 to-blue-100 border-blue-300'
}`}>
<div className="absolute top-0 right-0 w-32 h-32 bg-white/20 rounded-full blur-3xl"></div>
<div className="relative">
<div className="flex items-start justify-between gap-3 mb-3">
<div className="flex items-center gap-2">
<span className={`text-xs font-black px-3 py-1.5 rounded-full shadow-sm ${
violation.severity === 'CRITICAL' ? 'bg-red-600 text-white' :
violation.severity === 'HIGH' ? 'bg-orange-600 text-white' :
violation.severity === 'MEDIUM' ? 'bg-yellow-600 text-slate-900' :
'bg-blue-600 text-white'
}`}>
{violation.severity}
</span>
<span className="text-xs font-bold text-slate-500 uppercase tracking-wider">
{violation.category}
</span>
</div>
</div>
<div className="flex items-start gap-3">
<span className="text-2xl mt-1">
{violation.severity === 'CRITICAL' ? '🔴' :
violation.severity === 'HIGH' ? '🟠' :
violation.severity === 'MEDIUM' ? '🟡' : '🔵'}
</span>
<div className="flex-1">
<div className="text-base font-bold text-slate-800 mb-1">
{violation.message}
</div>
{violation.details && (
<div className="text-sm text-slate-600 leading-relaxed">
{violation.details}
</div>
)}
</div>
</div>
</div>
</div>
))}
</div>
</div>
)}
{/* Key Insights Section with Enhanced Design */}
{analyzeResult.risk_assessment.insights &&
analyzeResult.risk_assessment.insights.length > 0 && (
<div className="relative overflow-hidden bg-gradient-to-br from-blue-600 via-indigo-600 to-purple-600 rounded-xl border-2 border-blue-700 p-8 shadow-2xl">
<div className="absolute top-0 right-0 w-64 h-64 bg-white/5 rounded-full blur-3xl"></div>
<div className="absolute bottom-0 left-0 w-48 h-48 bg-purple-500/10 rounded-full blur-3xl"></div>
<div className="relative">
<div className="flex items-center gap-3 mb-6">
<span className="text-4xl">💡</span>
<h3 className="text-2xl font-black text-white">Key Insights</h3>
</div>
<div className="space-y-3">
{analyzeResult.risk_assessment.insights.map((insight: string, idx: number) => (
<div key={idx} className="flex items-start gap-3 bg-white/10 backdrop-blur-sm border border-white/20 rounded-lg p-4 hover:bg-white/15 transition-all">
<span className="text-yellow-300 text-xl mt-0.5 flex-shrink-0">•</span>
<span className="text-white text-sm leading-relaxed font-medium">{insight}</span>
</div>
))}
</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 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>
);

9
frontend/lib/api.ts
View File

@@ -27,10 +27,15 @@ export interface AnalyzeResponse {
};
risk_assessment: {
overall_risk_score: number;
privacy_risks: any[];
risk_level?: string;
presidio_enabled?: boolean;
risk_categories?: Record<string, number>;
privacy_risks: any;
ethical_risks: any[];
compliance_risks: any[];
compliance_risks: any;
data_quality_risks: any[];
violations?: any[];
insights?: string[];
};
recommendations: string[];
report_file: string;