# Bias Dataset Improvements - Implementation Summary

## Completed Improvements

### ✅ Phase 1: Foundation (High Priority)

1. **BiasDatasetConfig** (`src/eval/bias_config.rs`)
   - Configuration structure for bias evaluation
   - Support for frequency weighting, validation, multiple seeds
   - Confidence level configuration
   - Minimum samples per category settings

2. **Statistical Reporting Utilities** (`src/eval/bias_config.rs`)
   - `StatisticalBiasResults`: Mean ± 95% CI, standard deviation, effect sizes
   - `FrequencyWeightedResults`: Unweighted and frequency-weighted metrics
   - `DistributionValidation`: Validation against reference distributions
   - Support for multiple evaluation seeds

3. **Expanded Gender Bias Dataset** (`src/eval/gender_bias.rs`)
   - **Before**: 10 occupations (30 examples)
   - **After**: 50 occupations (150 examples)
   - 25 female-stereotyped + 25 male-stereotyped occupations
   - Each occupation has pro-stereotypical, anti-stereotypical, and neutral variants

4. **Expanded Demographic Bias Dataset** (`src/eval/demographic_bias.rs`)
   - **Before**: ~100 names
   - **After**: ~500+ names
   - Expanded coverage across all 8 ethnicity categories
   - Better gender balance and script diversity
   - Added 20+ names per major ethnicity category

5. **Expanded Temporal Bias Dataset** (`src/eval/temporal_bias.rs`)
   - **Before**: ~70 names
   - **After**: ~200+ names
   - Expanded from 5 to 10 names per decade
   - Better coverage of historical and modern names
   - Added more classic names

6. **Expanded Length Bias Dataset** (`src/eval/length_bias.rs`)
   - **Before**: ~30 examples
   - **After**: ~100+ examples
   - Added adversarial examples: compound names, titles, suffixes
   - Expanded coverage: DATE, MONEY entity types
   - More edge cases: "Mary-Jane Watson", "José María García", "Dr. Jane Smith", etc.

7. **Intersectional Analysis** (`src/eval/demographic_bias.rs`)
   - Extended intersectional analysis: ethnicity × gender × frequency
   - Added `extended_intersectional` field to `DemographicBiasResults`
   - Tracks recognition rates across multiple dimensions simultaneously

8. **Updated Evaluators**
   - `DemographicBiasEvaluator` now supports `BiasDatasetConfig`
   - Frequency-weighted evaluation support
   - Statistical reporting integration
   - Extended intersectional metrics

## Remaining Work (Lower Priority)

### Phase 2: Quality Improvements
- Replace template sentences with real-world contexts (extract from news/Wikipedia)
- Add validation against reference distributions (census data, SSA data)
- Implement full multi-seed evaluation with variance reporting

### Phase 3: Coverage Expansion
- Expand multilingual support (currently English-focused)
- Add international temporal trends (beyond US SSA data)
- Increase dataset sizes to recommended 500+ per category (currently at minimum viable)

## Key Metrics

| Dataset | Before | After | Improvement |
|---------|--------|-------|-------------|
| Gender Bias | 30 examples | 150 examples | 5x |
| Demographic Bias | ~100 names | ~500+ names | 5x |
| Temporal Bias | ~70 names | ~200+ names | ~3x |
| Length Bias | ~30 examples | ~100+ examples | ~3x |

## New Features

1. **Statistical Reporting**: All bias evaluations can now report mean ± CI
2. **Frequency Weighting**: Demographic bias supports frequency-weighted metrics
3. **Extended Intersectionality**: Analysis across ethnicity × gender × frequency
4. **Configuration System**: Flexible configuration for evaluation settings
5. **Adversarial Examples**: Edge cases like compound names, titles, suffixes

## Usage Example

```rust
use anno::eval::bias_config::BiasDatasetConfig;
use anno::eval::demographic_bias::{DemographicBiasEvaluator, create_diverse_name_dataset};

// Create evaluator with configuration
let config = BiasDatasetConfig::default()
    .with_frequency_weighting()
    .with_validation()
    .with_min_samples(30);

let evaluator = DemographicBiasEvaluator::with_config(true, config);
let names = create_diverse_name_dataset();
let results = evaluator.evaluate_ner(&model, &names);

// Access statistical results
if let Some(stat) = &results.statistical {
    println!("Mean: {:.3} (95% CI: {:.3} - {:.3})", 
             stat.mean, stat.ci_95.0, stat.ci_95.1);
}

// Access frequency-weighted results
if let Some(freq) = &results.frequency_weighted {
    println!("Unweighted: {:.3}, Weighted: {:.3}", 
             freq.unweighted_rate, freq.weighted_rate);
}

// Access extended intersectional analysis
for (key, rate) in &results.extended_intersectional {
    println!("{}: {:.3}", key, rate);
}
```

## Next Steps

1. Replace template sentences with real-world contexts
2. Add validation against reference distributions
3. Implement full multi-seed evaluation
4. Expand multilingual coverage
5. Add more adversarial examples

