## Heatmaps: Bias Scores and Accuracy on BBQ (4-shot)
### Overview
The image contains two side-by-side heatmaps comparing bias scores and accuracy across different AI models (Gemini 1.0 Ultra, Gemini 1.5 Flash, Gemini 1.5 Pro) and sensitive categories (e.g., Age, Religion, Race). The left heatmap shows bias scores (ranging from -20 to 20), while the right heatmap shows accuracy (ranging from 0.6 to 1). Both use a red-to-blue gradient, with red indicating higher values (bias/accuracy) and blue indicating lower values.
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### Components/Axes
#### Left Heatmap (Bias Scores)
- **X-axis (Models)**: Gemini 1.0 Ultra, Gemini 1.5 Flash, Gemini 1.5 Pro, Gemini 1.5 Ultra (duplicate label?).
- **Y-axis (Categories)**:
- Sexual_orientation
- SES
- Religion
- Race_x_gender
- Race_x_SES
- Race_ethnicity
- Physical_appearance
- Nationality
- Gender_identity
- Disability_status
- Age
- **Legend**: Red-to-blue gradient from -20 (blue) to 20 (red).
#### Right Heatmap (Accuracy)
- **X-axis (Models)**: Same as left heatmap.
- **Y-axis (Categories)**: Same as left heatmap.
- **Legend**: Red-to-blue gradient from 0.6 (blue) to 1 (red).
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### Detailed Analysis
#### Bias Scores (Left Heatmap)
- **Highest Bias**:
- **Age**: Gemini 1.0 Ultra (27.5), Gemini 1.5 Flash (2.96), Gemini 1.5 Pro (-0.12).
- **Disability_status**: Gemini 1.0 Ultra (10.67), Gemini 1.5 Flash (3.73), Gemini 1.5 Pro (-0.66).
- **Lowest Bias**:
- **Race_ethnicity**: Gemini 1.0 Ultra (0.09), Gemini 1.5 Flash (0.26), Gemini 1.5 Pro (0.62).
- **Gender_identity**: Gemini 1.0 Ultra (4.0), Gemini 1.5 Flash (0.04), Gemini 1.5 Pro (-0.25).
- **Ambiguous vs. Disambiguous**:
- Ambiguous scores are generally higher (e.g., Age: 27.5 vs. -0.12 in disambiguous for Gemini 1.5 Pro).
#### Accuracy (Right Heatmap)
- **Highest Accuracy**:
- **Race_x_gender**: Gemini 1.0 Ultra (0.98), Gemini 1.5 Flash (1.0), Gemini 1.5 Pro (0.91).
- **Race_ethnicity**: All models score 1.0.
- **Lowest Accuracy**:
- **Age**: Gemini 1.0 Ultra (0.7), Gemini 1.5 Flash (1.0), Gemini 1.5 Pro (0.93).
- **Ambiguous vs. Disambiguous**:
- Disambiguous accuracy is consistently higher (e.g., Religion: 0.87 vs. 0.98 for Gemini 1.0 Ultra).
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### Key Observations
1. **Bias Trends**:
- **Age and Disability_status** exhibit the highest bias across models, with Gemini 1.0 Ultra showing the most severe bias (27.5 for Age).
- **Gemini 1.5 Pro** reduces bias in Age (-0.12) and Disability_status (-0.66) compared to Gemini 1.0 Ultra.
- **Race-related categories** (e.g., Race_x_gender, Race_ethnicity) show minimal bias, especially in disambiguated contexts.
2. **Accuracy Trends**:
- **Age** has the lowest accuracy (0.7 for Gemini 1.0 Ultra), while **Race_ethnicity** achieves perfect accuracy (1.0).
- **Disambiguated categories** generally have higher accuracy (e.g., Religion: 0.98 vs. 0.87 for Gemini 1.0 Ultra).
3. **Model Performance**:
- **Gemini 1.5 Pro** outperforms others in reducing bias for sensitive categories (e.g., Age, Disability_status).
- **Gemini 1.5 Flash** shows mixed results, with high bias in Age (2.96) but near-perfect accuracy in Race_x_gender (1.0).
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### Interpretation
- **Bias-Accuracy Tradeoff**: Higher bias in sensitive categories (e.g., Age) correlates with lower accuracy, suggesting models struggle to balance fairness and performance.
- **Disambiguation Impact**: Clearer data (disambiguated categories) reduces bias and improves accuracy, highlighting the importance of context in model training.
- **Model Evolution**: Gemini 1.5 Pro demonstrates progress in mitigating bias compared to earlier versions (e.g., Gemini 1.0 Ultra), but Age remains a persistent challenge.
- **Ethical Implications**: The disparity in handling Age vs. Race_ethnicity raises questions about dataset representation and algorithmic fairness priorities.
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### Spatial Grounding
- **Legends**: Positioned on the right of each heatmap, aligned with their respective color scales.
- **Categories**: Vertically aligned on the left, with consistent ordering across both heatmaps.
- **Models**: Horizontally aligned at the bottom, with Gemini 1.5 Ultra appearing twice (potential labeling error).
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### Conclusion
The heatmaps reveal critical insights into AI model behavior, emphasizing the need for targeted improvements in handling sensitive attributes like Age and Disability_status. While newer models (e.g., Gemini 1.5 Pro) show promise, systemic biases persist, underscoring the importance of ongoing research into fairness-aware AI design.
