# Technical Document Extraction: Violation of Equalized Odds Analysis
## Overview
The image contains **8 comparative line graphs** organized in a 2x4 grid, analyzing the performance of fairness algorithms across different dimensions. All graphs share consistent labeling conventions and color-coded data series.
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## Graph Structure
### Axes
- **Y-axis**: "Increased Dimensions" (ranging from 0 to 15 in some subplots)
- **X-axis**:
- Left column: "KPC" (values 0.0–0.3)
- Right column: "Power" (values 0.2–1.0)
### Legend
Positioned in the **top-left corner** of each graph. Color-mapped methods:
- **Blue**: Oracle
- **Green**: FairICP
- **Cyan**: FairCP
- **Red**: FDL
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## Key Trends
### Left Column: "Violation of Equalized Odds: test on KPC"
1. **KPC: dim = 1**
- All methods show **steep initial decline** as KPC increases
- Oracle (blue) consistently lowest loss (~1.0–1.5 range)
- FDL (red) highest loss (~2.5–3.0 range)
- FairICP (green) and FairCP (cyan) show similar trajectories
2. **KPC: dim = 5**
- Similar trend but with **higher baseline loss** (~2.5–8 range)
- Oracle maintains lowest loss (~2.0–2.5)
- FDL exhibits **sharp spike** at KPC=0.1 (~12.5 loss)
3. **KPC: dim = 10**
- Oracle loss remains stable (~2.0–2.5)
- FDL shows **extreme outlier** at KPC=0.1 (~15 loss)
- Fair methods converge toward Oracle performance
### Right Column: "Violation of Equalized Odds: test on statistical power"
1. **Power: dim = 1**
- All methods decline gradually as Power increases
- Oracle (blue) lowest loss (~1.0–1.5)
- FDL (red) highest loss (~2.5–3.0)
2. **Power: dim = 5**
- Oracle loss stable (~2.0–2.5)
- FDL exhibits **sharp spike** at Power=0.5 (~12.5 loss)
3. **Power: dim = 10**
- Oracle loss minimal (~2.0–2.5)
- FDL shows **extreme outlier** at Power=0.5 (~15 loss)
- Fair methods demonstrate improved performance at higher dimensions
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## Critical Observations
1. **Dimension Sensitivity**:
- Higher dimensions (dim=10) show **more pronounced performance gaps** between methods
- FDL consistently underperforms across all dimensions
2. **Algorithm Robustness**:
- Oracle maintains **lowest violation rates** regardless of dimension
- FairICP and FairCP show **diminishing returns** with increased dimensions
3. **Statistical Power Impact**:
- Power tests reveal **consistent trends** across dimensions
- FDL's performance degradation becomes **more extreme** at higher dimensions
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## Data Point Verification
All legend colors match data series:
- Blue (Oracle) points consistently lowest on y-axis
- Red (FDL) points highest, with **notable spikes** at x=0.1 (KPC) and x=0.5 (Power)
- Green (FairICP) and cyan (FairCP) show intermediate values with similar trajectories
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## Conclusion
The graphs demonstrate that:
1. Oracle algorithm maintains optimal performance across all tested conditions
2. FDL algorithm exhibits **catastrophic failure** at low KPC/Power values in higher dimensions
3. FairICP and FairCP provide **moderate improvements** over baseline methods
All textual elements have been extracted with spatial grounding and trend verification. No non-English text detected.
