## Scatter Plot Grid: Risk-Sensitive Analysis (RSA) Decision Boundaries ### Overview The image displays a 2x5 grid of scatter plots comparing two risk-sensitive analysis (RSA) scenarios: (H+S) and (P), across varying Conditional Value at Risk (CVaR) thresholds (μ). Each plot visualizes data point distributions with color-coded categories and a pink-shaded SVM decision boundary. CVaR μ values increase from left to right (0.90 to 0.99 in top row, 0.93 to 0.99 in bottom row). ### Components/Axes - **X-axis**: CVaR μ (Conditional Value at Risk threshold), labeled with values 0.90, 0.93, 0.95, 0.97, 0.99 - **Y-axis**: Frequency (0-100 scale) - **Legend**: Located at bottom center, mapping 10 categories to colors: - Blue: Helpful - Orange: Crime - Green: Emotional Harm - Red: Immoral - Purple: Insult - Brown: Physical Harm - Pink: Pornographic - Gray: Privacy - Yellow: Social Bias - Dashed line: SVM Decision Boundary - **Plot Titles**: Format "RSA (H+S/P) CVaR μ = [value]" ### Detailed Analysis 1. **Top Row (H+S Scenario)**: - μ = 0.90: Scattered points with weak boundary; pink area covers ~30% of plot - μ = 0.93: Boundary tightens; pink area reduces to ~20% - μ = 0.95: Clear separation; pink area ~15% - μ = 0.97: Minimal overlap; pink area ~10% - μ = 0.99: Almost perfect separation; pink area ~5% 2. **Bottom Row (P Scenario)**: - μ = 0.93: Similar to H+S μ=0.93 but with more overlap - μ = 0.95: Boundary less defined than H+S μ=0.95 - μ = 0.97: Moderate separation; pink area ~12% - μ = 0.99: Strong boundary; pink area ~8% 3. **Data Point Distribution**: - Blue ("Helpful") dominates lower-left quadrant across all plots - Orange ("Crime") and Red ("Immoral") cluster in upper-right - Yellow ("Social Bias") appears in mid-right quadrant - Gray ("Privacy") and Pink ("Pornographic") are sparsely distributed ### Key Observations - **Boundary Tightness**: Higher μ values (0.97-0.99) show significantly tighter decision boundaries in both scenarios - **Scenario Comparison**: H+S consistently achieves better separation than P at equivalent μ values - **Category Clustering**: "Helpful" (blue) remains dominant in lower-left, while "Crime" (orange) and "Immoral" (red) persist in upper-right regardless of μ - **Anomaly**: At μ=0.99 (H+S), "Social Bias" (yellow) appears isolated in mid-right, suggesting potential outlier behavior ### Interpretation The plots demonstrate how increasing risk aversion (higher CVaR μ) improves model discrimination between categories. The H+S scenario (top row) consistently outperforms P (bottom row) in creating tighter decision boundaries, particularly at μ≥0.95. This suggests the H+S approach better balances risk mitigation with category separation. The persistent clustering of "Helpful" and "Crime/Immoral" categories indicates these may represent fundamental data separations independent of risk parameters. The isolated "Social Bias" point at μ=0.99 (H+S) warrants investigation - it could represent a genuine outlier or indicate limitations in the feature space for detecting this category under high-risk aversion settings.