examples/bias_detection/README.md

Bias Detection Test Images

Images for testing performance variations across different subgroups.

📸 Recommended Images

What to Include:

1. Same Subject, Different Conditions: Day/night, indoor/outdoor
2. Environmental Variations: Weather, seasons, lighting
3. Context Variations: Urban/rural, natural/artificial
4. Quality Variations: Professional vs amateur

Current Images:

• dog_daylight.jpg - Good lighting conditions
• cat_indoor.jpg - Controlled indoor environment
• bird_outdoor.jpg - Natural outdoor setting
• urban_scene.jpg - City environment

🧪 Testing Guide

Lighting Bias:

1. Compare dog_daylight.jpg with similar night image
2. Check confidence differences
3. Identify lighting bias if present

Environment Bias:

1. Compare cat_indoor.jpg with outdoor cat image
2. Check performance variations
3. Assess environmental impact

Context Bias:

1. Use urban_scene.jpg and compare with rural scene
2. Check if model favors certain contexts
3. Review subgroup metrics

💡 Tips

• Create matched pairs (same subject, different conditions)
• Test systematic variations (brightness, contrast, saturation)
• Document performance differences
• Look for consistent patterns across subgroups