Week 14: Understanding Bias in Deep Learning Systems
Overview
This week examines how bias can be introduced, detected, and mitigated in deep learning systems, from data collection through model deployment.
Learning Objectives
- Identify sources of bias in deep learning
- Understand data representation issues
- Recognize model architecture biases
- Implement bias detection methods
- Apply bias mitigation strategies
1. Data-Level Bias
- Collection Bias
- Sampling methods
- Historical prejudices
- Representation issues
- Coverage gaps
- Preprocessing Bias
- Feature selection
- Normalization choices
- Missing data handling
- Labeling practices
2. Detection and Mitigation
- Bias Detection
- Statistical measures
- Performance disparities
- Fairness metrics
- Monitoring systems
- Mitigation Strategies
- Data augmentation
- Model constraints
- Ensemble methods
- Post-processing techniques
Key Takeaways
- Data collection and preprocessing significantly impact bias
- Statistical measures can reveal hidden biases
- Multiple mitigation strategies are often needed
- Ongoing monitoring is essential for bias control
Practical Exercises
- Identify bias in data collection methods
- Apply statistical bias detection
- Compare mitigation strategies
- Build bias monitoring systems