The Deep Learning Architect's Handbook

11

Explaining Neural Network Predictions

Have you ever wondered why a facial recognition system flagged a photo of a person with a darker skin tone as a false positive while identifying people with lighter skin tones correctly? Or why a self-driving car decided to swerve and cause an accident, instead of braking and avoiding the collision? These questions illustrate the importance of understanding why a model predicts a certain value for critical use cases. By providing explanations for a model’s predictions, we can gain insights into how the model works and why it made a specific decision, which is crucial for transparency, accountability, trust, regulatory compliance, and improved performance.

In this chapter, we will explore neural network-specific ...