Quantifying Interpretability and Trust in Machine Learning Systems

Understand

Decisions by Machine Learning (ML) models have become ubiquitous.

• Trusting these decisions requires understanding how algorithms take them.
• Hence interpretability methods for ML are an active focus of research.
• A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure.