Human-interpretable model explainability on high-dimensional data

Understand

The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex.

• Unique challenges arise when a model's input features become high dimensional: on one hand, principled model-agnostic approaches to explainability become too computationally expensive; on the other, more efficient explainability algorithms lack natural interpretations for general users.
• In this work, we introduce a framework for human-interpretable explainability on high-dimensional data, consisting of two modules.
• First, we apply a semantically meaningful latent representation, both to reduce the raw dimensionality of the data, and to ensure its human interpretability.