Explainability
What is Explainability?
Explainability in Edge AI refers to the ability to interpret and understand how AI models running on edge devices make decisions. Also called AI interpretability, it ensures transparency, trust, and accountability in automated processes without relying on cloud computation.
Why Is It Used?
Explainability is crucial for validating AI outputs, detecting biases, and building user trust in real-time decision-making at the edge.
How Is It Used?
Edge AI systems use explainability tools to visualize decision paths, feature importance, and anomaly detection, helping engineers and stakeholders understand AI reasoning locally on devices.
Types of Explainability
Global Explainability: Understanding the overall behavior of an AI model.
Local Explainability: Explaining individual predictions or decisions made by the AI.
Benefits of Explainability
Increases trust in Edge AI solutions
Enables faster debugging and model optimization
Supports compliance with AI governance and ethical standards
Reduces dependency on cloud analytics