ABSTRACT
The increased complexity of artificial intelligence (AI), machine learning (ML) and deep learning (DL) methods, models, and training data to satisfy industrial application needs has emphasised the need for AI model providing explainability and interpretability. Model Explainability aims to communicate the reasoning of AI/ML/DL technology to end users, while model interpretability focuses on in-powering model transparency so that users will understand precisely why and how a model generates its results.
Edge AI, which combines AI, Internet of Things (IoT) and edge computing to enable real-time collection, processing, analytics, and decision-making, introduces new challenges to acheiving explainable and interpretable methods. This is due to the compromises among performance, constrained resources, model complexity, power consumption, and the lack of bench-marking and standardisation in edge environments.
This chapter presents the state of play of AI explainability and interpretability methods and techniques, discussing different benchmarking approaches and highlighting the state-of-the-art development directions.
