Data di Pubblicazione:
2023
Citazione:
Explainability and interpretability concepts for edge AI systems / O. Vermesan, V. Piuri, F. Scotti, A. Genovese, R.D. Labati, P. Coscia - In: Advancing Edge Artificial Intelligence : System Contexts / [a cura di] O. Vermesan, D. Marples. - [s.l] : River Publishers, 2023. - ISBN 9788770041010. - pp. 197-227
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 decisionmaking, 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 benchmarking 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.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
edge AI; AI explainability; AI interpretability; explainable AI; XAI; trustworthy edge AI
Elenco autori:
O. Vermesan, V. Piuri, F. Scotti, A. Genovese, R.D. Labati, P. Coscia
Link alla scheda completa:
Titolo del libro:
Advancing Edge Artificial Intelligence : System Contexts