Interpretation of Explainable AI Methods as Identification of Local Linearized Models

Darya Biparva, Donatello Materassi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Artificial intelligence (AI) models are increasingly ubiquitous in daily life, their unparalleled predictive and decision-making capabilities are being utilized for applications of all magnitudes, ranging from minor decisions to those with significant impacts on individuals and society. However, many of these models feature a multitude of redundant parameters, which render them incomprehensible to human understanding. The lack of transparency raises concerns about the reliability and fairness of the decisions made by AI models motivating a new field of research called eXplainable AI (XAI), which aims to elucidate complex AI model outcomes and develop tools to enable human understanding. Given the increasing impact of machine learning in the fields of data-driven estimation and control, it becomes crucial to integrate XAI tools with control theory to better comprehend the decisions made by AI models in estimation and control. In this article, we propose the use of an XAI method called Local Interpretable Model-Agnostic Explanations (LIME) to explain the mechanisms behind a black-box estimation algorithm processing time-series data. Moreover, we demonstrate that LIME can be used to identify a local linearized model that approximates the complex machine learning algorithm. We show that the identified local linearized model can shed light on the dynamic of the model that generated the training data.

Original languageEnglish (US)
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Number of pages6
ISBN (Electronic)9781713872344
StatePublished - Jul 1 2023
Event22nd IFAC World Congress - Yokohama, Japan
Duration: Jul 9 2023Jul 14 2023

Publication series

ISSN (Electronic)2405-8963


Conference22nd IFAC World Congress

Bibliographical note

Publisher Copyright:
Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (


  • Continuous time system estimation
  • Dynamic networks
  • Estimation and filtering
  • Machine learning
  • Time series modeling


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