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A Practical Tutorial on Explainable AI Techniques

Published: 07 November 2024 Publication History

Abstract

The past years have been characterized by an upsurge in opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although DNNs have great generalization and prediction abilities, it is difficult to obtain detailed explanations for their behavior. As opaque Machine Learning models are increasingly being employed to make important predictions in critical domains, there is a danger of creating and using decisions that are not justifiable or legitimate. Therefore, there is a general agreement on the importance of endowing DNNs with explainability. EXplainable Artificial Intelligence (XAI) techniques can serve to verify and certify model outputs and enhance them with desirable notions such as trustworthiness, accountability, transparency, and fairness. This guide is intended to be the go-to handbook for anyone with a computer science background aiming to obtain an intuitive insight from Machine Learning models accompanied by explanations out-of-the-box. The article aims to rectify the lack of a practical XAI guide by applying XAI techniques, in particular, day-to-day models, datasets and use-cases. In each chapter, the reader will find a description of the proposed method as well as one or several examples of use with Python notebooks. These can be easily modified to be applied to specific applications. We also explain what the prerequisites are for using each technique, what the user will learn about them, and which tasks they are aimed at.

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Published In

cover image ACM Computing Surveys
ACM Computing Surveys  Volume 57, Issue 2
February 2025
974 pages
EISSN:1557-7341
DOI:10.1145/3696822
  • Editors:
  • David Atienza,
  • Michela Milano
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2024
Online AM: 12 June 2024
Accepted: 08 May 2024
Revised: 22 March 2024
Received: 08 March 2023
Published in CSUR Volume 57, Issue 2

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Author Tags

  1. Explainable artificial intelligence
  2. machine learning
  3. deep learning
  4. interpretability
  5. shapley
  6. Grad-CAM
  7. layer-wise relevance propagation
  8. DiCE
  9. counterfactual explanations
  10. TS4NLE
  11. neural-symbolic learning

Qualifiers

  • Tutorial

Funding Sources

  • Austrian Science Fund (FWF)
  • Juan de la Cierva Incorporación
  • “ESF Investing in your future”, a MSCA Postdoctoral Fellowship
  • Google Research Scholar Program, and a 2022 Leonardo Grant for Researchers and Cultural Creators from BBVA Foundation
  • European Union’s Horizon 2020 research and innovation programme
  • European Union’s Horizon 2020 research and innovation programme
  • PNRR project INEST - Interconnected North-East Innovation Ecosystem
  • PNRR project FAIR - Future AI Research

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  • (2024)Fostering Trust in AI-Driven Healthcare: A Brief Review of Ethical and Practical Considerations2024 International Symposium on Electronics and Telecommunications (ISETC)10.1109/ISETC63109.2024.10797264(1-4)Online publication date: 7-Nov-2024
  • (2024)A Semantic Framework for Neurosymbolic ComputationArtificial Intelligence10.1016/j.artint.2024.104273(104273)Online publication date: Dec-2024

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