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Expert Systems - 2023 - Buyuktepe - Food fraud detection using explainable artificial intelligence.pdf (5.17 MB)

Food fraud detection using explainable artificial intelligence

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journal contribution
submitted on 2024-02-20, 10:59 and posted on 2024-02-20, 11:00 authored by Okan Buyuktepe, Cagatay Catal, Gorkem Kar, Yamine Bouzembrak, Hans Marvin, Anand Gavai

Recently, the global food supply chain has become increasingly complex, and its scalability has grown. From farm to fork, the performance of food‐producing systems is influenced by significant changes in the environment, population and economy. These changes may cause an increase in food fraud and safety hazards and hence, harm human health. Adopting artificial intelligence (AI) technology in the food supply chain is one strategy to reduce these hazards. Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. We aimed to interpret the predictions of a machine learning model with the aid of these technologies. The case study was performed on a food fraud dataset using adulteration/fraud notifications retrieved from the Rapid Alert System for Food and Feed system and economically motivated adulteration database. A deep learning model was built based on this dataset and XAI tools have been investigated on the proposed deep learning model. Both features and shortcomings of the current XAI tools in the food fraud area have been presented.

Other Information

Published in: Expert Systems
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1111/exsy.13387

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Wiley

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU