Neural network based identification of terrorist groups using explainable artificial intelligence

Odartey Lamptey; Alexander Gegov; Djamila Ouelhadj; Adrian Alan Hopgood; Serge Da Deppo

doi:10.1109/CAI54212.2023.00090

Neural network based identification of terrorist groups using explainable artificial intelligence

Odartey Lamptey, Alexander Gegov, Djamila Ouelhadj, Adrian Alan Hopgood, Serge Da Deppo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Currently machine learning (ML) and artificial intelligence (AI) are applied in diverse domains and governments believe these technologies can be applied in identifying terrorist groups. This paper reviews literature covering the varied use of ML algorithms and proposes a novel approach with a first-time application of a deep neural network (DNN) to an existing case study for the identification of terrorist groups. The research will further seek to explain how the neural network arrived at its decision using SHapley Additive exPlanations (SHAP). The results reveal DNN outperformed two benchmark models in terms of accuracy. SHAP was able to explain features that influenced the predicted results.

Original language	English
Title of host publication	Proceedings of the 2023 IEEE Conference on Artificial Intelligence
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	191-192
Number of pages	2
ISBN (Electronic)	9798350339840
ISBN (Print)	9798350339857
DOIs	https://doi.org/10.1109/CAI54212.2023.00090
Publication status	Published - 2 Aug 2023
Event	2023 IEEE Conference on Artificial Intelligence, CAI 2023 - Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023

Conference

Conference	2023 IEEE Conference on Artificial Intelligence, CAI 2023
Country/Territory	United States
City	Santa Clara
Period	5/06/23 → 6/06/23

Keywords

Big Data
Deep Neural Network
Explainable AI (XAI)
Global Terrorism Database
Machine Learning
Terrorism Identification

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/CAI54212.2023.00090

2023124763_1_
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Accepted author manuscript (Post-print), 207 KB

Cite this

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title = "Neural network based identification of terrorist groups using explainable artificial intelligence",

abstract = "Currently machine learning (ML) and artificial intelligence (AI) are applied in diverse domains and governments believe these technologies can be applied in identifying terrorist groups. This paper reviews literature covering the varied use of ML algorithms and proposes a novel approach with a first-time application of a deep neural network (DNN) to an existing case study for the identification of terrorist groups. The research will further seek to explain how the neural network arrived at its decision using SHapley Additive exPlanations (SHAP). The results reveal DNN outperformed two benchmark models in terms of accuracy. SHAP was able to explain features that influenced the predicted results.",

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author = "Odartey Lamptey and Alexander Gegov and Djamila Ouelhadj and Hopgood, {Adrian Alan} and {Da Deppo}, Serge",

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doi = "10.1109/CAI54212.2023.00090",

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Lamptey, O , Gegov, A , Ouelhadj, D , Hopgood, AA & Da Deppo, S 2023, Neural network based identification of terrorist groups using explainable artificial intelligence. in Proceedings of the 2023 IEEE Conference on Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., pp. 191-192, 2023 IEEE Conference on Artificial Intelligence, CAI 2023, Santa Clara, United States, 5/06/23. https://doi.org/10.1109/CAI54212.2023.00090

Neural network based identification of terrorist groups using explainable artificial intelligence. / Lamptey, Odartey ; Gegov, Alexander ; Ouelhadj, Djamila et al.
Proceedings of the 2023 IEEE Conference on Artificial Intelligence. Institute of Electrical and Electronics Engineers Inc., 2023. p. 191-192.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Currently machine learning (ML) and artificial intelligence (AI) are applied in diverse domains and governments believe these technologies can be applied in identifying terrorist groups. This paper reviews literature covering the varied use of ML algorithms and proposes a novel approach with a first-time application of a deep neural network (DNN) to an existing case study for the identification of terrorist groups. The research will further seek to explain how the neural network arrived at its decision using SHapley Additive exPlanations (SHAP). The results reveal DNN outperformed two benchmark models in terms of accuracy. SHAP was able to explain features that influenced the predicted results.

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Neural network based identification of terrorist groups using explainable artificial intelligence

Abstract

Conference

Keywords

UN SDGs

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Cite this