Neural network based identification of terrorist groups using explainable artificial intelligence

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

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

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	1-2
Number of pages	2
Publication status	Accepted for publication - 1 May 2023
Event	2023 IEEE Conference on Artificial Intelligence - Hyatt Regency Santa Clara 5101 Great America Parkway, Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023 https://cai.ieee.org/2023/

Conference

Conference	2023 IEEE Conference on Artificial Intelligence
Abbreviated title	IEEE CAI
Country/Territory	United States
City	Santa Clara
Period	5/06/23 → 6/06/23
Internet address	https://cai.ieee.org/2023/

UN SDGs

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

Embargoed Document

2023124763_1_
Accepted author manuscript (Post-print), 207 KB
Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 1/01/50

Cite this

@inproceedings{d92443095dff44c792f251c29b586f62,

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.",

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

year = "2023",

month = may,

day = "1",

language = "English",

pages = "1--2",

booktitle = "Proceedings of the 2023 IEEE Conference on Artificial Intelligence",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2023 IEEE Conference<br/>on Artificial Intelligence, IEEE CAI ; Conference date: 05-06-2023 Through 06-06-2023",

url = "https://cai.ieee.org/2023/",

}

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. 1-2, 2023 IEEE Conference
on Artificial Intelligence, Santa Clara, California, United States, 5/06/23.

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. 1-2.

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

TY - GEN

T1 - Neural network based identification of terrorist groups using explainable artificial intelligence

AU - Lamptey, Odartey

AU - Gegov, Alexander

AU - Ouelhadj, Djamila

AU - Hopgood, Adrian Alan

AU - Da Deppo, Serge

PY - 2023/5/1

Y1 - 2023/5/1

N2 - 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.

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.

M3 - Conference contribution

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EP - 2

BT - Proceedings of the 2023 IEEE Conference on Artificial Intelligence

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Conference<br/>on Artificial Intelligence

Y2 - 5 June 2023 through 6 June 2023

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