Abstract
AI has transformed the field of terrorism prediction, allowing law enforcement agencies to identify potential threats much more quickly and accurately. This paper proposes a first-time application of a neural network to predict the “success” of a terrorist attack. The neural network attains an accuracy of 91.66% and an F1 score of 0.954. This accuracy and F1 score are higher than those achieved with alternative benchmark models. However, using AI for predictions in high- stakes decisions also has limitations, including possible biases and ethical concerns. Therefore, the explainable AI (XAI) tool LIME is used to provide more insights into the algorithm's inner workings.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of IEEE CAI 2023: Conference on Artificial Intelligence |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 2 |
| ISBN (Electronic) | 9798350339840 |
| ISBN (Print) | 9798350339857 |
| DOIs | |
| Publication status | Published - 2 Aug 2023 |
| Event | IEEE CAI2023: Conference on Artificial Intelligence - Santa Clara, United States Duration: 5 Jun 2023 → 6 Jun 2023 https://cai.ieee.org/2023/ |
Conference
| Conference | IEEE CAI2023: Conference on Artificial Intelligence |
|---|---|
| Country/Territory | United States |
| City | Santa Clara |
| Period | 5/06/23 → 6/06/23 |
| Internet address |
Keywords
- explainable AI
- terrorism prediction
- Global Terrorism Database (GTD)
- LIME
- neural networks
Access to Document
- IEEECAI2023_Rosner_et_al_AAM
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