Predicting global conflicts through data-driven approaches has the potential to aid political decisionmakers in formulating more efective and targeted policies. However, high-performance models that derive patterns from data often become highly complex, making it challenging to extract understandable rationales behind their outcomes. In this paper, we suggest integrating a transformer-based Artificial Intelligence Early Warning System (AI-EWS) with integrated gradients, an eXplainable Artificial Intelligence (XAI) technique attributing model predictions to specific features at a given time in the input data, thereby enhancing interpretability. To validate our methodology, we conduct experiments on a prominent geopolitical dataset: ACLED. This dataset provides comprehensive insights into global conflict events, facilitating efective pattern learning and generalization by our model. Leveraging these explainability techniques, our goal is to bridge the gap between complex, high-performance models and the practical needs of policymakers in conflict prevention and resolution. Predictive analytics algorithms in conjunction with an XAI approach can foresee the impact of decisions on various population segments, fostering equity, and inclusion and supporting a data-driven approach, along with a culture of openness and accountability within the public administration.
Predicting potential conflicts has played a crucial role in the landscape of peace research since
Singer’s work in the early 70’s [
The field of conflict prediction has explored the usage of various machine learning models, including Random Forest [8], naive Bayes classifiers [ 9] and Neural Networks [10]. Notably, in the realm of time series forecasting, researchers have recently applied transformer architectures to univariate time series forecasting tasks. For instance, Li and co-authors solution showcased superior performance compared to classical statistical methods like ARIMA, as well as recent approaches such as TRMF, DeepAR, and DeepState, on four public forecasting datasets [11]. In our work we extend the application of transformers for multivariate time-series tasks as done by Zerveas and co-authors [12], where they use only the encoder part of the original transformer architecture. We use the same approach with some modification: in particular, we decided to include residual connections between input and output, ensuring that a purely linear model is always a subclass of our model [13]. The reason is that a simple linear models surprisingly may outperform existing sophisticated transformer-based models for long timeseries forecasting problems [14]. Due to the intricate nature of the model, it’s necessary to exploit XAI approaches to provide trustworthy explanations of its output. Regarding the use of integrated gradients within transformers, a self-attention attribution method was proposed and demonstrated on BERT [15]. Integrated hessians, an extension of integrated gradients, explain pairwise feature interactions in DistilBERT and demonstrating its efectiveness in sentiment analysis [16]. Following this trend, a recent work focuses on applying model-agnostic XAI techniques [17], such as SHAP [18] and LIME [19], to interpret predictions from transformerbased models in mental healthcare monitoring on social networks. The study underscores the social and public importance of explainability for the adoption of AI-based diagnostic systems.
The value of data selection in defining a data-driven conflict prediction model’s performance is recognized. Such models usually are dependent on either social media or diplomatic datasets. Social media datasets, specifically Twitter, were historically utilized due to their convenience and utility in examining factors influencing civil unrest [ 20, 21]. However, owing to restrictions on violent content and monitoring by authoritarian regimes, their eficacy has been mitigated [22]. Consequently, our study leans towards diplomatic datasets. Our chosen dataset, ACLED, is disaggregated, regularly updated, and emphasizes disorder events. ACLED data has proven valuable in predicting conflict [ 23], and is publicly accessible via their API1. The data chronicles various conflict events with distinct descriptions, location, and time, and ensures reliability through a rigorous verification process. Although coverage periods vary across nations, the detailed structure and transparency foster academic research and informed decision-making. Our research aggregates these data weekly and categorizes them by event types, resulting in a dataset where each observation corresponds to the number of a specific event type within that week in a particular country.
Our AI-EWS employs a transformer model that focuses on predicting the number of fatalities across all countries over twelve weeks. Inspired majorly by the Time-series Dense Encoder (TiDE) model prominent in the domain of long-term forecasting [13], our design substitutes the dense encoder conventionally used in TiDE with an attention-based encoder, due to better results with the dataset in use in our study. The model, as in the original TiDE implementation, incorporates residual connections from input to output ensuring the preservation of linear activation, an approach backed by empirical evidence for its eficiency in time-series forecasting [14]. For a comprehensive understanding of the model’s operation and data flow, please refer to the detailed explanation provided under Figure 1. Overall, the model’s design is geared towards robust long-term prediction while maintaining a fundamental simplicity in its architecture, balancing advanced modeling techniques with practical forecasting reliability. The model is trained using the Negative Log Likelihood (NLL) loss function to optimize its probabilistic forecasts. Additionally, it’s worth noting that for each country, 12 weeks were retained for testing, 24 for validation, and the remaining weeks were allocated for training. 1Armed Conflict Location and Event Data Project (ACLED); https://acleddata.com/
Integrated Gradients (IG) is a technique utilized for attributing predictions of deep neural
networks to their input features, facilitating a deeper understanding of their behavior [
Sensitivity: This axiom dictates that for inputs and baselines difering in a single feature yet yielding diferent predictions, the difering feature must receive a non-zero attribution.
Implementation Invariance: Attributions should remain consistent for functionally equivalent networks. Networks are functionally equivalent if their outputs coincide for all inputs, despite potential diferences in their implementations. Failure to satisfy this axiom may indicate sensitivity to insignificant model aspects. IG computes attributions by following a straight-line path from a baseline input ′ to the input , evaluating gradients along this path. Specifically, IG along the ℎ dimension for inputs and ′ are calculated as: () = ( − ′) × ∫︁ 1 =0 (′ + ( − ′)) d, (1) where () represents the gradient of () along the ℎ dimension.
Furthermore, IG adheres to an additional axiom:
Completeness: Attributions sum up to the discrepancy between the output of at input and baseline ′. This axiom serves as a sanity check, ensuring the method comprehensively accounts for diferences.
In our study, the baseline was established as the mean matrix, a critical decision considering the MinMax scaling applied to our dataset. Notably, employing a baseline filled with mean values for each rescaled feature can assign significance to count features with zero values, especially in a dataset subjected to MinMax scaling. This decision was based on the assumption that the absence of unrest events might hold relevance for the model’s prediction. Therefore, we opted for this baseline matrix rather than a zero baseline matrix. The choice of IG is motivated by its transparency, simplifying the comprehension of attributions to input features. While SHAP and LIME, as leading and commonly used methods in XAI, provide in-depth explorations of model behaviors, IG’s clear computational approach provides an easier understanding, making it an efective preliminary step before advancing to more complex explanatory methods.
This study sets out to forecast the potential number of fatalities from February 13, 2024, to March 30, 20242, focusing on 168 countries that have recorded at least one fatality throughout their historical time series3.
The primary aim of this research is not to benchmark the prediction accuracy against other leading models but to explore the insights provided by the predictions of AI-EWS. Our investigation is centered around the application of integrated gradients, the chosen XAI methodology, to reveal the reasons behind these forecasts. The analysis initiates by pinpointing the crucial variables influencing the forecasting during the testing period. As depicted in Figure 3, these variables are visualized using boxplots and are arranged in descending order of their influence. For clarity, take the instance of a specific country: a value of 1 marks the highest absolute shift in Integrated Gradients, showcasing that a variable is critically influential in making predictions; a value of 0 suggests no shift, indicating the variable’s non-involvement in the prediction model; any value between 0 and 1 highlights the variable’s proportional relevance compared to the most impactful variable in that country. 2This timeframe spans the most recent twelve weeks of ACLED data available for each country up to April 9, 2024. 3This criterion is crucial as predicting deviations from zero fatalities where no prior occurrences exist is statistically improbable. Therefore, countries are assessed individually based on their historical data.
Additionally, the relevance of diferent time intervals within the forecasting model is scrutinized. The model encompasses a lookback period of 48 weeks to integrate the data leading up to a prediction. Figure 4 clarifies the weighting assigned to each subsequent week, arranged chronologically, which assists in understanding the adaptive significance throughout the considered period. To summarize, initial findings illuminate prominent patterns concerning the importance of variables and the dynamics of time intervals within the prediction framework. As elucidated in Figure 3, certain variables evidently carry more weight consistently across all analyzed countries. However, a review of Figure 4 portrays a more complex landscape. Although there exists a mild preference for recent weeks, variable importance demonstrates relative uniformity regardless of the elapsed time since the event. This reflection reveals a coherent strategy by the AI-EWS to value variables uniformly, irrespective of their temporal proximity to the predicted event.
In this study, we proposed a novel approach to conflict prediction on a global scale,
leveraging advanced transformer models and XAI methodologies. We applied our approach to a
comprehensive geopolitical dataset implemented using data obtained from the ACLED API.
The transformer model proposed is inspired by its original architecture [
This study was funded by the European Union (EU) - NextGenerationEU, in the framework of the GRINS - Growing Resilient, INclusive and Sustainable project (GRINS PE00000018 – CUP D13C22002160001). The views/opinions expressed are solely those of the authors and do not necessarily reflect those of the EU, nor can the EU be held responsible for them. [8] D. Muchlinski, D. Siroky, J. He, M. Kocher, Comparing random forest with logistic regression for predicting class-imbalanced civil war onset data, Political Analysis 24 (2016) 87–103. doi:10.1093/pan/mpv024. [9] C. Perry, Machine learning and conflict prediction: A use case, Stability: International
Journal of Security & Development 2 (2013) 56. doi:10.5334/sta.cr. [10] N. Beck, G. King, L. Zeng, Improving quantitative studies of international conflict: A conjecture, The American Political Science Review 94 (2000) 21. doi:10.2307/2586378. [11] S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, X. Yan, Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting, in: H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems, volume 32, Curran Associates, Inc., 2019. [12] G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, C. Eickhof, A transformer-based framework for multivariate time series representation learning, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD ’21, Association for Computing Machinery, 2021, p. 2114–2124. doi:10.1145/3447548.3467401. [13] A. Das, W. Kong, A. Leach, S. Mathur, R. Sen, R. Yu, Long-term forecasting with tide:
Time-series dense encoder, 2023. arXiv:2304.08424. [14] A. Zeng, M. Chen, L. Zhang, Q. Xu, Are transformers efective for time series forecasting?, AAAI Conference on Artificial Intelligence 37 (2023) 11121–11128. doi: 10.1609/aaai. v37i9.26317. [15] Y. Hao, L. Dong, F. Wei, K. Xu, Self-attention attribution: Interpreting information interactions inside transformer, 2021. arXiv:2004.11207. [16] J. D. Janizek, P. Sturmfels, S.-I. Lee, Explaining explanations: Axiomatic feature interactions for deep networks, The Journal of Machine Learning Research 22 (2021) 4687–4740. [17] A. Malhotra, R. Jindal, Xai transformer based approach for interpreting depressed and suicidal user behavior on online social networks, Cognitive Systems Research (2023) 101186. doi:10.1016/j.cogsys.2023.101186. [18] S. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions, 2017.
arXiv:1705.07874. [19] M. T. Ribeiro, S. Singh, C. Guestrin, "why should i trust you?": Explaining the predictions of any classifier, 2016. arXiv:1602.04938. [20] G. Korkmaz, J. Cadena, C. J. Kuhlman, A. Marathe, A. Vullikanti, N. Ramakrishnan, Combining heterogeneous data sources for civil unrest forecasting, in: Proceedings of the 2015 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining 2015, ASONAM ’15, ACM, 2015, p. 258–265. doi:10.1145/2808797.2808847. [21] R. Compton, C. Lee, J. Xu, A. M. Luis, T.-C. Lu, Using publicly visible social media to build detailed forecasts of civil unrest, 2014. doi:10.1186/s13388-014-0004-6. [22] M. Junior, P. Melo, A. P. C. da Silva, F. Benevenuto, J. Almeida, Towards understanding the use of telegram by political groups in brazil, in: Brazilian Symposium on Multimedia and the Web, WebMedia ’21, ACM, 2021, p. 237–244. doi:10.1145/3470482.3479640. [23] M. Halkia, S. Ferri, M. K. Schellens, M. Papazoglou, D. Thomakos, The global conflict risk index: A quantitative tool for policy support on conflict prevention, Progress in Disaster Science 6 (2020) 100069. doi:https://doi.org/10.1016/j.pdisas.2020.100069.