When Conflict Forecasting Helps And When It Fails

Introduction

AI systems that try to forecast political violence promise something unusually valuable: more time. If governments, aid agencies and peacebuilding organisations can identify regions drifting towards conflict months before violence erupts, they may be able to move resources, support negotiations, protect civilians and prevent crises from escalating. In the broader vision of AI helping civilisation become more resilient, conflict forecasting is one of the clearest examples of using machine intelligence not for consumer convenience but for reducing large-scale human suffering.

Yet these systems face a difficult problem. Violent conflict is relatively rare, politically complex and shaped by human decisions that can change suddenly. A forecasting model may correctly identify many genuine risks while still producing large numbers of false alarms. Those errors can damage trust, stigmatise communities, distort policy priorities and, in some circumstances, worsen the tensions they were meant to reduce. The question is therefore not simply whether AI can predict conflict, but how such predictions should be interpreted, communicated and governed.

When Conflict Forecasting Helps

Conflict prediction has existed for decades, but recent systems use machine learning to analyse far larger datasets than traditional expert-driven approaches. Modern platforms combine information from conflict event databases, economic indicators, demographic trends, environmental stress, migration patterns, social media signals and satellite imagery to estimate the probability of future violence. The goal is rarely to predict a specific event with certainty. Instead, these systems generate risk assessments that help decision-makers focus attention on places where violence may become more likely.[VIEWS]viewsforecasting.orgVIEWSViolence Early-Warning SystemThe Violence & Impacts Early-Warning System (VIEWS) is an open-source project leveraging machine learni…

One of the most influential examples is the Violence & Impacts Early-Warning System (VIEWS), developed by researchers associated with the Peace Research Institute Oslo and collaborating institutions. VIEWS produces forecasts of armed conflict and conflict-related fatalities across countries and subnational regions, using machine learning models trained on large historical datasets. Its forecasts are publicly available and are used by researchers, humanitarian actors and policymakers interested in conflict prevention.[VIEWS]viewsforecasting.orgVIEWSViolence Early-Warning SystemThe Violence & Impacts Early-Warning System (VIEWS) is an open-source project leveraging machine learni…

The attraction of such systems is straightforward. Human analysts can miss weak signals spread across thousands of reports and datasets. Machine learning systems can process large volumes of information continuously and identify statistical patterns that would be difficult for individual experts to detect. Several reviews of conflict early-warning systems argue that data-driven forecasting can improve situational awareness and help institutions allocate scarce prevention resources more effectively.[ScienceDirect]sciencedirect.comA review and comparison of conflict early warning systemsby EG Rød · 2024 · Cited by 53 — A conflict early warning system (C…[EU Institute for Security Studies]iss.europa.eupower and limits data peaceEU Institute for Security StudiesThe power and limits of data for peace12 Jan 2024 — To help avoid deadly violence and its consequences i…

Within an AI-bloom framework, the importance of these tools is not limited to military affairs. Preventing wars protects scientific institutions, economic development, public health systems and international cooperation. A civilisation capable of anticipating conflict more effectively may be better able to preserve the conditions needed for long-term human flourishing.

How Violence Forecasting Systems Use Data

Modern conflict forecasting systems generally operate as probabilistic models rather than deterministic predictors.

A typical system draws on several categories of information:

• Historical conflict data, including previous battles, fatalities and ceasefire violations.
• Political indicators, such as regime instability, elections, repression or state weakness.
• Economic measures, including inflation, unemployment and income shocks.
• Environmental variables, such as drought, water stress or crop failure.
• Population movements, including refugee flows and internal displacement.
• Open-source information, ranging from news reports to social media discussions.
• Geospatial data, including satellite observations and infrastructure mapping.[VIEWS]viewsforecasting.orgVIEWSViolence Early-Warning SystemThe Violence & Impacts Early-Warning System (VIEWS) is an open-source project leveraging machine learni…[World Resources Institute]wri.orgWorld Resources InstituteGlobal Early Warning ToolThe tool predicts conflict using 15-20 global indicators as model inputs. So far, it ha…

Machine learning models search for relationships between these variables and subsequent violence. Some newer systems employ deep-learning architectures that analyse spatial and temporal patterns simultaneously, attempting to detect how instability spreads across regions over time.[arXiv]arxiv.orgNext-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal LearningJune 8, 2025…Published: June 8, 2025

Importantly, most systems do not claim to know why a conflict will occur. They identify statistical correlations that have historically preceded violence. This distinction matters because policymakers may mistakenly treat model outputs as causal explanations rather than probabilistic warnings.

Researchers working on conflict forecasting repeatedly emphasise uncertainty. Forecasts are generally expressed as risk levels, probability estimates or expected fatality ranges rather than firm predictions.[VIEWS]viewsforecasting.orgVIEWSViolence Early-Warning SystemThe Violence & Impacts Early-Warning System (VIEWS) is an open-source project leveraging machine learni…

Cases Where Early Warning Improved Response

Evidence suggests that data-driven early-warning systems can contribute to better preparedness, even when they do not perfectly predict specific events.

The European Union’s conflict early-warning framework combines quantitative forecasting tools with qualitative assessments from regional experts and diplomats. Rather than replacing human judgement, forecasting systems help identify countries and regions requiring closer attention. Analysts can then investigate local dynamics and determine whether preventive action is warranted.[EU Institute for Security Studies]iss.europa.eupower and limits data peaceEU Institute for Security StudiesThe power and limits of data for peace12 Jan 2024 — To help avoid deadly violence and its consequences i…

The Water, Peace and Security partnership provides another example. Its Global Early Warning Tool combines hydrological, political and socioeconomic indicators to identify locations where water stress may increase the risk of instability. The system is designed to support preventive interventions before tensions become violent. According to project documentation, it has demonstrated substantial success in identifying areas that later experienced conflict, though performance is stronger for ongoing conflicts than for entirely new outbreaks.[World Resources Institute]wri.orgWorld Resources InstituteGlobal Early Warning ToolThe tool predicts conflict using 15-20 global indicators as model inputs. So far, it ha…

Researchers also point to cases where elevated-risk forecasts corresponded with real-world escalation. VIEWS documentation notes that Ukraine entered higher-risk categories shortly before the 2014 Crimea crisis. Such examples help explain why governments continue investing in forecasting systems despite their imperfections.[Conflict Forecast]conflictforecast.orgConflict ForecastPrevention GainsStages 5-10, elevated risk: These stages generally capture countries that are on the precipice of confli…

The broader lesson is that forecasting can be useful even when it is not highly precise. If warnings trigger closer monitoring, contingency planning or diplomatic engagement, they may create opportunities for prevention that would otherwise be missed.

The False Alarm Problem

The central challenge is that conflict is a low-frequency event.

When an outcome is relatively rare, even a model with respectable accuracy can generate many false positives. A system may correctly identify most future conflicts while simultaneously flagging numerous places where violence never occurs. Statistically, this is a familiar problem across forecasting domains, from fraud detection to disease surveillance.[Medium]medium.comFalse Positives: The Hidden Cost Center in Production AIFalse positives (FPs) are “false alarms”: the system predicts positive (fra…

For conflict forecasting, false alarms carry unusually high costs.[youtube.com]youtube.comUsing AI In Warfare Could Increase Civilian Casualties | Professor Elke SchwarzConfusion Matrix: False Alarms - Intro to Machine Learning…

A region labelled as high risk may experience:

In extreme cases, actions taken in response to a warning can alter the political environment itself. A government that believes unrest is imminent may deploy security forces aggressively. Opposition groups may interpret those deployments as hostile. The forecast can then become part of the chain of events that shapes reality.

This creates a paradox. Effective early warning requires drawing attention to potential dangers before violence occurs. But many warnings will inevitably concern places where violence never materialises. It is often difficult to determine whether those warnings were mistakes or whether preventive actions helped avert the predicted outcome.

Why New Conflicts Are Hardest To Predict

Many forecasting systems perform best when analysing places that have already experienced violence.

Researchers have repeatedly found that models are more successful at predicting the continuation or recurrence of conflict than forecasting entirely new outbreaks. Historical violence is one of the strongest predictors of future violence, making recurring conflicts easier for algorithms to identify.[JRC Publications]publications.jrc.ec.europa.euJRC Publications The Future of Conflict Early WarningMachine learning has shown promise in forecasting political violence, but it struggles to predict the…

The problem is that some of the most important conflicts emerge in places that previously appeared relatively stable.

Political revolutions, sudden coups, leadership crises, unexpected military invasions and rapid breakdowns of social order often involve factors that are poorly represented in historical datasets. Forecasting systems trained on past patterns may struggle when political behaviour changes abruptly or when unprecedented events occur.

This limitation reflects a broader challenge for AI. Pattern-recognition systems generally perform best when future conditions resemble past conditions. They are less reliable when confronted with genuinely novel situations.

For civilisational resilience, this distinction matters because some of the most consequential conflicts may be precisely the ones that historical data struggles to anticipate.

Bias, Escalation and Misuse Risks

False alarms are not distributed evenly.[youtube.com]youtube.comUsing AI In Warfare Could Increase Civilian Casualties | Professor Elke SchwarzConfusion Matrix: False Alarms - Intro to Machine Learning…

Conflict forecasting systems depend heavily on the data used to train them. If some regions receive more media coverage, more NGO reporting or more intensive monitoring, they may generate richer datasets than neglected areas. Models can therefore become more sensitive to instability in highly observed regions while overlooking risks elsewhere.[ResearchGate]researchgate.netArtificial Intelligence in Conflict Prediction and PreventionMuch more specifically, an emphasis on data can amplify bias or…[Palo Alto Networks]paloaltonetworks.comWhat Is AI Bias? Causes, Types, & Real-World ImpactsAI bias is a systematic tendency of an AI system to produce outputs that unfairly fav…

Several risks follow from this.

Reinforcing existing assumptions

If a country has long been viewed as unstable, historical records may contain extensive documentation of violence. Models trained on those records can repeatedly classify the same country as high risk, reinforcing existing perceptions even when conditions improve.

This may create a feedback loop in which historical reputation influences future forecasts more than present realities.

Political manipulation

Forecasting systems can also become political tools.

Governments may selectively publicise forecasts that support their preferred policies while ignoring forecasts that challenge them. Authoritarian regimes could potentially use predictive systems to justify surveillance, restrictions on political opponents or preventive security measures against groups labelled as risky.[RO Journals]rojournals.orgRO JournalsConflict Early-Warning with Big Data: Ethics, Accuracy, and…April 23, 2026 — by H Kato Nabirye — However, these innovations…Published: April 23, 2026

The problem becomes particularly acute when models are opaque. If outsiders cannot examine how predictions were generated, it becomes difficult to distinguish evidence-based warnings from politically convenient interpretations.

Escalation through prediction

Perhaps the most unusual risk is that predictions themselves can influence behaviour.

A public forecast warning of likely unrest may affect investor decisions, migration choices, diplomatic relations or military planning. Some actors may respond defensively; others may exploit the forecast strategically.

In this sense, conflict forecasting differs from predicting rainfall. Human beings react to predictions, and those reactions can change outcomes. Early-warning systems therefore operate inside the systems they are attempting to observe.

Why Human Judgement Still Matters

Most experts do not advocate fully automated conflict prevention.

Reviews of conflict early-warning systems increasingly emphasise hybrid approaches that combine machine-generated forecasts with local expertise, diplomatic reporting and contextual analysis.[EU Institute for Security Studies]iss.europa.eupower and limits data peaceEU Institute for Security StudiesThe power and limits of data for peace12 Jan 2024 — To help avoid deadly violence and its consequences i…[ScienceDirect]sciencedirect.comA review and comparison of conflict early warning systemsby EG Rød · 2024 · Cited by 53 — A conflict early warning system (C…

Human analysts can evaluate factors that are difficult to encode in datasets:

Machine learning systems can highlight patterns and anomalies, but they often cannot explain their significance in ways that policymakers can readily assess.

Transparency also matters. Researchers reviewing early-warning systems have argued for greater openness around data sources, modelling assumptions and performance metrics. When decision-makers understand how a forecast was generated, they are better positioned to judge whether it deserves confidence. ScienceDirect[HCSS]hcss.nlConflict Early Warning Systems HCSS 2022Practices, Principles and Promises of Conflict Early…by T Sweijs · 2022 · Cited by 9 — Most EWS predict or give a risk assessment abou…

What This Means For AI And Civilisational Resilience

Conflict forecasting illustrates both the promise and the limits of AI-enabled resilience.

The optimistic case is credible. Better forecasting can help governments and humanitarian organisations identify emerging dangers earlier, allocate resources more effectively and potentially prevent violence before it escalates. In a future where AI systems become more capable, richer data streams and improved modelling could strengthen humanity’s ability to anticipate crises across entire regions.[Emerging Tech & Security Center]cetas.turing.ac.ukEmerging Tech & Security Center The State of AI for Strategic WarningEmerging Tech & Security CenterThe State of AI for Strategic WarningMay 7, 2025 — AI has the potential to help policymakers spend more ti…Published: May 7, 2025[Trends Research]trendsresearch.orgthe impact of ai and machine learning on conflict preventionThe Impact of AI and Machine Learning on Conflict…2 May 2025 — The rise of AI and machine learning (ML) can contribute to global peace…Published: May 2025

But conflict forecasting also shows why more intelligence does not automatically produce better outcomes.

Predictions can be wrong. Data can be biased. Institutions can overreact. Political actors can misuse forecasts. And because conflict involves strategic human behaviour, forecasts may alter the very systems they are trying to predict.

The strongest role for these tools may therefore be as decision-support systems rather than decision-makers. Used carefully, they can expand human awareness and shorten response times. Used carelessly, they can create a false sense of certainty, encourage overconfidence and amplify political tensions.

For the larger AI-bloom vision, that lesson is important. Advanced AI may substantially improve civilisation’s ability to foresee risks, but resilience depends not only on prediction. It also depends on judgement, accountability, institutional trust and the ability to act on warnings without turning uncertainty itself into a new source of instability.

Amazon book picks

Further Reading

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Superforecasting

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The Better Angels of Our Nature

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Provides broad context for understanding patterns of violence.

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Highlights forecasting limits and surprise events.

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The Fog of Peace

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Endnotes

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Additional References

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