In recent years, violence linked to social unrest and group conflict has grown across many regions. While governments and organisations have tried to respond, many efforts are reactive and often come too late. The use of artificial intelligence (AI) may change this. By analysing micro-level data—such as individual behaviour, local tensions, or online speech—AI offers a new way to predict early signs of violent conflict.
This article looks at how AI works in this field, what kinds of data it uses, and how this can help shape better conflict policies. We also consider the risks and limits of these new tools.
What Is Micro-Level Data?
Micro-level data refers to information that comes from individuals, households, or small groups. It includes things like:
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Social media posts
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Online search patterns
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Survey results
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Community incident reports
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Local economic indicators
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Public health records
This type of data helps show how people feel, act, or respond to events in real-time. When collected across many individuals, it gives a detailed view of tension within a community.
How AI Uses This Data to Spot Risk
AI systems can process large amounts of data faster than humans. Machine learning tools find patterns, trends, or changes that may signal a growing risk of violence. Here are some examples:
Sudden Shifts in Online Speech
AI can track words or phrases linked to hate speech or calls for action. A rise in hostile language within a group may point to growing anger or plans for protest.
Location-Specific Tensions
If AI detects many small incidents—such as school fights, police reports, or rising food prices—in the same area, it may show pressure is building.
Link Between Household Stress and Public Violence
Data about rising debt, job loss, or domestic conflict can help highlight where households feel trapped or angry. These stresses may later feed group actions like riots or protests.
Real-World Use Cases
Kenya’s Election Monitoring
During past elections, organisations used AI to track local speech on social media. By spotting areas with rising hate speech, they warned teams on the ground to step in early.
Crisis Maps from UN Agencies
The UN has used AI-powered crisis maps to track conflict signals. These tools combine weather data, food prices, and displacement reports to assess areas at high risk.
Gang Violence in Latin America
In cities like Medellín, AI systems have used local crime and school absence data to help predict gang recruitment and street violence patterns.
Why Micro-Level Focus Matters
Large-scale conflict often begins with small acts. Micro-level data helps detect these early signs before they turn into mass violence. Focusing only on national data or political events may miss what happens in homes, schools, or streets.
For example, tension between two ethnic groups may start with insults online or fights at a local market. If these signals are seen early, steps can be taken to reduce risk through talks, mediation, or support services.
Benefits for Policymakers and Communities
Faster Response
Early warnings let authorities act before a situation grows worse. This could mean adding support in schools, opening crisis hotlines, or speaking directly with at-risk groups.
Better Resource Use
With clear signals, funds and staff can be sent to the right areas. This avoids wasting time or money on places not at risk.
Support for Local Actors
Data helps community groups understand their own risks and prepare better responses. It also supports peacebuilding groups who need facts to make their case.
Risks and Challenges
Privacy and Consent
Collecting personal data raises serious concerns. AI tools must follow clear rules on privacy, consent, and use of sensitive information.
Misuse or Bias
If not trained well, AI tools may show false signals or reflect social bias. This could lead to wrong actions—such as unfair arrests or increased surveillance of peaceful groups.
Dependence on Tech
AI can help but it should not replace local knowledge, community leaders, or human judgement. Tools must support—not replace—people on the ground.
The Role of EU Programmes and Research
The European Union funds projects that explore the use of AI in peace and security. These include Horizon Europe calls on crisis prediction, digital tools for governance, and social inclusion. EU-funded studies are also testing how micro-level data can guide peacebuilding in post-conflict zones.
For example, projects that track the effect of household stress on community conflict can shape better support services and improve long-term peace.
What This Means for the Future
AI cannot stop violence alone. But when used with care and strong local support, it helps spot early risks and guide smarter actions. More accurate data, faster alerts, and better planning could save lives and reduce harm.
Researchers, policymakers, and local groups must work together to shape how these tools are used. When done right, AI offers a way to protect people and prevent violence—before it starts.