Artificial intelligence has emerged as a useful tool in peacebuilding. It offers new methods for analyzing complex social patterns, helping predict conflict. AI systems process vast amounts of data; they can identify correlations among historical grievances, economic disparities, and political tensions, allowing for much more targeted interventions. These insights help inform strategies that address the root causes of division in divided societies, whether through resource allocation or facilitating dialogue flow.

However, applying AI to conflict resolution isn’t simply about analyzing raw data. It requires designing systems that prioritize ethical engagement with communities, ensuring technology serves as a bridge rather than a barrier. Integrating AI into peace processes mandates careful consideration of how algorithms interpret human behavior; misaligned assumptions can perpetuate cycles of mistrust. Addressing these deep-seated challenges demands intense focus on the sources of data used to train AI models, since those datasets often reflect historical inequities and cultural biases.

Furthermore, data collection practices in conflict zones may inadvertently reinforce existing power imbalances, embedding systemic prejudices into the very tools meant to foster reconciliation. Recognizing these risks is essential for developing AI applications that genuinely support sustainable peace, rather than worsening the divisions they aim to solve.

What is AI?

Artificial intelligence represents a massive shift in modern society, encompassing systems built to handle tasks that normally require human intellect, reasoning, learning, problem-solving, and decision-making. Its scope spans many industries, including healthcare, finance, governance, and conflict resolution; these applications are increasingly shaping how societies manage complex challenges. Essentially, AI systems process enormous amounts of data using algorithms: they spot patterns, predict outcomes, and automate processes. This capability provides efficiency and scalability in domains where human oversight alone simply won’t suffice.

The technology’s adaptability has spurred the creation of diverse types of AI. Narrow AI specializes in specific tasks, like image recognition or language translation; meanwhile, general AI remains theoretical but promises broader cognitive capabilities. While general intelligence hasn’t been realized yet, narrow AI is already everywhere, it’s present in virtual assistants and autonomous vehicles, making its influence on society undeniable (Building trust in data and AI for conflict prevention: Three lessons).

In the context of peacebuilding, AI plays a particularly important role, offering tools to analyze data, forecast conflict risks, and support diplomatic negotiations. For instance, AI-driven platforms can process satellite images or social media activity to detect early warning signs of violence, enabling timely interventions. These capabilities have been observed in regions like Syria and Bangladesh; there, AI applications are reshaping the conditions for peace by making dialogue and resource allocation easier. Furthermore, because AI draws information from diverse sources, it boosts transparency, helping stakeholders make well-informed decisions. However, integrating this technology into peacekeeping efforts presents challenges: the reliability of data and the integrity of algorithms become crucial factors in building trust among conflicting parties (Press Release - The Double-Edged Sword of AI in Peacebuilding).

Trust is emerging as a defining challenge for conflict prevention, particularly when accurate information and impartiality are paramount. The effectiveness of AI in fostering peace depends not only on the credibility of its information but also on the transparency of its decision-making process. Where historical grievances run deep, AI’s perceived neutrality can either strengthen or undermine peacebuilding initiatives. For example, if an AI system is trained using biased data, its recommendations might inadvertently favor certain groups, worsening existing divisions. This issue is compounded by current AI models; they often rely on datasets that reflect historical inequalities, such as English-language dominance in training data. Such biases can skew outcomes in conflict settings where language and cultural nuances play a critical role in communication and reconciliation.

AI in conflict resolution

Artificial intelligence’s potential for solving complex conflict resolution problems in divided societies has drawn much exploration. Integrating AI into peacebuilding efforts raises crucial questions: how can technology support, yet also complicate, the process of fostering trust and reconciliation? Fundamentally, one of the most pressing issues is building confidence, specifically, developing faith in the data and AI systems themselves.

The Data for Peace Conference 2026 highlighted that trusting data, analysis, and institutions has emerged as a defining obstacle when utilizing AI for conflict prevention. Without reliable, transparent data, AI’s ability to inform decision-making is limited; moreover, the risk of perpetuating existing biases or power imbalances only grows. This situation demands aligning technical capability with ethical and social responsibility, ensuring that AI doesn’t become just another tool for division, but instead a mechanism designed to foster inclusive dialogue (Data for peace: how novel data sources and technology can enhance).

AI enhances human decision-making in conflict resolution by processing large amounts of information and identifying patterns that traditional methods might miss. However, this benefit only materializes if the data used to train these systems accurately reflects the diverse realities of the involved communities. The Peacebuilding Loop framework makes it clear: data choices are inherently peacebuilding decisions. The selection of sources, the categories used to label individuals or groups, and the distribution of insights all carry implications for power dynamics. For instance, an AI system trained on data that disproportionately represents some groups while ignoring others can inadvertently reinforce existing inequalities. This emphasis underscores the importance of developing AI tools through participatory approaches involving local stakeholders; they’ve got to be empowered to shape both the data inputs and the algorithms influencing conflict resolution strategies (Nd).

Addressing bias in AI for peacebuilding requires a dual focus, demanding reform on both technical and institutional levels. Biases embedded in training data can lead to skewed outcomes, often reinforcing stereotypes or marginalizing minority voices. To counter this problem, developers and policymakers must prioritize transparency across data collection and algorithmic design, ensuring diverse perspectives are integrated into the system’s architecture. Furthermore, institutions deploying AI must establish mechanisms for accountability, like independent audits or community oversight panels, to monitor the impact of AI interventions. These measures aren’t just helpful; they prevent the technology from becoming a mere tool for surveillance, control, or exclusion, which could exacerbate tensions rather than resolving them (Nd).

Biases in data collection

Integrating artificial intelligence into conflict prevention highlights how critical trust in data and analysis is; yet, that trust remains fragile. As the Data for Peace Conference 2026 highlighted, the core challenge lies in making sure both data and AI systems are perceived as reliable and impartial. This reliability is especially vital in divided societies where misinterpretation or manipulation carries high stakes.

However, this trust falters when data collection processes themselves are flawed. For example, while disaggregated conflict event data offers much detail, it also introduces significant risks. Such data often reflect historical patterns that skew toward certain regions, groups, or events; creating a directional bias that distorts understanding. Recent analyses have noted this phenomenon, fueling skepticism about the objectivity of conflict data, particularly when those findings inform peacebuilding strategies. Misperceptions of bias can quickly undermine confidence in AI systems, complicating efforts to leverage technology for reconciliation (Nd).

Growing recognition of these difficulties has spurred calls for human rights-centered frameworks in AI design. The 2025 press release underscores the necessity of aligning AI development with ethical principles, which is key to preventing harmful biases. This approach emphasizes transparency in data collection, accountability in algorithmic decision-making, and inclusivity throughout the entire design process. By prioritizing human rights, AI systems can be structured to challenge existing power structures and amplify historically excluded voices.

Case Studies and Examples

Artificial intelligence increasingly influences conflict resolution in divided societies. It enables data-driven approaches designed to identify early warning signals and predict potential escalations. In regions marked by historical tensions, AI systems analyze social media, satellite imagery, and communication patterns; they detect subtle shifts in public sentiment or resource distribution that could fuel conflict. For instance, a 2026 initiative in a region with longstanding ethnic divisions used AI algorithms to monitor and categorize online interactions, helping local mediators intervene before disputes escalated. This approach underscores the importance of trust in data and AI systems, as highlighted by the Data for Peace Conference 2026. The conference stressed that credibility in these technologies hinges on transparency and collaboration with affected communities. These efforts show how AI can provide actionable insights; however, they also reveal a critical need to balance technological capabilities with ethical considerations.

Successful setups of AI in peacebuilding often focus on bridging information gaps and fostering dialogue. One notable project involves a post-conflict area where AI-powered tools helped map community networks and identify key influencers, enabling targeted mediation efforts. By analyzing communication patterns and social connections, the system allowed peacebuilders to prioritize interactions that could strengthen trust between rival groups. Another case showcased AI’s ability to process and translate vast amounts of multilingual data; this allows organizations to better understand marginalized groups’ perspectives and tailor interventions accordingly. These examples illustrate how AI can enhance the efficiency and inclusivity of peacebuilding, but they also highlight the importance of aligning technical solutions with the complex realities of human relationships and cultural contexts.

Despite these successes, AI faces much limitations when addressing deep-rooted social divisions. Many conflicts are fueled by historical grievances, economic inequities, or identity-based tensions that can’t be resolved through data analysis alone. Furthermore, in some instances, AI systems have inadvertently exacerbated divisions by reinforcing existing biases within datasets or failing to account for the nuances of local dynamics. For example, an AI tool designed to allocate humanitarian resources in a divided region might prioritize areas with more accessible data, thereby neglecting communities that lack digital infrastructure. This underscores the challenge of ensuring that AI systems aren’t just technically sound, but are also socially responsive. Additionally, relying on technology can create a false sense of control, diverting attention from the need for sustained grassroots engagement and institutional reforms.

Bias within AI systems poses a critical risk to peacebuilding efforts.

Sources

  1. Building trust in data and AI for conflict prevention: Three lessons. Available at: https://www.unssc.org/news-and-insights/blog/building-trust-data-and-ai-conflict-prevention-three-lessons-data-peace [Accessed: 05 August 2026].
  2. Press Release - The Double-Edged Sword of AI in Peacebuilding. Available at: https://www.ipie.info/news/press-release-the-double-edged-sword-of-ai-in-peacebuilding-why-human-rights-must-guide-its-design [Accessed: 05 August 2026].
  3. Nd. Available at: https://peacepolicy.nd.edu/2026/02/23/putting-ai-in-the-peacebuilding-loop/ [Accessed: 05 August 2026].
  4. Data for peace: how novel data sources and technology can enhance. Available at: https://www.cambridge.org/core/journals/data-and-policy/article/data-for-peace-how-novel-data-sources-and-technology-can-enhance-peace/8C3521EF9D26646B050C6EF9554D37E4 [Accessed: 05 August 2026].