Policy Significance Statement

In an era marked by complex global challenges, the intersection of technology, data, and peacebuilding is emerging as a game-changing force. This field holds immense policy significance, offering innovative solutions to some of the world’s most persistent and deeply entrenched conflicts. Computer-aided, algorithmic, and data-driven approaches to peace and peacebuilding can help to identify and target the root causes of conflict, improve the quality of relationships, facilitate mediation and consensus finding during peace negotiations, assist in reconstruction efforts through “peace engineering”, and can also improve the evaluation and monitoring of peacebuilding activities.

2. The papers

Aras and Westlake (Reference Aras and Westlake2025) open this collection by examining augmented intelligence-based prediction models aimed at enhancing early-warning capabilities in conflict settings such as Afghanistan. Operating in limited-data environments, they show that blending human judgment with machine learning increases the accuracy of forecasts of multidimensional vulnerabilities. While these innovations carry promise, the authors draw attention to ongoing technical and ethical concerns, urging the development of robust human oversight procedures to prevent errors and avoid excessive reliance on computational results.

Turning to the broader potential of artificial intelligence (AI) for promoting global peace, Giovanardi (Reference Giovanardi2024) critically examines the dual role of AI in international relations, highlighting its capacity both to intensify violence and to enable conflict prevention. He underscores the need for more rigorous impact assessment, grounded in a clear conceptualisation of peace, and proposes evaluative measures along the negative–positive peace continuum. Furthermore, he advocates for the establishment of robust policy frameworks supported by a structured three-phase investment strategy: systematic mapping of initiatives, empirical evaluation of their effectiveness, and the strategic allocation of resources to the most impactful interventions.

Mueller et al. (Reference Mueller, Rauh and Seimon2024) describe a large-scale data repository and advanced forecasting technique for the onset of armed conflict, structured at both the national and grid-cell levels. Their methodology involves mining global news sources through topic modelling to predict escalations in violence with heightened granularity. Crucially, their findings underscore how more precise risk evaluations can better inform peacebuilding strategies, allowing for more efficient resource allocation and improved outcomes in conflict-affected areas.

In a related yet distinct approach, Luna-Pla (Reference Luna-Pla2024) employs network analysis to expose powerful elites whose activities can derail peace and stability. Drawing on the example of Guatemala’s anti-impunity commission, the paper demonstrates how mapping connections between corrupt or coercive actors reveals structural problems that undermine governance. By pinpointing these networks and their principal beneficiaries, policymakers and activists can design more pragmatic reforms that directly address institutional weaknesses.

Shifting to frameworks for global stewardship, Kirchschlaeger (Reference Kirchschlaeger2024) advocates the creation of an International Data-Based Systems Agency (IDA) under the United Nations. Inspired by the International Atomic Energy Agency’s model, IDA would oversee the responsible use of AI, data analytics, and other emerging digital tools. The proposed body would combine strict regulatory standards with supportive guidance, ensuring transparency, accountability, and respect for human rights in the pursuit of peaceful technological applications.

Mac Ginty and Firchow (Reference Mac Ginty and Firchow2024) examine the “data myth” in peacebuilding: the pervasive assumption that simply amassing and accelerating data collection inherently leads to better policy decisions. The authors argue for a critical evaluation of the origins, ownership, and biases embedded within data, suggesting that relying solely on quantitative metrics can overshadow ethical and contextual nuances. They propose a blend of robust data-gathering with participatory methods to ground policies in both reliable statistics and local voices.

Focusing on machine learning for civil war prediction, Murphy et al. (Reference Murphy, Sharpe and Huang2024) demonstrate that advanced computational tools can illuminate pathways to conflict earlier than conventional approaches allow. Although they caution that such methods often struggle with interpretability, the authors nonetheless see them as valuable for allocating peacekeeping personnel, strengthening diplomatic negotiations, and anticipating where conflict may flare next.

Hawke et al. (Reference Hawke, Puig Larrauri, Sutjahjo and Cerigo2024) describe a collaborative model for generating specialised text classifiers. By drawing on close consultation with peace practitioners, they refine algorithms to detect and categorise harmful content online more accurately. This process, requiring iterative design and context-specific knowledge, points to a compelling strategy for tackling rising digital polarisation and incitement to violence.

Finally, Tauchnitz (Reference Tauchnitz2025) redefines peace as a relational phenomenon and human behaviour, emerging through social connections, interactions, and relationships that can be mapped into networks of peace (and violence). As global connectivity increases and digital networks are gaining influence, the quantity and quality of human interactions also change. She argues that everyday behaviours—both online and offline—can sustain or erode peace, underscoring the need to integrate normative ethics with empirical methods such as social network analysis.

Collectively, these nine contributions illustrate how innovative data-driven strategies, interpreted through ethical frameworks and close collaboration among diverse stakeholders, can strengthen efforts to prevent conflict, address structural injustice, improve relationships, and cultivate sustainable positive peace. They call on both scholars and practitioners to ensure that the introduction of sophisticated technological tools is always guided by principles of accountability, human dignity, respect for privacy and personal freedoms, and practical relevance.

3. Thematic reflections on contributions

Across the papers, several themes emerged, including how the design of AI and data systems can respect human rights, address data asymmetries, and function responsibly across low-tech and high-tech environments. Below, we explore these themes further and identify areas for further exploration.

4. Technological adoption and data asymmetries

Future scholarship should examine the comparative value of “low-tech” and “high-tech” approaches to peacebuilding, particularly in settings marked by data scarcity or infrastructural limitations. Mac Ginty and Firchow (Reference Mac Ginty and Firchow2024) caution against the uncritical privileging of high-tech, data-intensive solutions, arguing that locally appropriate, culturally embedded, and often lower-tech alternatives may be more legitimate and effective in fragile contexts. This theme points to the importance of addressing structural disparities in data access, technical capacity, and digital infrastructure—what might be termed “data asymmetries.” Further research is needed to critically assess how these asymmetries shape peacebuilding outcomes and to design inclusive, context-sensitive technological interventions.

5. Final reflection

Nearly four decades ago, Joseph Nye and Sean Lynn-Jones (Reference Nye and Lynn-Jones1988) highlighted the necessity for scholars in international security to anchor their work in robust theoretical frameworks and historical context, warning against the pitfalls of over-specialisation, faddishness, and the fixation on novel tools at the expense of cumulative insight. Their reflection remains pertinent as we navigate integrating technology and data into peacebuilding efforts. The current enthusiasm for AI and predictive analytics in conflict prevention and resolution could mirror past tendencies to favour immediate, tool-driven solutions over comprehensive, context-aware approaches. In introducing this special issue, we advocate for a balanced perspective that combines innovation with critical reflection, ensuring that advancements in peacebuilding are both effective and grounded in a deep understanding of the complexities involved.

The articles of this special issue collectively demonstrate that emerging technologies—when applied thoughtfully and ethically—can serve as powerful tools to enhance early warning systems, support inclusive policy design, expose structural injustice, generate new human connections, and reframe our very understanding of peace. Yet, they also remind us that technological innovation is not a neutral force. Without critical reflection, participatory engagement, robust governance, and legal frameworks, data and technology risk reproducing the very harms they aim to address.

Looking ahead, the field of data for peace must remain grounded in a holistic, human-centred vision of peace—one that recognises the importance of lived experiences, relational dynamics, and contextual nuances alongside algorithmic predictions and computational power. As the boundaries between local and global, online and offline, and conflict and peace continue to blur, we need adaptive frameworks that can capture complexity, anticipate risks, and harness digital tools to promote human dignity, personal freedom, accountability, and resilience.

Ultimately, the promise of data and technology for peace lies not only in what they can measure or predict but in how they can help societies re-imagine, co-create, and sustain more just and peaceful futures.