Artificial intelligence is increasingly discussed as a tool that may support negotiation, mediation, early warning, and dispute resolution. Much of this discussion, however, still treats AI as a collection of separate applications: a system that summarizes documents, a chatbot that helps people prepare for a negotiation, a platform that supports online dispute resolution, or an algorithm that detects signs of conflict escalation. These applications may be useful, but they capture only part of the challenge. If AI is to make a meaningful contribution to conflict management, we should not think about it only as a set of tools. We should think of it as public infrastructure, available to everyone, not only to the privileged.
This is the central argument of our contribution, co-authored with Peter Kesting, to the Expert Perspectives on New Paths to Peace edition of Negotiation and Conflict Management Research. The edition asks how scholars and practitioners might broaden the repertoire of approaches available for preventing, managing, and resolving conflict. Its premise is not that despite a wide range of established methods of diplomacy, mediation, negotiation, or peacebuilding, the number and intensity of conflict has become higher than every before. It invites us to examine what additional capacities may be needed in a world in which conflicts are increasingly complex, information-rich, fast-moving, and difficult to contain.

Our article proposes that AI-based conflict management should be developed as public-interest infrastructure. By this we mean a shared socio-technical system designed to help individuals, organizations, communities, and institutions understand conflicts better, assess risks more carefully, explore options more systematically, and access appropriate forms of support. The key point is that such infrastructure should not be created primarily as a private advantage for those who can afford sophisticated tools. It should be oriented toward broad collective benefit, much like other forms of infrastructure that support social functioning.
From private tools to public goods
The language of public goods is useful because it shifts attention from individual use to collective value. In economic theory, public goods are often described as resources from which people cannot easily be excluded and whose use by one person does not necessarily reduce their availability to others. AI-based conflict management infrastructure would not be a public good in this strict economic sense. It would require investment, governance, maintenance, institutional oversight, and rules of access. Nevertheless, the public-good perspective is helpful because the benefits of better conflict management extend far beyond the immediate users of a particular system.
When conflicts are managed constructively, the positive effects are rarely limited to the parties directly involved. Organizations avoid disruption, communities preserve relationships, public institutions maintain legitimacy, and societies reduce the human and economic costs of escalation. Conversely, poorly managed conflicts often create negative externalities: mistrust, polarization, violence, litigation, displacement, institutional paralysis, and long-term social fragmentation. For this reason, improving access to high-quality conflict management support should be understood not only as a private service, but also as a public interest.
This has important implications for how AI systems in this field should be designed. A commercial tool may be optimized for user convenience, market share, or proprietary advantage. A public-interest infrastructure must be optimized for trust, accessibility, neutrality, accountability, and responsible use. These criteria are particularly important in conflict settings because the information involved is sensitive, the stakes are often high, and the legitimacy of the process matters as much as the quality of the analytical output.
What AI can contribute to conflict management
AI will not replace mediators, negotiators, diplomats, community leaders, judges, or political decision-makers. Nor should it. Conflict management is not simply a problem of information processing. It involves emotions, identity, power, legitimacy, trust, moral judgment, and human responsibility. However, AI can support human actors by helping them deal with complexity more effectively.
One possible contribution concerns early warning and risk analysis. Conflicts often produce signals before they escalate, but these signals may be dispersed across different sources, interpreted inconsistently, or noticed too late. AI systems can help organize large amounts of information, identify patterns, make emerging risks more visible, and help human actors recognize developments that require attention.
A second contribution lies in conflict mapping. Many disputes involve multiple stakeholders, overlapping interests, competing narratives, and hidden assumptions. Parties may focus on stated positions while overlooking underlying needs, constraints, or fears. AI can help structure this complexity by organizing information about actors, issues, interests, relationships, and possible points of misunderstanding. Used carefully, such support can make conflict analysis more transparent and systematic.
A third contribution concerns perspective-taking. In many conflicts, the formal parties at the table do not represent everyone affected by the outcome. Some voices are excluded because of power asymmetries, institutional barriers, geographic distance, fear, or lack of resources. AI cannot solve these political and ethical problems by itself, but it can help make overlooked stakeholders, interests, and consequences more visible. This may improve the quality of preparation, process design, and decision-making.
AI may also support the generation of options. Negotiations often become trapped in narrow comparisons between fixed positions. By drawing on large bodies of knowledge, analogous cases, and structured problem-solving techniques, AI systems can help generate alternative solutions, identify possible trade-offs, and suggest integrative options. The value of this function is not that the system produces the “right” answer, but that it expands the range of possibilities that humans can evaluate.
Finally, AI can support process navigation. Many people facing conflict do not know whether they need mediation, legal advice, facilitation, counseling, an internal complaint process, restorative dialogue, or another form of support. A well-designed public-interest system could help users understand the nature of their conflict, consider the risks of different pathways, and connect them with qualified professionals or institutions. This may be especially valuable for individuals and communities that currently lack access to expert conflict management advice.
Why infrastructure matters
Thinking in terms of infrastructure changes the normative and institutional questions we ask. If AI for conflict management is treated merely as a tool, then the main questions are technical: Does it work? Is it accurate? Is it efficient? Can it scale? These questions matter, but they are not sufficient. Once we think of AI as infrastructure, additional questions become unavoidable: Who governs it? Who has access to it? Who can audit it? Whose interests does it serve? How is sensitive data protected? What forms of misuse are prohibited? How are errors corrected? How is neutrality maintained?
These questions determine whether such systems can be trusted. Trust is indispensable in conflict management because parties are often suspicious of each other and of the institutions around them. A system that is perceived as biased, opaque, politically captured, commercially exploitative, or vulnerable to misuse will not support constructive conflict resolution. It may even intensify mistrust.
Public-interest infrastructure therefore requires clear governance principles. Access should be broad and non-discriminatory wherever the system is offered. Human decision-making authority must remain intact, so that AI informs, structures, and supports, but does not decide. The system must be transparent enough to be audited and challenged. Data protection must be especially robust because conflict-related information may expose individuals and communities to serious risks. Referral mechanisms must be based on clear criteria, regular vetting, and conflict-of-interest safeguards.
Equally important are red lines. AI-based conflict management infrastructure should not be used for surveillance, repression, coercive manipulation, military targeting, or political control. These risks are not hypothetical. Technologies designed to analyze social tensions can also be used to monitor dissent, identify vulnerable groups, manipulate narratives, or strengthen coercive power. For this reason, the institutional design of such infrastructure is the core condition of legitimacy.
Different users, shared benefits
The potential users of AI-based conflict management infrastructure are diverse. Individuals might use it to understand a workplace dispute, family conflict, neighborhood disagreement, or institutional problem before the situation escalates. Organizations might use it to identify recurring sources of tension, prepare for mediation, or design fairer internal processes. Communities might use it to map interests and concerns in local disputes. Educators might use it to strengthen negotiation and conflict literacy. Public institutions might use it to improve access to appropriate dispute resolution pathways.
In each of these cases, the immediate benefit accrues to a specific user or group of users. The broader benefit, however, is social. When individuals can access better conflict guidance earlier, disputes may be resolved before they become destructive. When organizations understand internal tensions more clearly, they may prevent costly breakdowns. When communities are better able to surface excluded voices, decisions may gain legitimacy. When public institutions help people navigate conflict more effectively, trust in those institutions may improve.
This is why the public-good logic is so important. The aim is not simply to give one party a better instrument for winning a dispute. The aim is to strengthen society’s general capacity to manage conflict constructively. In this sense, AI-based conflict management infrastructure should be evaluated not only by its technical performance, but also by its contribution to access, fairness, de-escalation, learning, and institutional trust.
A realistic but demanding agenda
It would be naive to assume that AI can solve the political, social, and moral challenges of conflict. Peace depends on interests, institutions, leadership, legitimacy, empathy, courage, and often difficult compromises. No algorithm can substitute for these conditions. At the same time, it would also be a mistake to ignore the potential of AI to improve the informational and analytical environment in which conflict-related decisions are made.
AI is already used in conflict management and its role is likely to grow. The more important question is whether this development will be shaped mainly by private markets, state interests, and isolated experiments, or whether we will build systems that serve a broader public purpose.
Our argument is that AI-based conflict management should be developed as public-interest infrastructure with public-good characteristics. It should be accessible, trustworthy, neutral, transparent, and governed in ways that prevent misuse. It should support human judgment rather than replace it. It should help people understand conflicts more clearly, recognize the costs of escalation, identify constructive options, and reach appropriate support earlier.
If designed in this way, AI will not create peace on its own, but it may help create better conditions for peace by expanding access to conflict management capacity. In a world where destructive conflicts remain persistent and costly, that is a goal worth taking seriously.








