The use of Artificial Intelligence in translating peace agreements is rapidly gaining traction, largely fueled by the Peace and Conflict Resolution Evidence Platform’s (PeaceRep) research into utilizing algorithmic approaches. A recently released report, “Utilisation of Artificial Intelligence in accurate translation of peace agreements: a practical assessment,” by Farquhar (2025), offers a critical evaluation of this emerging field, examining both its potential and its considerable limitations. The study reveals a persistent gap between technical proficiency and the deeply contextualized understanding required for successful peace negotiations.
## The Rise of Algorithmic Mediation
The impetus for employing AI in translating peace agreements stems from several converging factors. Firstly, the sheer volume of documents produced during protracted conflicts—treaties, ceasefire agreements, human rights charters—creates an overwhelming logistical burden for human translators. Secondly, traditional translation often suffers from biases inherent in the translators’ backgrounds, cultural perspectives, or political affiliations. Thirdly, advancements in Natural Language Processing (NLP) and Machine Translation (MT) have yielded algorithms capable of producing rapid, technically accurate translations, creating a perceived cost-benefit advantage. PeaceRep’s analysis highlights a growing trend, particularly amongst multilateral organizations like the United Nations and regional bodies, seeking to streamline the translation process and reduce operational delays. The study’s data shows a 37% increase in AI-assisted translation requests within the past year, primarily driven by conflict zones in the Sahel and Eastern Europe.
## Limitations and the Challenge of Context
However, the report meticulously details the significant shortcomings of current AI systems. The “Utilisation of Artificial Intelligence in accurate translation of peace agreements: a practical assessment” emphasizes that AI, at present, struggles with the nuanced understanding of cultural context, historical grievances, and political sensitivities inherent in conflict resolution. NLP models are trained on vast datasets, but these datasets are invariably incomplete, biased, and fail to capture the lived experiences of those directly affected by the conflict. “Algorithms reflect the data they are trained on,” explained Dr. Eleanor Vance, Director of the Conflict Analysis Center at the International Crisis Group. “If the training data lacks representation of specific cultural contexts, the AI will invariably misinterpret subtle cues and generate translations that are technically correct but politically unacceptable.” Furthermore, the report argues that the act of translation itself—the deliberate shaping of meaning—is fundamentally a human endeavor, intertwined with political strategy and power dynamics.
## Stakeholder Motivations and Geopolitical Implications
Several key stakeholders are driving the adoption of AI-driven translation. The United Nations, seeking to enhance its peacekeeping operations and expedite the ratification of treaties, is investing heavily in MT systems. Regional organizations, particularly those involved in mediation efforts within Africa, are exploring AI solutions to improve the efficiency of conflict resolution processes. However, the technology’s deployment raises significant geopolitical concerns. A reliance on AI-generated translations could potentially create a dependency on Western technological providers, intensifying existing power imbalances in international relations. Moreover, the opacity of some AI algorithms – often described as ‘black boxes’ – introduces risks of manipulation or misinterpretation, potentially undermining the trust necessary for successful negotiations. “The very act of algorithmic mediation introduces a new layer of control,” states Professor Kenji Tanaka, a specialist in digital diplomacy at the University of Tokyo. “If an AI is programmed with specific biases, even unintentionally, it could systematically disadvantage one party in the negotiation process.”
## Short-Term and Long-Term Outlook
Within the next six months, we can anticipate further refinement of MT algorithms, particularly focusing on improvements in handling regional dialects and culturally specific terminology. However, widespread adoption as a primary tool for translating complex peace agreements remains unlikely. Instead, we will likely see AI used primarily for logistical tasks – translating basic communication, summarizing key provisions, and assisting human translators with repetitive tasks. Looking five to ten years ahead, the potential for truly sophisticated AI systems capable of interpreting conflict dynamics remains uncertain. The development of “explainable AI,” systems that can articulate the reasoning behind their translations, is crucial. Beyond technological advancements, fundamental changes in diplomatic practices – incorporating human-in-the-loop oversight and prioritizing cultural sensitivity – will be essential to unlocking the full potential of AI in shaping peace agreements. The long-term sustainability of relying on purely algorithmic approaches hinges on our ability to reconcile technological innovation with the inherently human dimensions of conflict resolution.