This paper documents a collaborative research process involving peacebuilders and data scientists in Kenya and Sudan to develop AI-based text classifiers for monitoring online polarization and hatespeech. The method describes a participatory annotation process in which practitioners and domain experts contributed to problem definition, annotation design, iterative validation, and model evaluation. Fine-tuned BERT-based classifiers were trained on collaboratively annotated datasets and evaluated against held-out test sets. In each case, the models produced enhanced contextual alignment, reduced misclassification driven by cultural nuance, and increased practitioner ownership of AI tools. The resulting models (Kenya-polarization and Sudan-hate speech) are open-source and accessible via HuggingFace. The study contributes empirical evidence that participatory AI development can simultaneously improve technical robustness, contextual validity, and normative alignment in sensitive humanitarian domains.

翻译：本文记录了肯尼亚和苏丹的和平建设者与数据科学家之间的一项协作研究过程，旨在开发基于AI的文本分类器，以监控网络极化与仇恨言论。该方法描述了一个参与性标注过程，其中实践者与领域专家共同参与问题定义、标注设计、迭代验证以及模型评估。基于微调BERT的分类器在协作标注的数据集上进行训练，并在保留测试集上进行评估。在每种情况下，模型均表现出增强的上下文对齐能力，减少了因文化细微差异导致的错误分类，并提升了实践者对AI工具的所有权。所得到的模型（肯尼亚-极化模型与苏丹-仇恨言论模型）均为开源，可通过HuggingFace获取。本研究提供了经验性证据，表明参与式AI开发能够在敏感的人道主义领域中同时提升技术稳健性、上下文有效性与规范性对齐。