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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.21034 |
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| _version_ | 1866915988801323008 |
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| author | Cheboi, Allan Kipyator Kipkemboi Hawke, Julie Abualfatah, Hussam Sutjahjo, Andrew Cerigo, Daniel Burkhardt Olpengs, Rachael OBrien, William |
| author_facet | Cheboi, Allan Kipyator Kipkemboi Hawke, Julie Abualfatah, Hussam Sutjahjo, Andrew Cerigo, Daniel Burkhardt Olpengs, Rachael OBrien, William |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_21034 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | White Paper: Human-AI Collaboration in Conflict Analysis: Text Classifier Development with Peacebuilders Cheboi, Allan Kipyator Kipkemboi Hawke, Julie Abualfatah, Hussam Sutjahjo, Andrew Cerigo, Daniel Burkhardt Olpengs, Rachael OBrien, William Human-Computer Interaction 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. |
| title | White Paper: Human-AI Collaboration in Conflict Analysis: Text Classifier Development with Peacebuilders |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.21034 |