UnBias-Plus: Detect, Explain, and Rewrite Bias

Ahmed Y. Radwan, Ahmed ElKady, Sindhuja Chaduvula, Mohamed Hafez, Amrit Krishnan +1 more
6/22/2026
cs.CLcs.AIcs.SE

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Abstract

Bias in natural language remains a persistent challenge in both human-written and AI-generated content, affecting domains such as journalism, education, and AI research. Most existing detection methods identify only the presence of bias, with limited support for granular detection, interpretable explanations, neutral rewriting, and openly available trained models. We present UnBias-Plus, an open-source toolkit unifying (1) segment-level multi-class bias classification, (2) biased span localization, (3) neutral text rewriting, and (4) reasoning for each decision. Available via Python, CLI, REST API, and web interfaces, UnBias-Plus supports accessible bias analysis. The toolkit, source code, models, datasets, and documentation are publicly available.

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Cite this paper

@article{radwan2026unbiasplus,
  title  = {UnBias-Plus: Detect, Explain, and Rewrite Bias},
  author = {Ahmed Y. Radwan and Ahmed ElKady and Sindhuja Chaduvula and Mohamed Hafez and Amrit Krishnan and Shaina Raza},
  year   = {2026},
  eprint = {2606.23412},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.23412v1}
}

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