mlsanityData to 14 Oct 2026

Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

arXiv 2609.00361 ↗First posted 31 Aug 2026cs.CY cs.CLcited by 0

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Abstract (arXiv)

Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Existing moderation tools primarily delete or block harmful messages, disrupting communication and offering no constructive resolution. This study adopts a Design Science Research approach to create a responsible AI artifact that detects and detoxifies toxic communication. The artifact integrates fine-tuned transformer-based classifiers (DistilBERT, DistilRoBERTa) with a generative detoxification model (mT0-XL-Detox-ORPO) that rewrites toxic text into semantically equivalent, non-offensive paraphrases. Technical evaluation demonstrates high accuracy in toxicity detection and strong semantic preservation in rewritten messages, supporting conversation continuity while reinforcing respectful discourse. The paper contributes design principles for responsible AI moderation that prioritize meaning preservation and fairness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 31 Aug 2026.

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References · 14

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
  3. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  4. 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 422
  5. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 407
  6. 2023Explainability for Large Language Models: A Surveycited by 333
  7. 2021Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detectioncited by 264
  8. 2022The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspectivecited by 159
  9. 2020Directions in Abusive Language Training Data: Garbage In, Garbage Outcited by 140
  10. 2020Toxicity Detection: Does Context Really Matter?cited by 93
  11. 2021Text Detoxification using Large Pre-trained Neural Modelscited by 52
  12. 2024Content Moderation by LLM: From Accuracy to Legitimacycited by 30
2 more
  1. 2024SLM-Mod: Small Language Models Surpass LLMs at Content Moderationcited by 27
  2. 2022Style Transfer as Data Augmentation: A Case Study on Named Entity Recognitioncited by 2

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