Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

Abstract: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.

10 pages. An updated version appears in the Proceedings of the 60th Hawaii International Conference on Systems Science (HICSS-60), Honolulu, HI, January 5-8, 2027

Computers and Society (cs.CY); Computation and Language (cs.CL)

arXiv:2609.00361 [cs.CY]

(or arXiv:2609.00361v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2609.00361 arXiv-issued DOI via DataCite (pending registration)

From: Henry M. Kim [view email] [v1] Mon, 31 Aug 2026 20:54:33 UTC (4,533 KB)