Detoxifying Toxic Communication: A Design Science Approach to Responsible AI

arXiv:2609.00361 · cs.CY, cs.CL · Submitted 2026-08-31 · Read on arXiv

cs.CY, cs.CL

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/thearhamn/detox_org_com

Terminology

• Design Science Research — 12× in this paper
• Accuracy — 8× in this paper · explained in 1 episode
• Transformer — 7× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Semantic Similarity — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Explainability — 2× in this paper · explained in 3 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Style Transfer Accuracy — 1× in this paper

Sources

• Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
• Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI
• Text Detoxification using Large Pre-trained Neural Models
• ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection
• The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Toxicity Detection: Does Context Really Matter?
• Directions in Abusive Language Training Data: Garbage In, Garbage Out

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