Holistic approach to content moderation systems
Build robust content moderation with a chain of carefully designed steps.
A holistic approach to building a robust and useful natural language classification system for real-world content moderation.
The success of such a system relies on a chain of carefully designed and executed steps, including the design of content taxonomies and labeling instructions, data quality control, an active learning pipeline to capture rare events, and a variety of methods to make the model robust and to avoid overfitting.
Train a moderation system to detect sexual content, hateful content, violence, self-harm, and harassment. This approach generalizes to a wide range of different content taxonomies and can be used to create high-quality content classifiers that outperform off-the-shelf models.
Use a multi-stage process for content moderation, covering taxonomy design, labeling, data quality, active learning for rare events, and model robustness techniques. This creates high-quality classifiers that generalize well.
This approach ensures all aspects of content moderation are considered, from data collection to model training and generalization, leading to more reliable and effective systems.
- A Holistic Approach to Undesired Content Detection in the Real World ↗OpenAI · OpenAI News · Jun 19, 2024 · paraphrased
First seen on Subvolts Jun 19, 2024. Updated Sep 11, 2026. Spotted a problem? Tell us.
- A Holistic Approach to Undesired Content Detection in the Real WorldOpenAI · OpenAI News · Jun 19, 2024
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- OpenAI (OpenAI News). Subvolts collected, normalized and credited it; the link above opens the original.