AI Guardrails Index:

We broke AI guardrails down to six categories.

We curated datasets and models that demonstrate the state of AI safety using LLMs and other open source models.

Introduction

Content moderation guardrails are crucial for LLM-based AI applications, addressing inherent risks and regulatory requirements. They prevent the propagation of toxic language from user inputs and mitigate LLMs' potential to amplify harmful content and biases. By safeguarding against inappropriate outputs, these guardrails protect brand reputation, user trust, and ensure regulatory compliance. Their implementation is essential for responsible AI deployment and maintaining system integrity across industries.

Results

Leaderboard

Metric:

Task:

Developer	Model	Latency	Outcome
Guardrails AI	Toxic Language	0.01 ms	0.72
Google	Natural Language Content Safety	0.11 ms	0.60
Microsoft	Azure Content Safety	0.06 ms	0.51

Dataset Breakdown

Developer	Samples
toxic	6090
obscene	3691
insult	3427
identity_hate	712
severe_toxic	367
threat	211

See the full dataset here: Content Moderation dataset

Conclusion

In this comprehensive evaluation of content moderation guardrails, Guardrails AI consistently outperforms Google's Moderate Text and Microsoft's Content Safety API across key metrics. With the highest Max F1 score (0.718), Guardrails AI demonstrates superior content classification. Its low FPR (0.034) and high TNR (0.966) indicate exceptional precision in identifying non-toxic content, crucial for maintaining user engagement and free speech. The outstanding AUC ROC score (0.969) further solidifies Guardrails AI's versatility across different thresholds. While Google shows moderate performance with a Max F1 of 0.596, Microsoft lags slightly with 0.512. Both competitors exhibit higher FPR, potentially leading to over-flagging of content. These results clearly position Guardrails AI as the most effective and adaptable solution for content moderation needs, ensuring both safety and user satisfaction in AI-powered applications.