AI Red Teams and Adversarial Data Labeling with Redwood Research

2026-02-19 · Source: Surge AI Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Intermediate, short

Summary

Surge AI collaborated with Redwood Research to develop a highly robust classifier for identifying violent text, aiming for 99.999% reliability. This project involved building and training a human "red team" to generate adversarial examples that could trick Redwood's existing model, which scores inputs in real-time. The red team, composed of labelers with creative writing and AI/ML backgrounds, focused on creating violent text completions that scored below a 5% detection threshold. Initial basic tricks failed, requiring the team to devise creative strategies like logical misdirection and poetic/metaphorical descriptions to bypass the model. Tens of thousands of these adversarial examples were returned to Redwood to refine their model, with future phases expected to be more challenging as the model improves. Surge AI is also evaluating the violence filter's impact on text-generation quality.

Key takeaway

For AI Engineers developing safety-critical text classifiers, integrating a human red team is essential for achieving extremely low false negative rates. You should prioritize recruiting creative labelers and defining nuanced guidelines for adversarial text generation. Expect an iterative process where the model continuously improves, requiring increasingly sophisticated red team strategies to identify new vulnerabilities and ensure robust alignment.

Key insights

Human red teaming is crucial for building highly robust AI models with extremely low false negative rates.

Principles

• Adversarial evaluation improves model robustness.
• Creative human input enhances red teaming effectiveness.

Method

A human red team generates adversarial text examples to trick a classifier, which then uses these examples for retraining. This iterative process aims to reduce false negatives and improve model alignment.

In practice

• Recruit labelers with creative writing skills.
• Define "violence" precisely for labeling tasks.
• Use logical misdirection to bypass text classifiers.

Topics

• AI Alignment
• Adversarial Evaluation
• Red Teaming
• Text Classification
• Data Labeling

Best for: AI Engineer, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Surge AI Blog.