EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 23·5 min read
From Evaluation to Guardrails: What We Brought to ACM FAccT 2026
TL;DR
At ACM FAccT 2026, a tutorial highlighted the importance of evaluating guardrails for large language models (LLMs) to ensure responsible AI deployment. The team presented findings from evaluating 120 refugee and asylum scenarios across multiple languages, leading to actionable guardrail policies.
✦ Why It Matters
Evaluate your AI systems' guardrails today to ensure they are effective and contextually relevant.
Key Takeaways
- Evaluate guardrails as rigorously as LLMs to ensure safety.
- Incorporate fact-checking tools to improve guardrail reliability.
- Utilize Mozilla's any-guardrail for customizable AI policies.
- Test guardrails across multiple languages for broader applicability.
How It Works
The tutorial demonstrated that integrating tools like web search with LLMs can enhance the evaluation of guardrails. By allowing LLMs to verify factual claims, the evaluation process becomes more reliable, moving beyond static assessments.