From Evaluation to Guardrails: What We Brought to ACM FAccT 2026
Researchers at the ACM FAccT conference argued that AI guardrails require the same level of rigorous evaluation as the underlying models. They emphasized the need for context-specific and language-specific testing to ensure reliable AI deployment.
Why it matters
As AI systems become more agentic, the mechanisms that constrain them are becoming critical points of failure, necessitating a shift toward standardized safety evaluation.
At ACM FAccT, we demonstrated why AI guardrails need the same scrutiny as models. Moving from static policies to context- and language-specific evaluations, our hands-on session proved that agentic guardrails equipped with tools like web search are vital for reliable, real-world deployment.
In June, we traveled to Montreal for the ACM Conference on Fairness, Accountability, and Transparency ( ACM FAccT ), the premier venue for safe and responsible AI development; a conference where computer scientists, social scientists, policymakers, and lawyers share a room to ask not just how to build AI systems, but whether , when , and for whom . It was the right audience for our tutorial, Contextual Evaluation of LLM Guardrails Across Languages and Agentic Systems .
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