Interview with Anindya Das Antar: Evaluating effectiveness of moderation guardrails in aligning LLM outputs

In their paper presented at AIES 2025, "Do Your Guardrails Even Guard?" Method for Evaluating Effectiveness of Moderation Guardrails in Aligning LLM Outputs with Expert User Expectations, Anindya Das Antar Xun Huan and Nikola Banovic propose a method to evaluate and select guardrails that best align LLM outputs with domain knowledge from subject-matter experts. Here, Anindya tells us more about their method, some case studies, and plans for future developments. Could you give us some background to your work - why are guardrails such an important area for study? Ensuring that large language models (LLMs) produce desirable outputs without harmful side effects and align with user expectations, organizational goals, and existing domain knowledge is crucial for their adoption in high-stakes decision-making. However, despite training on vast amounts of data, LLMs can still produce incorrect, misleading, or otherwise unexpected and undesirable outputs.