I’m a technology and human rights researcher. For the past few years, one of my focuses has been the question of how language shapes the ways we benefit from, or are harmed by, AI. I developed an open-source platform for language-pair analysis of LLM responses across different languages and contexts. I’ve also worked on evaluating policy-prompts guardrails and on whether giving LLM guardrails access to tools can make them more reliable and trustworthy (this work was recently accepted to NeurIPS! Yay!!).

Recently, though, I was on a panel at RightsCon on the human rights impact assessment of agentic AI. It got me thinking more about which aspects of language matter when evaluating LLM agents. I wanted to move beyond asking whether a model performs differently when I ask the same question in English versus Farsi (my native language), and look instead at the whole agentic trajectory (reasoning, planning, search, source selection, source hierarchy, artifact creation) while taking language and context into account.