AI ethics guidelines for humanitarian settings have grown in number and scope. Whether they produce their intended outcomes depends on which deployers are expected to follow them. These guidelines respond to documented risks: surveillance, data misuse, and discriminatory outcomes affecting refugee populations. For high-risk applications such as biometric identification and asylum adjudication, the concerns they address are genuine. Many differentiate risk tiers in principle, yet the compliance expectations they establish (staff capacity, technical infrastructure, formal evaluation) reflect the organizational contexts in which they were developed. Many nonprofits providing frontline services to refugees operate with limited administrative capacity. When compliance requirements exceed what these organizations can meet, formal AI adoption stalls, while informal adoption proceeds without oversight or recourse. Current guidelines also tend to treat non-adoption as a neutral default, without accounting for the service gaps that follow when AI-assisted language access is unavailable. Drawing on collaboration with refugee-serving practitioners, we show that this gap between governance design and organizational reality has consequences for the people these guidelines are meant to protect. Evaluating AI guidelines, we argue, requires the same realist logic that evaluation research has long applied to social programs: not "does this guideline exist?" but "for which deployers, under what conditions, and does it produce its intended protective outcomes?"

Large language models (LLMs) are increasingly embedded in development pipelines and the daily workflows of AI practitioners. However, their effectiveness depends on access to high-quality datasets that are sufficiently large, diverse, and contextually relevant. Existing datasets often fall short of these requirements, prompting the use of synthetic data (SD) generation. A critical step in this process is the creation of human seed examples, which guide the generation of SD tailored to specific tasks. We propose a participatory methodology for seed example generation, involving multidisciplinary teams in structured workshops to co-create examples aligned with Responsible AI principles. In a pilot study with a Responsible AI team, we facilitated hands-on activities to produce seed examples and evaluated the resulting data across three dimensions: diversity, sensibility, and relevance. Our findings suggest that participatory approaches can enhance the representativeness and contextual fidelity of synthetic datasets. We provide a reproducible framework to support NLP practitioners in generating high-quality seed data for LLM development and deployment