@inproceedings{yoo-etal-2026-guidelines,
    title = "Guidelines for Whom? Rethinking {AI} Ethics in Resource-Constrained Migration Services",
    author = "Yoo, Nari  and
      Khor, Ashley  and
      Mukhija, Namrata  and
      Adebiyi, Aminat  and
      Zilka, Miri",
    editor = "Akhtar, Mubashara  and
      Batzner, Jan  and
      Choshen, Leshem  and
      Ghosh, Avijit  and
      Gohar, Usman  and
      Mickel, Jennifer  and
      Pant, Ichhya  and
      Talat, Zeerak  and
      Lin, Michelle",
    booktitle = "Proceedings of the Workshop on Evaluating Evaluations ({E}val{E}val)",
    month = jul,
    year = "2026",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.evaleval-1.3/",
    pages = "19--25",
    ISBN = "979-8-89176-429-3",
    abstract = "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?''"
}