@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://aclanthology.org/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?''"
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<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?”</abstract>
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%0 Conference Proceedings
%T Guidelines for Whom? Rethinking AI Ethics in Resource-Constrained Migration Services
%A Yoo, Nari
%A Khor, Ashley
%A Mukhija, Namrata
%A Adebiyi, Aminat
%A Zilka, Miri
%Y Akhtar, Mubashara
%Y Batzner, Jan
%Y Choshen, Leshem
%Y Ghosh, Avijit
%Y Gohar, Usman
%Y Mickel, Jennifer
%Y Pant, Ichhya
%Y Talat, Zeerak
%Y Lin, Michelle
%S Proceedings of the Workshop on Evaluating Evaluations (EvalEval)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA
%@ 979-8-89176-429-3
%F yoo-etal-2026-guidelines
%X 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?”
%U https://aclanthology.org/2026.evaleval-1.3/
%P 19-25
Markdown (Informal)
[Guidelines for Whom? Rethinking AI Ethics in Resource-Constrained Migration Services](https://aclanthology.org/2026.evaleval-1.3/) (Yoo et al., EvalEval 2026)
ACL