@inproceedings{niu-etal-2025-bias,
title = "Bias and Reliability in {AI} Safety Assessment: Multi-Facet Rasch Analysis of Human Moderators",
author = "Niu, Chunling and
Bradley, Kelly and
Ma, Biao and
Waltman, Brian and
Cossette, Loren and
Jin, Rui",
editor = "Wilson, Joshua and
Ormerod, Christopher and
Beiting Parrish, Magdalen",
booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
month = oct,
year = "2025",
address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
publisher = "National Council on Measurement in Education (NCME)",
url = "https://aclanthology.org/2025.aimecon-main.42/",
pages = "393--397",
ISBN = "979-8-218-84228-4",
abstract = "Using Multi-Facet Rasch Modeling on 36,400 safety ratings of AI-generated conversations, we reveal significant racial disparities (Asian 39.1{\%}, White 28.7{\%} detection rates) and content-specific bias patterns. Simulations show that diverse teams of 8-10 members achieve 70{\%}+ reliability versus 62{\%} for smaller homogeneous teams, providing evidence-based guidelines for AI-generated content moderation."
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<abstract>Using Multi-Facet Rasch Modeling on 36,400 safety ratings of AI-generated conversations, we reveal significant racial disparities (Asian 39.1%, White 28.7% detection rates) and content-specific bias patterns. Simulations show that diverse teams of 8-10 members achieve 70%+ reliability versus 62% for smaller homogeneous teams, providing evidence-based guidelines for AI-generated content moderation.</abstract>
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%0 Conference Proceedings
%T Bias and Reliability in AI Safety Assessment: Multi-Facet Rasch Analysis of Human Moderators
%A Niu, Chunling
%A Bradley, Kelly
%A Ma, Biao
%A Waltman, Brian
%A Cossette, Loren
%A Jin, Rui
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84228-4
%F niu-etal-2025-bias
%X Using Multi-Facet Rasch Modeling on 36,400 safety ratings of AI-generated conversations, we reveal significant racial disparities (Asian 39.1%, White 28.7% detection rates) and content-specific bias patterns. Simulations show that diverse teams of 8-10 members achieve 70%+ reliability versus 62% for smaller homogeneous teams, providing evidence-based guidelines for AI-generated content moderation.
%U https://aclanthology.org/2025.aimecon-main.42/
%P 393-397
Markdown (Informal)
[Bias and Reliability in AI Safety Assessment: Multi-Facet Rasch Analysis of Human Moderators](https://aclanthology.org/2025.aimecon-main.42/) (Niu et al., AIME-Con 2025)
ACL
- Chunling Niu, Kelly Bradley, Biao Ma, Brian Waltman, Loren Cossette, and Rui Jin. 2025. Bias and Reliability in AI Safety Assessment: Multi-Facet Rasch Analysis of Human Moderators. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 393–397, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).