Examining risks of racial biases in NLP tools for child protective services
Open accessAbstract
Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be reflective of real-world applications. At the same time, practitioners are increasingly using algorithmic tools in high-stakes settings, with particular recent interest in NLP. In this work, we focus on one such setting: child protective services (CPS). CPS workers often write copious free-form text notes about families they are working with, and CPS agencies are actively seeking to deploy NLP models to leverage these data. Given well-established racial bias in this setting, we investigate possible ways deployed NLP is liable to increase racial disparities. We specifically examine word statistics within notes and algorithmic fairness in risk prediction, coreference resolution, and named entity recognition (NER). We document consistent algorithmic unfairness in NER models, possible algorithmic unfairness in coreference resolution models, and little evidence of exacerbated racial bias in risk prediction. While there is existing pronounced criticism of risk prediction, our results expose previously undocumented risks of racial bias in realistic information extraction systems, highlighting potential concerns in deploying them, even though they may appear more benign. Our work serves as a rare realistic examination of NLP algorithmic fairness in a potential deployed setting and a timely investigation of a specific risk associated with deploying NLP in CPS settings.
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Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups, including people with disabilities.
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [36]
The Abstract says NLP models can absorb and amplify data biases, including biases related to race and disability. But the Discussion does not establish that generative AI outputs tend to reproduce normative views or societal biases; it calls for evaluating generative models for racial bias if they are deployed. The source therefore supports the broader bias concern, not the claim specifically about generative outputs. (LLM, conf=0.85)
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- Authors
- Field, A., Coston, A., Gandhi, N., Chouldechova, A., Putnam-Hornstein, E., Steier, D., & Tsvetkov, Y.
- Affiliations
- Johns Hopkins University (US)
- Carnegie Mellon University (US)
- School of Social Work, University of North Carolina at Chapel Hill, USA
- University of Washington (US)
- ORCID
- Venue
- 2023 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 1479-1492
- OpenAlex
- W4379087118
- Cited by
- 18
- References
- 58
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 1
- FWCI
- 4.51
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Allegheny County Department of Human Services
- NSF (National Science Foundation) (IIS2142739, DGE1745016)
- Alfred P. Sloan Foundation
- Block Center for Technology and Society at Carnegie Mellon University
- Google (PhD Fellowship)
- Meta (PhD Fellowship)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- racial bias, algorithmic fairness, child protection services, natural language processing, named entity recognition, coreference resolution, risk prediction, racial disparities, information extraction
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