Generative AI-driven employment recommendations for individuals with disabilities

Abstract Persons with disabilities need to make informed choices regarding their preferred employment setting. Generative artificial intelligence (GAI) may help provide such information. This study examined the employment recommendations provided by GAI to hypothetical individuals with disabilities. As access to employment among women with disabilities is reduced compared to men, this study focused specifically on women. Three vignettes describing a woman with intellectual, mental, or physical disabilities were provided to three GAI tools, and their responses were analyzed. Five themes were identified: (1) GAI recommendations and definitions of employment settings; (2) inaccuracies and biases in GAI responses; (3) GAI personal engagement; (4) actionable job search guidance; and (5) recommendations according to disability type. We discuss GAI’s potential to perpetuate bias and its usefulness in providing employment recommendations for persons with disabilities. Further, we highlight the improvements warranted in GAI to increase its effectiveness.

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Publication Details

Journal
AI & Society
Published
2026-01-07
DOI
https://doi.org/10.1007/s00146-025-02840-7
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Generative AI-driven employment recommendations for individuals with disabilities

Amir Winer, Shirli Werner, Mimi Ajzenstadt
AI & Society
Artificial Intelligence in Healthcare and Education
article

Generative AI-driven employment recommendations for individuals with disabilities

Amir Winer, Shirli Werner, Mimi Ajzenstadt
article en

Abstract

Abstract Persons with disabilities need to make informed choices regarding their preferred employment setting. Generative artificial intelligence (GAI) may help provide such information. This study examined the employment recommendations provided by GAI to hypothetical individuals with disabilities. As access to employment among women with disabilities is reduced compared to men, this study focused specifically on women. Three vignettes describing a woman with intellectual, mental, or physical disabilities were provided to three GAI tools, and their responses were analyzed. Five themes were identified: (1) GAI recommendations and definitions of employment settings; (2) inaccuracies and biases in GAI responses; (3) GAI personal engagement; (4) actionable job search guidance; and (5) recommendations according to disability type. We discuss GAI’s potential to perpetuate bias and its usefulness in providing employment recommendations for persons with disabilities. Further, we highlight the improvements warranted in GAI to increase its effectiveness.

AI & Society
Open University of Israel (IL), Hebrew University of Jerusalem (IL)
Openalex Percentile: Top 94%
Artificial Intelligence in Healthcare and Education
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