Bias Bytes: A Study Examining Disability Bias in ChatGPT Job Recommendation

Abstract

Background

The emergence and accessibility of generative artificial intelligence (AI), such as ChatGPT, has facilitated opportunities for use of AI among vocational rehabilitation (VR) counselors and employment specialists for support in job development and recommendations for people with disabilities.

Objective

This study examined potential biases of ChatGPT in its responses to prompts requesting specific job recommendations for case vignettes based on specific disability types.

Method

ChatGPT 4o mini was probed for job placement recommendations for seven disability types and a no disability control across vignette conditions that varied by gender and level of case detail.

Results

Several significant differences between disability and vignette condition regarding average income level and categorization of jobs emerged. Notably, individuals with intellectual disabilities and those with serious mental illness were suggested occupations with lower salaries. Vignettes with intellectual disability showed a higher proportion of restricted job types compared to every other disability type explored in this study. Further, salaries were lowest when individual interests were omitted from the profile, while high salaries were observed when little to no information was included in the vignette. Alternatively, low salaries were observed for individuals with intellectual disabilities across all conditions.

Conclusion

Overall, these findings suggest the need for careful and thoughtful use of AI tools among VR counselors and employment support providers.

Keywords

artificial intelligence bias disability employment job placement

Get full access to this article

View all access options for this article.

References

Ali

Schur

Blanck

(2011). What types of jobs do people with disabilities want? Journal of Occupational Rehabilitation, 21(2), 199–210. https://doi.org/10.1007/s10926-010-9266-0

Bogart

K. R.

Rosa

N. M.

Slepian

M. L.

(2018). Born that way or became that way: Stigma toward congenital versus acquired disability. Group Processes & Intergroup Relations, 22(4), 594–612. https://doi.org/10.1177/1368430218757897

Bonaccio

Connelly

C. E.

Gellatly

I. R.

Jetha

Martin Ginis

K. A.

(2020). The participation of people with disabilities in the workplace across the employment cycle: Employer concerns and research evidence. Journal of Business and Psychology, 35(2), 135–158. https://doi.org/10.1007/s10869-018-9602-5

Burke

Bezyak

Fraser

R. T.

Pete

Ditchman

Chan

(2013). Employers’ attitudes towards hiring and retaining people with disabilities: A review of the literature. The Australian Journal of Rehabilitation Counselling, 19(1), 21–38. https://doi.org/10.1017/jrc.2013.2

Centers for Disease Control and Prevention. (2023). Prevalence of disability by occupation group — United States, 2016–2020. MMWR Morbidity and Mortality Weekly Report, 72(25), 689–692. https://doi.org/10.15585/mmwr.mm7220a1

Chen

(2023). Collaboration among recruiters and artificial intelligence: Removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135–149. https://doi.org/10.1007/s10111-022-00716-0

Colorafi

Cupples

Kallman

Kennedy

(2021). Disability stories: Personal perspectives of people with disabilities on navigating the U.S. health system. Disability & Society, 38(9), 1585–1607. https://doi.org/10.1080/09687599.2021.2004879

Dalgin

Bellini

(2008). Invisible disability disclosure in an employment interview: Impact on employers’ hiring decisions and views of employability. Rehabilitation Counseling Bulletin, 52(1), 6–15. https://doi.org/10.1177/0034355207311311

Daneshjou

Smith

M. P.

Sun

M. D.

Rotemberg

Zou

(2021). Lack of transparency and potential bias in artificial intelligence data sets and algorithms: A scoping review. JAMA Dermatology, 157(11), 1362–1369. https://doi.org/10.1001/jamadermatol.2021.3129

10.

El Morr

Kundi

Mobeen

Taleghani

El-Lahib

Gorman

(2024). AI and disability: A systematic scoping review. Health Informatics Journal, 30(3), 14604582241285743. https://doi.org/10.1177/14604582241285743

11.

Erickson

Lee

von Schrader

(2025). Disability statistics from the American Community Survey (ACS). NY: Cornell University Yang-Tan Institute (YTI). Retrieved from Cornell University Disability Statistics website: www.disabilitystatistics.org

12.

Galderisi

Heinz

Kastrup

Beezhold

Sartorius

(2015). Toward a new definition of mental health. World Psychiatry, 14(2), 231. https://doi.org/10.1002/wps.20231

13.

Gasper

Palan

Muz

(2020). Survey of employer policies on the employment of people with disabilities: Final report. U.S. Department of Labor. https://www.dol.gov/sites/dolgov/files/OASP/evaluation/pdf/EmployerSurveyFinalReport.pdf

14.

Guzman, G., & Kollar, M. ((2024, September). U.S. Department of Commerce, U.S. Census Bureau, Current Population Reports, P60-282, Income in the United States: 2023. U.S. Government Publishing Office.

15.

Jajtner

K. M.

Mitra

Fountain

Nichols

(2020). Rising income inequality through a disability lens: Trends in the United States 1981-2018. Social Indicators Research, 151(1), 81–114. https://doi.org/10.1007/s11205-020-02379-8

16.

Kaplan

D. M.

Palitsky

Arconada Alvarez

S. J.

Pozzo

N. S.

Greenleaf

M. N.

Atkinson

C. A.

Lam

W. A.

(2024). What's in a name? Experimental evidence of gender bias in recommendation letters generated by ChatGPT. Journal of Medical Internet Research, 26, e51837. https://doi.org/10.2196/51837

17.

Karakaş

Özdemir

(2025). Artificial intelligence and environmental impact: Moving beyond humanizing vocabulary and anthropocentrism. OMICS: A Journal of Integrative Biology, 29(1), 2–4. https://doi.org/10.1089/omi.2024.0197

18.

Kaye

H. S.

Jans

L. H.

Jones

E. C.

(2011). Why don’t employers hire and retain workers with disabilities? Journal of Occupational Rehabilitation, 21, 526–536. https://doi.org/10.1007/s10926-011-9302-8

19.

Kinn

L. G.

Costa

Voll

Austrheim

Aas

R. W.

Davidson

(2021). Navigating between unpredictable icebergs”: A meta-ethnographic study of employment specialists’ contributions in providing job support for people with mental illness. Journal of Occupational Rehabilitation, 31(3), 512–531. https://doi.org/10.1007/s10926-020-09943-6

20.

Lee

E. E.

Torous

De Choudhury

Depp

C. A.

Graham

S. A.

Kim

H. C.

Paulus

M. P.

Krystal

J. H.

Jeste

D. V.

(2021). Artificial intelligence for mental health care: Clinical applications, barriers, facilitators, and artificial wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 6(9), 856–864. https://doi.org/10.1016/j.bpsc.2021.02.001

21.

McDaniels

(2016). Disproportionate opportunities: Fostering vocational choice for individuals with intellectual disabilities. Journal of Vocational Rehabilitation, 45(1), 19–25. https://doi.org/10.3233/JVR-160807

22.

McDonald

H. R.

Derry

Bavuso

(2025). Embracing the future: Artificial intelligence in the provision of pre-employment transition services to autistic and neurodivergent youth. Journal of Vocational Rehabilitation, 62(2), 178–190. https://doi.org/10.1177/10522263241312734

23.

McDonnall

M. C.

Cmar

J. L.

McKnight

Z. S.

(2022). Beyond employment rates: Earnings of people with visual impairments. Journal of Visual Impairment & Blindness, 116(4), 526–532. https://doi.org/10.1177/0145482 ( 221121830

24.

Morwane

R. E.

Dada

Bornman

(2021). Barriers to and facilitators of employment of persons with disabilities in low- and middle-income countries: A scoping review. African Journal of Disability, 10, 833. https://doi.org/10.4102/ajod.v10i0.833

25.

MyDisabilityJobs. (2024, January). Intellectual disability employment statistics: Update 2024. MyDisabilityJobs. https://mydisabilityjobs.com/statistics/intellectual-disability-employment/

26.

Nagtegaal

de Boer

van Berkel

Derks

Tummers

(2023). Why do employers (fail to) hire people with disabilities? A systematic review of capabilities, opportunities, and motivations. Journal of Occupational Rehabilitation, 33(2), 329–340. https://doi.org/10.1007/s10926-022-10076-1

27.

National Center on Advancing Person-Centered Practices and Systems (NCAPPS). (2021, May 10). Wisconsin Employment First Conference: Final presentation [PowerPoint slides].

28.

National Institute of Biomedical Imaging and Bioengineering. (2025). Artificial intelligence (AI). National Institutes of Health. https://www.nibib.nih.gov/science-education/science-topics/artificial-intelligence-ai

29.

Newman-Griffis

Rauchberg

J. S.

Alharbi

Hickman

Hochheiser

(2023). Definition drives design: Disability models and mechanisms of bias in AI technologies. First Monday. https://doi.org/10.5210/fm.v28i1.12903

30.

OpenAI. (2023, October 18). How ChatGPT and our foundation models are developed. OpenAI. https://help.openai.com/en/articles/7842364-how-chatgpt-and-our-foundation-models-are-developed

31.

Packin

N. G.

(2021). Disability discrimination using artificial intelligence systems and social scoring: Can we disable digital bias? Journal of International and Comparative Law, 8(2), 487–512. https://dx.https://doi.org/10.2139/ssrn.3724556

32.

Park

(2023). Bias in artificial intelligence: Basic primer. Clinical Journal of the American Society of Nephrology, 18(3), 394–396. https://doi.org/10.2215/CJN.0000000000000078

33.

Reardon

Unruh

D. K.

Rowe

D. A.

(2025). Employer perspectives toward hiring and retaining individuals with disabilities: A national survey of entry-level employers. Journal of Vocational Rehabilitation, 62(3), 310–324. https://doi.org/10.1177/10522263251326524

34.

Sánchez-Monedero

Dencik

Edwards

(2020). What does it mean to ‘solve’ the problem of discrimination in hiring? In Proceedings of the 2020 conference on fairness, accountability, and transparency. https://doi.org/10.1145/3351095.3372849

35.

Shogren

K.A.

Dean

E.E.

Linnenkamp

Raley

S.K.

Martinis

Blanck

(2021). Supported decision-making. In Khemka

Hickson

(Eds.), Decision making by individuals with intellectual and developmental disabilities. Positive psychology and disability series (pp. 21–45). Springer. https://doi.org/10.1007/978-3-030-74675-9_2 .

36.

Skerritt

(2023). Ethical implications of artificial intelligence on vocational rehabilitation. Forensic Vocational Analysis, 21(1), 35–42. https://www.memphis.edu/umid/pdfs/j.f.v.a.outcome-based-perspectives-2022.pdf

37.

Sohn

(2020, April 15). The blurred line between autism and intellectual disability. Spectrum. https://doi.org/10.53053/AUSR8688.

38.

Stanford Law School. (2018, November 1). Subminimum Employment for People with Disabilities | Stanford Law School. https://law.stanford.edu/2018/11/01/subminimum-employment-for-people-with-disabilities/#:∼:text=These%20jobs%20often%20fall%20under,flowers%2C%20friendly%2C%20and%20festive

39.

Sundar

O’Neill

Houtenville

A. J.

Phillips

K. G.

Keirns

Smith

Katz

E. E.

(2018). Striving to work and overcoming barriers: Employment strategies and successes of people with disabilities. Journal of Vocational Rehabilitation, 48(1), 93–109. https://doi.org/10.3233/JVR-170918

40.

Tilmes

(2022). Disability, fairness, and algorithmic bias in AI recruitment. Ethics and Information Technology. https://doi.org/10.1007/s10676-022-09633-2

41.

U.S. Bureau of Labor Statistics. (2024a, April 3). May 2023 state occupational employment and wage estimates – Illinois. U.S. Department of Labor. https://www.bls.gov/oes/2023/may/oes_il.htm

42.

U.S. Bureau of Labor Statistics. (2022, March 30). Persons with a disability: Barriers to employment, types of assistance, and other labor-related issues — July 2021 (USDL-22-0555). U.S. Department of Labor. https://www.bls.gov/news.release/archives/dissup_03302022.htm

43.

U.S. Bureau of Labor Statistics. (2024b). List of SOC occupations. U.S. Department of Labor. https://www.bls.gov/oes/current/oes_stru.htm

44.

U.S. Bureau of Labor Statistics. (2025, February 25). Persons with a disability: Labor force characteristics — 2024 (USDL-25-0247). U.S. Department of Labor. https://www.bls.gov/news.release/archives/disabl_02252025.htm

45.

U.S. Department of Education, National Clearinghouse for Rehabilitation Training Materials. (2026). For VR counselors [Web page]. https://ncrtm.ed.gov/vr-journey

46.

U.S. Department of Education, Office of Special Education and Rehabilitative Services, Rehabilitation Services Administration. (2024, October 18). Dear colleague letter (DCL-25-01): Artificial intelligence and its role in vocational rehabilitation services [Dear colleague letter]. https://rsa.ed.gov/sites/default/files/subregulatory/DCL-25-01-Final_0.pdf

47.

Vestling

Tufvesson

Iwarsson

(2003). Indicators for return to work after stroke and the importance of work for subjective well-being and life satisfaction. Journal of Rehabilitation Medicine, 35(3), 127–131. https://doi.org/10.1080/16501970310010475

48.

Werner

(2015). Public stigma and the perception of rights: Differences between intellectual and physical disabilities. Research in Developmental Disabilities, 38, 262–271. https://doi.org/10.1016/j.ridd.2014.12.030

49.

Westfall

(2023, November 16). New research shows ChatGPT reigns supreme in AI tool sector. Forbes. https://www.forbes.com/sites/chriswestfall/2023/11/16/new-research-shows-chatgpt-reigns-supreme-in-ai-tool-sector/

50.

World Health Organization: WHO. (2022, June 17). Mental health. https://www.who.int/news-room/fact-sheets/detail/mental-health-strengthening-our-response

51.

Wunderlich

G. S.

Rice

D. P.

Amado

N. L.

(Eds.). (2002). The dynamics of disability: Measuring and monitoring disability for Social Security programs. National Academies Press. https://www.ncbi.nlm.nih.gov/books/NBK220734/