Modeling the Role of AI Bias on Aging Anxiety: A Mediation Analysis

Abstract

The present study investigates how experienced ageism mediates the relationship between perceived ageism from GenAI and age anxiety outcomes with a secondary data analysis from the Older Adult Annotator Demographic and Attitudinal Survey (N = 1,483). Measures consist of older adult (age range: 50–90) responses to the previously validated Aging Anxiety Scale (AAS) and the unvalidated Age Experience Survey (AES). An exploratory factor analysis followed by a confirmatory factor analysis establishes latent variables from both surveys. A structural mediation model was used to assess whether Experienced Ageism (AES) mediated the relationship between attitudes towards algorithmic ageism (AES) and age anxiety outcomes (AAS). Experienced ageism mediated the relationship between attitudes toward algorithmic ageism and implicit age anxieties (ps < .05) but not explicit age anxieties. Future work should explore how perceived ageism in GenAI influences age anxiety and adoption of GenAI technology among older adults.

Keywords

Human-AI Interaction Ageism Generative AI Older Adults Bias

Get full access to this article

View all access options for this article.

References

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411

Berridge

Grigorovich

(2022). Algorithmic harms and digital ageism in the use of surveillance technologies in nursing homes. Frontiers in Sociology, 7, 957246. https://doi.org/10.3389/fsoc.2022.957246

Berkowsky

R. W.

Sharit

Czaja

S. J.

(2017). Factors predicting decisions about technology adoption among older adults. Innovation in Aging, 1(3), igy002. https://doi.org/10.1093/geroni/igy002

Browne

M. W.

Cudeck

(1993). Alternative ways of assessing model fit. In Bollen

K. A.

Long

J. S.

(Eds.), Testing structural equation models (pp. 136–162). Sage. https://doi.org/10.1177/0049124192021002005

Chan

Liu

Ostrowski

A. K.

Vaidya

Brady

Yoquinto

D’Ambrosio

Zulfikar

Maniar

Patskanick

, & MIT AgeLab. (2024). Co-designing generative AI technologies with older adults to support daily tasks. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.4f2a95fc

Chang

E.-S.

Kannoth

Levy

Wang

S.-Y.

Lee

J. E.

Levy

B. R.

(2020). Global reach of ageism on older persons’ health: A systematic review. PLOS ONE, 15(1), e0220857. https://doi.org/10.1371/journal.pone.0220857

Chu

C. H.

Donato-Woodger

Khan

S. S.

Nyrup

Leslie

Lyn

Shi

Bianchi

Rahimi

S. A.

Grenier

(2023). Age-related bias and artificial intelligence: A scoping review. Humanities and Social Sciences Communications, 10(1), 510. https://doi.org/10.1057/s41599-023-01999-y

Díaz

(2020a). Older adult annotator demographic and attitudinal survey (Version V1) [Dataset]. Harvard Dataverse. https://doi.org/10.7910/DVN/GXS7DI

Díaz

(2020b). Biases as values: Evaluating algorithms in context [PhD thesis, Northwestern University]. ProQuest Dissertations Publishing. https://www.proquest.com/dissertations-theses/biases-as-values-evaluating-algorithms-context/docview/2448844183/se-2?accountid=10003

10.

Díaz

Johnson

Lazar

Piper

A. M.

Gergle

(2018). Addressing age-related bias in sentiment analysis. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1–14). Association for Computing Machinery. https://doi.org/10.1145/3173574.3173986

11.

Gartner. (2024). Generative AI: What is it, tools, models, applications and use cases. https://www.gartner.com/en/topics/generative-ai

12.

Hinkin

T. R.

(1998). A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121. https://doi.org/10.1177/109442819800100106

13.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

14.

Iversen

T. N.

Larsen

Solem

P. E.

(2009). A conceptual analysis of ageism. Nordic Psychology, 61(3), 4–22. https://doi.org/10.1027/1901-2276.61.3.4

15.

Lasher

K. P.

Faulkender

P. J.

(1993). Measurement of aging anxiety: Development of the anxiety about aging scale. The International Journal of Aging and Human Development, 37(4), 247–259. https://doi.org/10.2190/1U69-9AU2-V6LH-9Y1L

16.

Lyons

Alba

Heywood

Fileborn

Minichiello

Barrett

Hinchliff

Malta

Dow

(2018). Experiences of ageism and the mental health of older adults. Aging & Mental Health, 22(11), 1456–1464. https://doi.org/10.1080/13607863.2017.1364347

17.

Nielsen

Woemmel

(2024). Invisible inequities: Confronting age-based discrimination in machine learning research and applications. In Workshop on Generative AI and Law, co-located with the International Conference on Machine Learning, Vienna, Austria.

18.

Rosales

Fernández-Ardèvol

(2019). Structural ageism in big data approaches. Nordicom Review, 40(S1), 51–64. https://doi.org/10.2478/nor-2019-0013

19.

What Is Ground Truth in Machine Learning. (2024, February 9). GeeksforGeeks. https://www.geeksforgeeks.org/what-is-ground-truth-in-machine-learning/