Examining the technology-mediated cycles of injustice that contribute to digital ageism

Chu, Charlene H.; Nyrup, Rune; Donato-Woodger, Simon; Leslie, Kathleen; Khan, Shehroz S.; Bernett, Corinne; Grenier, Amanda

doi:10.1145/3529190.3534765

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…Data may also aggregate older adults into arbitrary age blocks, replicating problematic assumptions that link functional decline with age and failing to represent the diversity of the older adult population [10]. The marketing strategies for these ML systems often highlight their use in health care, reinforcing the idea of aging as a period of physical and mental decline [21]. As ML models and technologies become inextricably part of accessing opportunities and services, older adults' risk of being left behind by a growing digital divide increases [22].…”

Section: Introductionmentioning

confidence: 99%

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review

Chu,

Donato-Woodger,

Khan

et al. 2024

JMIR Aging

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Background Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. Despite the recognition of the importance of this problem, there is a lack of research that specifically examines the strategies used to mitigate age-related bias in ML models and the effectiveness of these strategies. Objective To address this gap, we conducted a scoping review of mitigation strategies to reduce age-related bias in ML. Methods We followed a scoping review methodology framework developed by Arksey and O’Malley. The search was developed in conjunction with an information specialist and conducted in 6 electronic databases (IEEE Xplore, Scopus, Web of Science, CINAHL, EMBASE, and the ACM digital library), as well as 2 additional gray literature databases (OpenGrey and Grey Literature Report). Results We identified 8 publications that attempted to mitigate age-related bias in ML approaches. Age-related bias was introduced primarily due to a lack of representation of older adults in the data. Efforts to mitigate bias were categorized into one of three approaches: (1) creating a more balanced data set, (2) augmenting and supplementing their data, and (3) modifying the algorithm directly to achieve a more balanced result. Conclusions Identifying and mitigating related biases in ML models is critical to fostering fairness, equity, inclusion, and social benefits. Our analysis underscores the ongoing need for rigorous research and the development of effective mitigation approaches to address digital ageism, ensuring that ML systems are used in a way that upholds the interests of all individuals. Trial Registration Open Science Framework AMG5P; https://osf.io/amg5p

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Section: Introductionmentioning

confidence: 99%

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review

Chu,

Donato-Woodger,

Khan

et al. 2024

JMIR Aging

Self Cite

View full text Add to dashboard Cite

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Next Steps in Ethics and Policy

Knoefel,

Wallace,

Thomas

et al. 2023

Synthesis Lectures on Technology and Health

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Age-related bias and artificial intelligence: a scoping review

Chu,

Donato-Woodger,

Khan

et al. 2023

Humanit Soc Sci Commun

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There are widespread concerns about bias and discriminatory output related to artificial intelligence (AI), which may propagate social biases and disparities. Digital ageism refers to ageism reflected design, development, and implementation of AI systems and technologies and its resultant data. Currently, the prevalence of digital ageism and the sources of AI bias are unknown. A scoping review informed by the Arksey and O’Malley methodology was undertaken to explore age-related bias in AI systems, identify how AI systems encode, produce, or reinforce age-related bias, what is known about digital ageism, and the social, ethical and legal implications of age-related bias. A comprehensive search strategy that included five electronic bases and grey literature sources including legal sources was conducted. A framework of machine learning biases spanning from data to user by Mehrabi et al. is used to present the findings (Mehrabi et al. 2021). The academic search resulted in 7595 articles that were screened according to the inclusion criteria, of which 307 were included for full-text screening, and 49 were included in this review. The grey literature search resulted in 2639 documents screened, of which 235 were included for full text screening, and 25 were found to be relevant to the research questions pertaining to age and AI. As a result, a total of 74 documents were included in this review. The results show that the most common AI applications that intersected with age were age recognition and facial recognition systems. The most frequent machine learning algorithms used were convolutional neural networks and support vector machines. Bias was most frequently introduced in the early ‘data to algorithm’ phase in machine learning and the ‘algorithm to user’ phase specifically with representation bias (n = 33) and evaluation bias (n = 29), respectively (Mehrabi et al. 2021). The review concludes with a discussion of the ethical implications for the field of AI and recommendations for future research.

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Examining the technology-mediated cycles of injustice that contribute to digital ageism

Cited by 7 publications

References 16 publications

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review

Strategies to Mitigate Age-Related Bias in Machine Learning: Scoping Review

Next Steps in Ethics and Policy

Age-related bias and artificial intelligence: a scoping review

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