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Browsing by Author "Lyn, Alexandra"

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    Age-related bias and artificial intelligence: a scoping review
    (2023) Chu, Charlene H.; Donato-Woodger, Simon; Khan, Shehroz; Nyrup, Rune; Leslie, Kathleen; Lyn, Alexandra; Shi, Tianyu; Bianchi, Andria; Rahimi, Samira Abbasgholizadeh; Grenier, Amanda
    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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    Ageism and artificial intelligence: protocol for a scoping review
    (2022) Chu, Charlene H.; Leslie, Kathleen; Shi, Jiamin; Nyrup, Rune; Bianchi, Andria; Khan, Shehroz; Rahimi, Samira Abbasgholizadeh; Lyn, Alexandra; Grenier, Amanda
    Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention has been paid to algorithmic biases toward older adults. This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications.
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    Digital ageism: challenges and opportunities in artificial intelligence for older adults
    (2022) Chu, Charlene H.; Nyrup, Rune; Leslie, Kathleen; Shi, Jiamin; Bianchi, Andria; Lyn, Alexandra; McNicholl, Molly; Khan, Shehroz; Rahimi, Samira; Grenier, Amanda
    Artificial intelligence (AI) and machine learning are changing our world through their impact on sectors including health care, education, employment, finance, and law. AI systems are developed using data that reflect the implicit and explicit biases of society, and there are significant concerns about how the predictive models in AI systems amplify inequity, privilege, and power in society. The widespread applications of AI have led to mainstream discourse about how AI systems are perpetuating racism, sexism, and classism; yet, concerns about ageism have been largely absent in the AI bias literature. Given the globally aging population and proliferation of AI, there is a need to critically examine the presence of age-related bias in AI systems. This forum article discusses ageism in AI systems and introduces a conceptual model that outlines intersecting pathways of technology development that can produce and reinforce digital ageism in AI systems. We also describe the broader ethical and legal implications and considerations for future directions in digital ageism research to advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader cycles of injustice.