Lawrence S. Bloomberg Faculty of Nursing

Navi Mental Health Wayfinder

Dr. Charlene Chu awarded Social Sciences and Humanities Research Council Insight Development grant

Bloomberg Nursing Assistant Professor Charlene Chu received a Social Sciences and Humanities Research Council (SSHRC) Insight Development grant for her project “Ageism in A.I.: Exploring the Social and Ethical Implications of Age-Based Bias in Artificially Intelligent Systems.” 

Artificial intelligence (AI) and machine learning can be found in almost every arena including healthcare, education, employment, finance, and law. This technology has become increasingly salient as a matter of public and political interest; it is poised to fundamentally change the nature of the global economy. 

However, AI systems can reflect the implicit and explicit biases of society, and the widespread application of AI has led to mainstream discourse about the proliferation of racism, sexism, and classism from AI systems. There are significant concerns about how the predictive models in AI systems perpetuate inequity, privilege, and power in society. 

Given the globally aging population and proliferation of AI technologies, there is a need to critically examine the presence of ageism in AI systems and how older adults experience ageism in and through AI’s predictive models. Yet, unlike racism and sexism, ageism has been largely absent in the AI bias literature. 

Dr. Chu and research team aim to fill this knowledge gap by developing insights about the potential for age based bias in AI as a mechanism to perpetuate social inequality for older adults. In stage 1 of the project, Chu and team will conduct an integrative scoping review to synthesize the current state of knowledge. In stage 2, the research team will build on this knowledge by conducting in-depth interviews with older adults, lead AI programmers working in the private and public technology sectors, and scholars in gerontology, ethics, and law. 

Chu’s research objectives include exploring how age-related bias may be encoded, executed, or amplified through AI systems; identifying to what extent AI systems are ageist and the societal and ethical implications of these systems; examining how older adults experience ageism in AI; and developing preliminary theoretical insights about the influence of age-related bias in AI.

The research findings will provide a deeper understanding of ageism in and through AI predictive models and identifying related challenges and opportunities, and have the potential to propel research and algorithmic development, leading to more social equality and inclusion in the lives of older adults. 

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