About this Research Topic
As the field of public health faces new challenges and opportunities globally, this Research Topic aims to gather cutting-edge research and insights from scholars actively addressing the evolving landscape of AI in public health. The confluence of artificial intelligence (AI), ageism, and social bias presents a complex web of challenges in healthcare. There is a concern that AI may reflect or even magnify the social biases and prejudices present in human societies, including ageism, racism, sexism, and ableism. This could impact the accuracy and fairness of AI systems and their effects on the health and overall well-being of various groups of individuals. Ageism, combined with social biases ingrained in AI algorithms, can have profound implications for the health and well-being of older adults, shaping their healthcare experiences and outcomes. The focus will be on but not limited to the following themes: Data: This theme will focus on how age-related biases and gaps can affect the quality, representativeness, and diversity of data used to train and test AI systems for health and how this can lead to inaccurate or unfair results for older people. Design: This dimension will focus on how age-related stereotypes and assumptions can affect the design choices and objectives of AI systems for health and how this can lead to exclusion or discrimination of older people as users or beneficiaries of AI solutions. Deployment: This theme will focus on how age-related norms and expectations can affect the deployment decisions and practices of AI systems for health and how this can lead to adverse outcomes for older people as patients or clients of AI services. Discourse: This theme will focus on how age-related narratives and representations can affect the discourse and communication around AI systems for health and how this can lead to the invisibility or stigmatization of older people as stakeholders or experts in AI development.
Public health is a dynamic field shaped by emerging health threats, innovative interventions, and technological advancements. The primary goal of this special issue is to advance our understanding of the complex relationship between AI and ageism within the context of public health. This research topic will analyze how AI can create or exacerbate intersectional health disparities for marginalized or vulnerable groups of people. The topic will analyze how AI systems can exacerbate or introduce new forms of age discrimination and exclusion for older adults. For example, an AI system that is trained with data from mostly young adults may fail to recognize cognitive decline affecting older adults. By bringing together interdisciplinary perspectives, we seek to raise awareness about AI's ethical and societal implications in healthcare and foster dialogue among researchers, practitioners, policymakers, and advocates to address ageism in AI-driven healthcare collaboratively. This special issue will identify solutions to the challenges posed by AI-driven ageism in healthcare, identify gaps and limitations in the current knowledge, and suggest directions for future research and policy interventions.
This special issue will cover a broad range of topics related to AI, focusing on how AI systems can exacerbate or introduce new forms of age discrimination and exclusion for older people. The theme will provide a conceptual framework for studying ageism and AI and cover different forms of AI ageism, including but not limited to:
1. Age biases in algorithms and datasets
2. Invisibility of old age in discourses on AI
3. Discriminatory effects of the use of AI technology on different age groups
4. Exclusion as users of AI technology, services, and products
We are interested in original research, brief research reports, conceptual analysis, data reports, policy and practice reviews, and systematic reviews.
Public health is a dynamic field shaped by emerging health threats, innovative interventions, and technological advancements. The primary goal of this special issue is to advance our understanding of the complex relationship between AI and ageism within the context of public health. This research topic will analyze how AI can create or exacerbate intersectional health disparities for marginalized or vulnerable groups of people. The topic will analyze how AI systems can exacerbate or introduce new forms of age discrimination and exclusion for older adults. For example, an AI system that is trained with data from mostly young adults may fail to recognize cognitive decline affecting older adults. By bringing together interdisciplinary perspectives, we seek to raise awareness about AI's ethical and societal implications in healthcare and foster dialogue among researchers, practitioners, policymakers, and advocates to address ageism in AI-driven healthcare collaboratively. This special issue will identify solutions to the challenges posed by AI-driven ageism in healthcare, identify gaps and limitations in the current knowledge, and suggest directions for future research and policy interventions.
This special issue will cover a broad range of topics related to AI, focusing on how AI systems can exacerbate or introduce new forms of age discrimination and exclusion for older people. The theme will provide a conceptual framework for studying ageism and AI and cover different forms of AI ageism, including but not limited to:
1. Age biases in algorithms and datasets
2. Invisibility of old age in discourses on AI
3. Discriminatory effects of the use of AI technology on different age groups
4. Exclusion as users of AI technology, services, and products
We are interested in original research, brief research reports, conceptual analysis, data reports, policy and practice reviews, and systematic reviews.
Keywords: AI, Ageism, Social Bias, Health, Algorithm, Data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
