The Challenge of Ageism in AI

June 29, 2023

Artificial intelligence (AI) is a collection of computational methods for studying human knowledge, learning, and behavior, including building agents able to know, learn, and behave (1). The advancements in AI technology are a driver of concern among ethical disciplines ranging from human rights to privacy, personal identity, and many other fields. At the same time,  Ageism refers to stereotypes, prejudice, and discrimination directed toward others or ourselves on the basis of age (2).

AI touches all life areas, increasingly influencing and shaping many aspects of our daily lives. As AI is related to older adults, it is revolutionizing many fields as public health and medicine for older people. From a longevity perspective, AI can help predict health risks and events, enable drug development, support the personalization of care management, and much more. More specifically, there are many potential uses of AI in the field of longevity, including but not limited to the following list:

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Source: Fintech for Longevity Academy

  1. Predictive modeling: AI can be used to analyze large amounts of data to identify patterns and predict the likelihood of health behaviors, and genetics or predict an individual's likelihood of developing certain age-related diseases.
  2. Drug development: AI can be utilized for analyze data on potential drug effectiveness and side effects, helping researchers identify promising candidates for further testing.
  3. Personalized medicine: AI can analyze data on an individual's genetic makeup and medical history to suggest personalized treatment plans.
  4. Assisting with healthy aging: AI can be applied for creating personalized recommendations for healthy aging based on an individual's specific needs and goals.
  5. Monitoring and detecting health problems: AI can be used to analyze data from wearable devices or other monitoring systems to detect changes in an individual's health and alert healthcare providers to potential problems.
  6. Improving diagnosis and treatment: AI can be deployed for analyzing medical images and assisting with diagnosis, or to identify patterns in patient data that may suggest new treatment approaches.
  7. Managing chronic conditions: AI can be used to help individuals with chronic conditions manage their health and prevent complications. For example, an AI-powered app could provide personalized guidance on diet, exercise, and medication management.
  8. Assisting with research: AI can be used to analyze data and identify patterns that could lead to new insights and approaches to understanding and combating age-related diseases and other health problems.
Despite the enormous advantages of using AI as a life-saving mechanism, it is important to consider potential age biases in AI to ensure that the technology is fair and does not discriminate against certain groups of people.

To do this, it is necessary to carefully evaluate the data used to train AI systems, the algorithms used by the AI system, and the design and implementation of the AI system itself to ensure that they do not perpetuate or amplify existing biases. It is also important to consider the potential impacts of AI on different age groups and to involve individuals from diverse age groups in the design and development of AI systems.

Age biases in AI refer to the potential for AI systems to discriminate against or favor individuals based on their age.
This can occur in a number of ways, such as through the data that is used to train the AI system, the algorithms that the AI system uses, or the design and implementation of the AI system itself.

For example, if an AI system is trained on data that is predominantly from younger individuals, it may be less effective at recognizing and responding to the needs and preferences of older individuals. Similarly, if an AI system is designed to optimize for certain goals or metrics that disproportionately benefit younger individuals, it may disadvantage older individuals.

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Rational or Relational?

Prof. Virginia DIGNUM from the Computing Science Department at Umea University in Sweden distinguishes between rational and relational AI. In a book on ‘Responsible AI in Africa’, Dignum claims that “AI is traditionally associated with rational decision making, understanding and shaping the societal impact of AI in all its facets requires a relational perspective”.

A rational approach to AI, where computational algorithms drive decision-making independent of human intervention, insights, and emotions, has been shown to result in bias and exclusion, laying bare societal vulnerabilities and insecurities. At the same time, a relational approach emphasizes the relational attributes and hence deals with the ethical, legal, societal, cultural, and environmental implications of AI.

From a rational perspective, the relationship between AI and ageism is something that should be tested with high prudence. AI ageism is a term that was recently denoted by Dr. Justyna Stypinska from the Freie Universität in Berlin. Stypinska defines AI Ageism as “a presented to make a theoretical contribution to how the understanding of inclusion and exclusion within the field of AI can be expanded to include the category of age”.

Stypisla further claims that the relationship between AI and ageism can be manifested in five interconnected forms: technological level, individual level, discourse level, group level, and user level.

The technological level refers to age biases in algorithms and datasets, the individual level deals with age stereotypes, prejudices, and ideologies of actors in AI, while the discourse level derives from the existing invisibility of old age in discourses. Lastly, the group form of AI ageism protests against the discriminatory effects of the use of AI technology on different age groups, and the user form of AI excluded older users of AI technology, services, and products.

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Minimizing the risks and maximizing opportunities

AI technologies can improve older people’s health and well-being, but only if ageism is eliminated from their design, implementation, and usage. At the begging of 2022, organizations like the WHO started to allocate resources to analyze AI Ageism. In a policy brief published in February 2022, The WHO focused on the risks and opportunities of artificial intelligence for health. The report examined the use of AI in medicine and public health for older people, including the conditions in which AI can exacerbate or introduce new forms of ageism. The policy brief presented by the WHO includes the technical measures that can be used to maximize its benefits for older people.  A few of these recommendations are described below:

• Participatory design of AI technologies by and with older people

• Age-diverse data science teams

• Age-inclusive data collection

• Investments in digital infrastructure and digital literacy for older people and their healthcare providers and caregivers

• Rights of older people to consent and contest:

• Governance frameworks and regulations to empower and work with older people:  

• Increased research

• Robust ethics processes

To summarize, despite the huge advantages of using AI in the fields of aging and longevity, there are many risks associated with using AI when it comes to older adults, often resulting in Agesign and other ways of breaching inclusive approaches. As populations around are aging at a rapid pace around the world and the use of AI are expanding in an even more rapid way, it is crucial to consider the risk of aging in every use of AI for aging and longevity.

Articles quoted in this blog:

  1. Dubber, Markus D., Frank Pasquale, and Sunit Das (eds), The Oxford Handbook of Ethics of AI (Oxford Academic, 9 July 2020),
  2. Global report on ageism. Geneva: World Health Organization; 2021 (
  3. WHO. (2022) Ageism in AI for health: WHO Policy Brief. Geneva.

 4. Dignum, V. (2022). Relational Artificial Intelligence. arXiv preprint arXiv:2202.07446.

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