Artificial intelligence (AI) holds great promise in the fight against ageism, and future technologies powered by AI can be a first step to ensure older adults are fully involved in the processes, systems and, ultimately, services that will affect them throughout the aging journey.

Healthcare providers, governments, academia, not-for-profits and businesses can ensure that AI is not only strengthening healthcare for older adults but also empowering them to meet their individual needs.

At the same time, there are also significant risks stemming from well-intentioned but badly implemented AI systems that have increasing importance on social decision-making. Biases, behavioral manipulation and economic and employability factors are all risks stemming from the incorrect development or deployment of AI systems.

Many of the world’s prominent AI groups are heavily investing in the ethical and social consequences of their AI systems. Examples include “responsible AI” (Meta’s rhetoric), “AI for everyone” (Google’s rhetoric) and “broadly distributing AI” (OpenAI’s rhetoric). While there are a lot of considerations, it is important to at least address the potential interplay between digital literacy and regulatory frameworks, as they can affect older people’s ability to combat ageism.

The risk of ageism through AI is a real concern. Like other challenges in this space, this risk manifests from a lack of engagement of older adults in the solution, a lack of contextual understanding by AI developers and policies and regulations that are slow to catch up.

An Aging Population

Aging adults born prior to 1946 will reach 9 million by 2030. With an impending increase in the number of elderly individuals, healthcare systems must evolve to address the requirements of the aging population or they could face overburdening.

Furthermore, a prolonged lifespan does not automatically lead to a prolonged “healthspan,” a relatively new concept in aging research, which can be defined as the period of life spent in good health.

Using AI to target aging may extend the average life expectancy more substantially than preventive care could ever fathom while alleviating some of the costs our healthcare system must incur in the face of overuse of human resources and late detection of preventable chronic conditions.

Digital Literacy For Older Adults

Digital ageism prevails when age-based biases are magnified by technology. A recent report indicates human beings can produce more than 2.5 quintillion bytes of data each day. Yet, even though aging adults are using technology more often, and possibly benefiting from that use, “they continue to be the least likely age cohort to have access to a computer and the internet,” according to an article published in The Gerontologist.

These findings suggest that the lack of digital infrastructure most aging individuals face has been somewhat overshadowed by the prevailing ageist belief that older adults do not use digital (or AI) technologies and therefore need not be accounted for.

Government regulations should require that certain aspects of AI technology built for healthcare are appropriately developed for older people while addressing the absence of appropriate digital infrastructure.

Age-Inclusive Data Collection

The basis of AI systems is algorithms, which are translated into computer code that carries instructions for rapid analysis and transformation of data into conclusions, information and other similar outputs. As such, if older individuals are underrepresented in a specific dataset, that group might end up being oversampled relative to its size to ensure that AI technology provides the same level of results for that population group.

Aging individuals may also have less “algorithmic awareness” compared to younger cohorts and minimal knowledge about the proliferation and daily use of algorithms in many digital technologies. Unfortunately, less algorithmic awareness can broaden the current digital divide, as it is a competency required for the successful appreciation of digital technologies.

Government-sponsored datasets should ensure that the data collected is appropriately represented to exclude biases while reassuring society that older individuals are fully represented in the datasets employed to validate AI algorithms.

Such systems could have a twofold function: First, they could set a benchmark for how data is collected and how it should be used, and second, they could ensure that the most appropriate data is gathered on older adults so that healthcare providers and businesses can inform AI technologies adequately and less specifically to individual characteristics.

Regulatory Framework To Safeguard Ageism

Unless intergovernmental agencies, public-private partnerships and nongovernmental organizations exercise sufficient oversight of AI technologies for healthcare-related treatments, it should come as no surprise that most older adults could reject the notion that such technologies are deployed on their behalf—though they can be.

An older cohort should be able to exercise choice and provide consent for the AI technologies to be used, as well as how these technologies should be used in conjunction with (or instead of) care and treatment provided by healthcare professionals.

Parallel to this, older generations should be consulted when policymakers are formulating new regulatory frameworks to test, select and approve AI technologies for use that align with their views, hence eliminating outdated ageist policies and practices.

Ultimately, older individuals should also have the right to contest recommendations provided by AI technology for health, perhaps through mechanisms put in place by a government body (i.e., a Ministry of Health), or in lieu of that, an appropriate legal proceeding.

Robust Ethics Processes

An omnipresent ethical challenge for the use of AI within healthcare is age bias, and as automated technologies become more widely used by aging adults, ongoing research and studies will be necessary to determine how ageism affects the design and use of AI.

A robust ethics process, especially in not-for-profit organizations, universities and businesses that design AI technologies, is vital when developing and implementing AI systems for older adults. To identify the measures most likely to eradicate, avoid and mitigate similar biases, governments should exercise greater authority over AI technologies for health so that aging individuals can ensure that such technologies are designed and deployed appropriately on their behalf.

Designing processes that narrow down ethical dilemmas related to ageism can place those challenges at the forefront of design and quality assurance.

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