Concern 634 · Working draft · editorial review in progress
Geriatric AI
Geriatric AI risk arises when tools overlook the varied needs of older people, including multiple health conditions, many medicines and different care goals.
Understanding the concern
Two people of the same age can have very different health and abilities. A model built mainly from younger patients may not handle these differences well. A system can also embed unfair assumptions, such as treating age alone as a reason that treatment would not be worthwhile.
The World Health Organization warns that health AI can repeat or deepen ageism when older people are excluded from its design and evidence. A useful comparison is a standard map that leaves out the paths a particular traveller needs. Better tools include diverse older adults and support discussion of what matters to each person, rather than treating age as a complete description.
An example
A treatment-support tool gives a rigid recommendation without considering the person's other medicines, daily life or own priorities.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The WHO brief explains risks and recommended safeguards. It does not establish that every tool performs poorly for older adults or that age should never inform clinical assessment.
World Health Organization 2022 · Policy brief
Ageism in artificial intelligence for health ↗ (opens in a new tab)Risks of age exclusion and stereotyping in health AI, with design and governance recommendations.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
How can older people help shape the decisions and goals that a health AI supports?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Service
Service means using abilities and resources to meet real needs. It asks whether the people meant to benefit actually receive help, rather than treating them as a means to another goal.
Speak with the people the project is meant to serve. Notice burdens it may add, such as extra checking or confusing forms. Judge success by their experience as well as the organization's targets.
Ask together: Who is this meant to serve, and do they experience it as helpful?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Listening
Listening means trying to understand someone's experience before preparing an answer. People who use a service, work with it, or are judged by it may notice problems its designers miss.
Invite people to describe what happened in their own words. Repeat back what you understood and let them correct it. A quiet person or a complaint that is hard to measure may still reveal something important.
Ask together: Whose experience have we not heard yet?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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