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    Sarah Marrs, Shannon Arnette, Courtney O’Hara
    Annie Rhodes, Jennifer Yanez Pryor
    Alexander Seifert

    Age bias in medical AI is often treated as a performance problem, a perspective that fails to recognize an already existing risk: Generative systems may produce narrow, default representations of later life that enter health communication, patient personas, training materials, …

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    Age bias in medical AI is often treated as a performance problem, a perspective that fails to recognize an already existing risk: Generative systems may produce narrow, default representations of later life that enter health communication, patient personas, training materials, and care documentation. Because these representations potentially determine the needs, risks, and forms of support that take priority before clinical decisions are made, representational ageism should be addressed in bias audits, design documentation, procurement, and participatory review.

    Jodi M. Winship, Kimberly Ivey
    Jenny Inker, Andrea Price, Deborah Hoskin, Tracey Gendron
    Kris Geerken, Emily Peron, Patricia Slattum, Sarah Marrs
    Faika Zanjani, Kennedy O'Donnell, Cate Newbanks-Hawks
    Aisling Clardy, Catherine MacDonald, Reed Bohn, Ashley Staton, Brian Battle
    Catherine MacDonald, Edmund Boxley, Emily Foreman, Madeline McIntyre
    Gigi Amateau, Cate Newbanks-Hawks, Catherine MacDonald