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Rethinking fairness in AI to improve current practice in oncology
Salamata Konate1, Jack Gallifant2, Charles Senteio3
1York University, Toronto, Ontario, Canada; Vector Institute, Toronto, Ontario, Canada.
None:
Fairness in artificial intelligence (AI) is often assessed with flawed metrics, particularly in oncology where patient diversity and structural inequities shape outcomes. Ground truth labels, predictions, and demographic attributes all carry biases that distort fairness evaluations. We argue for rethinking fairness frameworks to better capture equity in cancer care.
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