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Addressing fairness in artificial intelligence for medical imaging

María Agustina Ricci Lara · Rodrigo Echeveste · Enzo Ferrante

204 citations0/0 files storedNature Communications

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI)

Abstract

A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field of medical imaging, where AI systems are beginning to be increasingly adopted, is no exception. Here we discuss the meaning of fairness in this area and comment on the potential sources of biases, as well as the strategies available to mitigate them. Finally, we analyze the current state of the field, identifying strengths and highlighting areas of vacancy, challenges and opportunities that lie ahead.

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2021A Survey on Bias and Fairness in Machine Learning 2016Inherent Trade-Offs in the Fair Determination of Risk Scores 2018AI can be sexist and racist — it’s time to make it fair 2021Ethical Machine Learning in Healthcare

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