Fairness in AI for healthcare

Abstract

• Bias in medical data is prevalent for a wide range of reasons, for example unintended exclusion from randomised clinical trials. • Bias in artificial intelligence can often arise when it is trained on biased data. • Educational initiatives will be important for ensuring a wider understanding of AI systems in healthcare. • It is important that fairness testing and mitigation is built into the process of AI creation. Artificial intelligence (AI) is a technology that enables computers to simulate human intelligence and has the potential to improve healthcare in a multitude of ways. However, there are also possibilities that it may continue, or exacerbate, current disparities. We discuss the problem of bias in healthcare and AI, and go on to highlight some of the ongoing and future solutions that are being researched in the area.

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