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AI in HPE

An open-source, open-access resource on generative AI in health professions education.

Bias in generative AI

Because machine learning models are trained on human-generated data, there is an inherent bias in the outcomes that the algorithms produce.%%link back to generative AI training page%%

Human beings are responsible for generating, curating, organising, and labelling the datasets. They’re also responsible for designing the algorithms and deciding which variables to give more weight to. Then they choose which algorithms to apply to the problem. At each point in the training process, a human being is making a (sometimes biased) decision.

So inclusivity matters—from who designs it to who sits on the company boards and which ethical perspectives are included. Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with its old, familiar biases and stereotypes. - Kate Crawford (2016)

The difference between human bias and machine learning bias is that machine learning algorithms can be corrected. It’s not easy because of the black box nature of machine learning algorithms but it’s possible.

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