How machine learning models can amplify inequities in medical diagnosis and treatment

In a new paper, Ghassemi, now an assistant professor in MIT's Department of Electrical Science and Engineering , and three collaborators based at the Computer Science and Artificial Intelligence Laboratory, have probed the roots of the disparities that can arise in machine learning, often causing models that perform well overall to falter when it comes to subgroups for which relatively few data have been collected and utilized in the training process.

The main point of their inquiry is to determine the kinds of subpopulation shifts that can occur and to uncover the mechanisms behind them so that, ultimately, more equitable models can be developed. Given the data available to it, the machine could reach an erroneous conclusion—namely that cows can only be found on grass, not on sand, with the opposite being true for camels. Such a finding would be incorrect, however, giving rise to a spurious correlation, which, Yang explains, is a"special case" among subpopulation shifts—"one in which you have a bias in both the class and the attribute.

A relatively straightforward case would involve just two attributes: the people getting X-rayed are either female or male. If, in this particular dataset, there were 100 males diagnosed with pneumonia for every one female diagnosed with pneumonia, that could lead to an attribute imbalance, and the model would likely do a better job of correctly detecting pneumonia for a man than for a woman.

Improvements to the"encoder," one of the uppermost layers in the neural network, could reduce the problem of attribute imbalance."However, no matter what we did to the encoder or classifier, we did not see any improvements in terms of attribute generalization," Yang says,"and we don't yet know how to address that."There is also the question of assessing how well your model actually works in terms of evenhandedness among different population groups.

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