Machine Learning Can be Fair and Accurate
upstart writes:
Machine Learning Can Be Fair and Accurate:
Carnegie Mellon University researchers are challenging a long-held assumption that there is a trade-off between accuracy and fairness when using machine learning to make public policy decisions.
As the use of machine learning has increased in areas such as criminal justice, hiring, health care delivery and social service interventions, concerns have grown over whether such applications introduce new or amplify existing inequities, especially among racial minorities and people with economic disadvantages. To guard against this bias, adjustments are made to the data, labels, model training, scoring systems and other aspects of the machine learning system. The underlying theoretical assumption is that these adjustments make the system less accurate.
A CMU team aims to dispel that assumption in a new study, recently published in Nature Machine Intelligence.
[...] the researchers found that models optimized for accuracy - standard practice for machine learning - could effectively predict the outcomes of interest but exhibited considerable disparities in recommendations for interventions. However, when the researchers applied adjustments to the outputs of the models that targeted improving their fairness, they discovered that disparities based on race, age or income - depending on the situation - could be removed without a loss of accuracy.
Journal Reference:
Kit T. Rodolfa, Hemank Lamba, Rayid Ghani. Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy, Nature Machine Intelligence (DOI: 10.1038/s42256-021-00396-x)
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