Machine-learning designs can fail when they attempt to make forecasts for people who were underrepresented in the datasets they were trained on.
For example, a model that anticipates the very best treatment choice for somebody with a chronic disease may be trained using a dataset that contains mainly male clients. That design might make incorrect forecasts for disgaeawiki.info female patients when deployed in a healthcare facility.
To enhance outcomes, engineers can attempt balancing the training dataset by removing data points till all subgroups are represented similarly. While dataset balancing is appealing, it typically needs removing big quantity of information, injuring the model's general efficiency.
MIT scientists developed a brand-new strategy that determines and removes particular points in a training dataset that contribute most to a design's failures on minority subgroups. By removing far fewer datapoints than other methods, this method maintains the overall precision of the design while improving its efficiency relating to underrepresented groups.
In addition, the strategy can identify surprise sources of predisposition in a training dataset that lacks labels. Unlabeled data are far more common than identified data for numerous applications.
This technique could also be integrated with other techniques to enhance the fairness of machine-learning designs released in high-stakes situations. For example, it may someday help make sure underrepresented clients aren't misdiagnosed due to a biased AI model.
"Many other algorithms that try to address this issue presume each datapoint matters as much as every other datapoint. In this paper, we are showing that presumption is not true. There specify points in our dataset that are adding to this bias, and we can discover those data points, remove them, and get much better performance," says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate trainee at MIT and co-lead author of a paper on this technique.
She wrote the paper with co-lead authors Saachi Jain PhD '24 and fellow EECS graduate trainee Kristian Georgiev
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Researchers Reduce Bias in aI Models while Maintaining Or Improving Accuracy
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