Machine-learning designs can fail when they attempt to make predictions for individuals who were underrepresented in the datasets they were trained on.
For instance, a design that forecasts the finest treatment choice for somebody with a chronic disease might be trained utilizing a dataset that contains mainly male patients. That model might make incorrect predictions for female clients when deployed in a hospital.
To improve results, engineers can attempt balancing the training dataset by getting rid of information points up until all subgroups are represented similarly. While dataset balancing is promising, it typically requires removing big amount of information, injuring the model's general efficiency.
MIT scientists established a brand-new technique that identifies and removes particular points in a training dataset that contribute most to a design's failures on minority subgroups. By removing far less datapoints than other methods, this strategy maintains the total precision of the model while improving its performance relating to underrepresented groups.
In addition, the method can determine hidden sources of bias in a training dataset that lacks labels. Unlabeled data are far more widespread than labeled data for lots of applications.
This approach could likewise be combined with other approaches to enhance the fairness of machine-learning designs released in high-stakes circumstances. For instance, it may sooner or later assist make sure underrepresented patients aren't misdiagnosed due to a prejudiced AI model.
"Many other algorithms that try to address this concern presume each datapoint matters as much as every other datapoint. In this paper, we are showing that assumption is not real. There specify points in our dataset that are contributing to this predisposition, and we can find those data points, eliminate them, and improve performance," says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate trainee at MIT and co-lead author of a paper on this method.
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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