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Researchers Reduce Bias in aI Models while Maintaining Or Improving Accuracy
Adolph Cruickshank editou esta página 3 meses atrás
Machine-learning models can fail when they attempt to make forecasts for people who were underrepresented in the datasets they were trained on.
For example, a design that predicts the very best treatment alternative for someone with a persistent illness may be trained using a dataset that contains mainly male patients. That model might make incorrect predictions for female patients when deployed in a health center.
To enhance outcomes, engineers can try balancing the training dataset by eliminating information points until all subgroups are represented similarly. While dataset balancing is appealing, it often requires eliminating large quantity of data, hurting the design’s total efficiency.
MIT scientists established a brand-new technique that recognizes and eliminates particular points in a training dataset that contribute most to a design’s failures on minority subgroups. By eliminating far fewer datapoints than other techniques, this technique maintains the general accuracy of the model while improving its performance concerning underrepresented groups.
In addition, the method can recognize hidden sources of bias in a training dataset that does not have labels. Unlabeled data are much more widespread than labeled information for many applications.
This method could likewise be integrated with other methods to enhance the fairness of machine-learning models deployed in high-stakes circumstances. For example, it may sooner or later assist make sure underrepresented patients aren’t misdiagnosed due to a biased AI model.
“Many other algorithms that try to resolve this issue assume each datapoint matters as much as every other datapoint. In this paper, we are showing that presumption is not real. There are specific points in our dataset that are adding to this predisposition, and we can find those information points, remove them, and improve performance,” says Kimia Hamidieh, an electrical engineering and computer technology (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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