Machine-learning designs can fail when they attempt to make predictions for people who were underrepresented in the datasets they were trained on.
For instance, a model that forecasts the best treatment choice for somebody with a persistent disease may be trained utilizing a dataset that contains mainly male patients. That model may make incorrect predictions for female patients when deployed in a health center.
To enhance results, engineers can attempt balancing the training dataset by removing information points till all subgroups are represented equally. While dataset balancing is promising, it typically needs eliminating big quantity of information, injuring the model's total efficiency.
MIT scientists established a brand-new technique that recognizes and gets rid of specific points in a training dataset that contribute most to a design's failures on minority subgroups. By getting rid of far less datapoints than other methods, this method maintains the overall accuracy of the design while enhancing its efficiency relating to underrepresented groups.
In addition, the strategy can recognize concealed sources of bias in a training dataset that lacks labels. Unlabeled information are much more common than identified data for lots of applications.
This approach could also be combined with other techniques to improve the fairness of machine-learning models deployed in high-stakes situations. For example, it might someday help ensure underrepresented clients aren't misdiagnosed due to a AI design.
"Many other algorithms that attempt to resolve this problem 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 predisposition, and we can discover those information points, eliminate them, and get much better performance," says Kimia Hamidieh, an electrical engineering and computer technology (EECS) graduate trainee at MIT and co-lead author of a paper on this technique.
She composed 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
Alphonse Spivakovsky edited this page 2 weeks ago