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Researchers Reduce Bias in aI Models while Maintaining Or Improving Accuracy
Adrianna Ulrich edited this page 2 weeks ago
Machine-learning models can fail when they try to make predictions for people who were underrepresented in the datasets they were trained on.
For example, a design that predicts the finest treatment alternative for somebody with a persistent illness might be trained utilizing a dataset that contains mainly male clients. That model may make incorrect forecasts for female clients when released in a health center.
To improve outcomes, engineers can try balancing the training dataset by eliminating data points till all subgroups are represented equally. While dataset balancing is promising, it typically needs eliminating large quantity of data, injuring the model's total performance.
MIT researchers established a brand-new strategy that identifies and gets rid of particular points in a training dataset that contribute most to a model's failures on minority subgroups. By eliminating far fewer datapoints than other techniques, this strategy maintains the overall precision of the design while improving its efficiency regarding underrepresented groups.
In addition, the method can recognize concealed sources of bias in a training dataset that lacks labels. Unlabeled data are far more common than labeled information for lots of applications.
This method could also be integrated with other approaches to enhance the fairness of machine-learning designs released in high-stakes scenarios. For asteroidsathome.net example, it may sooner or later assist make sure underrepresented clients aren't misdiagnosed due to a prejudiced AI model.
"Many other algorithms that try to resolve this concern assume each datapoint matters as much as every other datapoint. In this paper, we are showing that presumption is not real. There specify points in our dataset that are contributing to this predisposition, and we can discover those information 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 strategy.
She the paper with co-lead authors Saachi Jain PhD '24 and fellow EECS graduate trainee Kristian Georgiev
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