Machine-learning models often underperform for minority groups due to imbalanced training datasets, which can lead to incorrect predictions. For example, a model trained primarily on data from male patients may not accurately predict treatment for female patients. To address this, engineers sometimes balance datasets by removing data points, but this can harm overall model performance. Researchers from MIT have developed a method that selectively removes data points that most contribute to a model's poor performance on minority groups, maintaining model accuracy and improving fairness. This technique can also reveal hidden biases in datasets that lack labels, which is useful as unlabeled data is more common.
The method has shown better performance than existing approaches by reducing the number of removed samples and increasing worst-group accuracy. It offers an accessible way to enhance model fairness without altering the model's architecture, making it a potentially useful tool for practitioners. The researchers aim to further validate and improve this approach, supporting the development of fairer and more reliable models. This research is supported by the National Science Foundation and the U. S. Defense Advanced Research Projects Agency.
MIT Researchers Develop Method to Enhance Machine Learning Fairness
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