Researchers at Apple developed Fairness Optimized Reweighting via Meta-Learning (FORML), a Machine Learning Training Algorithm that balances Fairness and Robustness with Accuracy by jointly learning training sample Weights and Neural Network Parameters

Deep neural networks are used in machine learning applications such as image classification, audio recognition, natural language comprehension, and healthcare. Despite modern DNN architectures' strong predictive performance, models can inherit biases and fail to generalize as data distribution differs in validation and training or when the test evaluation metrics differ from those used during training. This is due to spurious correlations in the dataset and overfitting of the training metric. Importantly, this can lead to fairness breaches for specific test groups. Data reweighting is a typical data-centric paradigm in fairness and robustness for minimizing data distribution shifts and class imbalance.