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title
Data Augmentation vs. Other Debiasing Techniques
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When it comes to reducing bias in AI systems, there’s no one-size-fits-all solution. Data augmentation - creating synthetic data to balance training datasets - is a popular method, but it’s just one of several options. Other techniques, like representation-based methods, pre- and post-processing approaches, and post hoc corrections, tackle bias at different stages of the machine learning pipeline. Here’s a quick breakdown: Data Augmentation: Adds synthetic data to address imbalances but risks amplifying existing biases. Best for small or imbalanced datasets. Representation-Based Techniques: Modify model internals to separate sensitive attributes but require significant expertise and resources. Pre-Processing: Adjusts data before training, ideal when training data is accessible, but it may introduce new biases. Post-Processing: Modifies model outputs after training, offering privacy and quick fixes but doesn’t address underlying bias. Post Hoc Metho...
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