Based Debiasing

Based debiasing aims to mitigate the negative impact of biases present in training data on machine learning models, improving their generalization and robustness. Current research focuses on developing methods to identify and correct for various biases, including positional biases in sequential data like dialogue and spurious correlations in image classification and natural language understanding tasks, employing techniques such as perturbation-based causal discovery, in-context learning, ensemble methods (like product-of-experts), and attention mechanism adjustments. These efforts are crucial for building more reliable and fair AI systems, addressing concerns about algorithmic bias in diverse applications ranging from question answering to medical diagnosis.