Natural Language Processing Classifier

Natural language processing (NLP) classifiers aim to automatically categorize text based on its content, a task crucial for various applications. Current research focuses on improving the accuracy, fairness, and interpretability of these classifiers, often employing deep learning architectures like BERT and exploring techniques to mitigate biases stemming from spurious correlations in training data. Significant efforts are dedicated to developing methods for visualizing model decisions and identifying systematic errors, leading to more transparent and reliable NLP systems with reduced reliance on protected attributes. These advancements are vital for building trustworthy AI systems and ensuring equitable outcomes across diverse populations.