Textual Outlier

Textual outliers are pieces of text significantly different from the majority in a dataset, and their identification is crucial for improving the robustness and fairness of AI models. Current research focuses on leveraging outlier detection methods to identify marginalized populations in applications like toxicity detection, revealing biases and performance disparities not readily apparent through traditional demographic analysis. This work highlights the importance of considering data distribution beyond simple group comparisons, leading to more equitable and accurate AI systems. The broader impact extends to improving model generalization and mitigating unintended harm caused by biased or insufficiently trained models.