RECAST: Interactive Auditing of Automatic Toxicity Detection Models
Georgia Institute of Technology（佐治亚理工学院）；Mailchimp
英文摘要：
As toxic language becomes nearly pervasive online, there has been increasing interest in leveraging the advancements in natural language processing (NLP), from very large transformer models to automatically detecting and removing toxic comments. Despite the fairness concerns, lack of adversarial robustness, and limited prediction explainability for deep learning systems, there is currently little work for auditing these systems and understanding how they work for both developers and users. We present our ongoing work, RECAST, an interactive tool for examining toxicity detection models by visualizing explanations for predictions and providing alternative wordings for detected toxic speech.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
