Multiple Authorship

Multiple authorship, encompassing both human-human and human-AI collaborations, is a burgeoning research area focusing on identifying, verifying, and even obfuscating authorship in various contexts. Current research utilizes machine learning models, including transformer-based architectures like BERT and LLMs, along with novel algorithms for stylometric analysis and graph clustering, to analyze textual features and determine authorship. These advancements have implications for combating misinformation, ensuring academic integrity, protecting author privacy, and improving the design of human-AI writing tools.