@inproceedings{richburg-etal-2024-automatic,
    title = "Automatic Authorship Analysis in Human-{AI} Collaborative Writing",
    author = "Richburg, Aquia  and
      Bao, Calvin  and
      Carpuat, Marine",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.165",
    pages = "1845--1855",
    abstract = "As the quality of AI-generated text increases with the development of new Large Language Models, people use them to write in a variety of contexts. Human-AI collaborative writing poses a potential challenge for existing AI analysis techniques, which have been primarily tested either on human-written text only, or on samples independently generated by humans and AI. In this work, we investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. Results show that, for AI text detection in the cowriting setting, classifiers based on authorship embeddings (Rivera-Soto et al., 2021) outperform classifiers used in prior work distinguishing AI vs. human text generated independently. However, these embeddings are not optimal for finer-grained authorship identification tasks: for authorship verification, n-gram based models are more robust to human-AI co-written text, and authorship attribution performance degrades compared to baselines that use human-written text only. Taken together, this suggests that the rise of human-AI co-written text will require adapting AI detection tools and authorship analysis techniques in the near future. We release our code at https://github.com/AARichburg/Human-AI{\_}Authorship{\_}Analysis.",
}

As the quality of AI-generated text increases with the development of new Large Language Models, people use them to write in a variety of contexts. Human-AI collaborative writing poses a potential challenge for existing AI analysis techniques, which have been primarily tested either on human-written text only, or on samples independently generated by humans and AI. In this work, we investigate the extent to which existing AI detection and authorship analysis models can perform classification on data generated in human-AI collaborative writing sessions. Results show that, for AI text detection in the cowriting setting, classifiers based on authorship embeddings (Rivera-Soto et al., 2021) outperform classifiers used in prior work distinguishing AI vs. human text generated independently. However, these embeddings are not optimal for finer-grained authorship identification tasks: for authorship verification, n-gram based models are more robust to human-AI co-written text, and authorship attribution performance degrades compared to baselines that use human-written text only. Taken together, this suggests that the rise of human-AI co-written text will require adapting AI detection tools and authorship analysis techniques in the near future. We release our code at https://github.com/AARichburg/Human-AI{\_}Authorship{\_}Analysis.
Richburg, Aquia Bao, Calvin Carpuat, Marine
Automatic Authorship Analysis in Human-AI Collaborative Writing