  ChatGPT has enabled access to AI-generated writing for the masses, and within
just a few months, this product has disrupted the knowledge economy, initiating
a culture shift in the way people work, learn, and write. The need to
discriminate human writing from AI is now both critical and urgent,
particularly in domains like higher education and academic writing, where AI
had not been a significant threat or contributor to authorship. Addressing this
need, we developed a method for discriminating text generated by ChatGPT from
(human) academic scientists, relying on prevalent and accessible supervised
classification methods. We focused on how a particular group of humans,
academic scientists, write differently than ChatGPT, and this targeted approach
led to the discovery of new features for discriminating (these) humans from AI;
as examples, scientists write long paragraphs and have a penchant for equivocal
language, frequently using words like but, however, and although. With a set of
20 features, including the aforementioned ones and others, we built a model
that assigned the author, as human or AI, at well over 99% accuracy, resulting
in 20 times fewer misclassified documents compared to the field-leading
approach. This strategy for discriminating a particular set of humans writing
from AI could be further adapted and developed by others with basic skills in
supervised classification, enabling access to many highly accurate and targeted
models for detecting AI usage in academic writing and beyond.
