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Fair Use Week 2024: Day Four With Guest Expert Dave Hansen | Why Fair Use Supports Non-Expressive Uses | Harvard

What is non-expressive use? 

Non-expressive use refers to uses that involve copying, but don’t communicate the expressive aspects of the work to be read or otherwise enjoyed. It is a term coined, as far as I can tell, by law professor Matthew Sag in a series of papers titled “Copyright and Copyright Reliant Technology” (in which he observes that courts have been approving of such uses–for example in search engine cases–albeit without a coherent framework) and then more directly in “Orphan Works as Grist for the Data Mill” and later in an article titled “The New Legal  Landscape for Text Mining and Machine Learning.”  You can do much better than this blog post if you just read Matt’s articles.

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How AI models work is explained much more thoroughly (and much better) elsewhere, but the basic idea is that they are built by developing extraordinarily robust word vectors which can be used to represent the relationships between words. To do this well, these models need to train on a large and diverse set of texts to build out a good model of how humans communicate in a variety of contexts. In short, these copy texts for the purpose of developing a model to describe facts about the underlying works and the relationship of words within them and with each other. What’s new is that we can now do this at a level of complexity and scale almost unimaginable before. Scale and complexity don’t change the underlying principles at issue, however,  and so this kind of training seems to me clearly within the bounds of non-expressive use as approved already by the courts in the cases cited above that authors, researchers, and the tech industry have been relying on for nearly a decade.

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