AI and Copyright: Expanding Copyright Hurts Everyone—Here’s What to Do Instead

You shouldn't need a permission slip to read a webpage–whether you do it with your own eyes, or use software to help. AI is a category of general-purpose tools with myriad beneficial uses. Requiring developers to license the materials needed to create this technology threatens the development of more innovative and inclusive AI models, as well as important uses of AI as a tool for expression and scientific research.  

Threats to Socially Valuable Research and Innovation 

Requiring researchers to license fair uses of AI training data could make socially valuable research based on machine learning (ML) and even text and data mining (TDM) prohibitively complicated and expensive, if not impossible. Researchers have relied on fair use to conduct TDM research for a decade, leading to important advancements in myriad fields. However, licensing the vast quantity of works that high-quality TDM research requires is frequently cost-prohibitive and practically infeasible.  

Fair use protects ML and TDM research for good reason. Without fair use, copyright would hinder important scientific advancements that benefit all of us. Empirical studies back this up: research using TDM methodologies are more common in countries that protect TDM research from copyright control; in countries that don’t, copyright restrictions stymie beneficial research. It’s easy to see why: it would be impossible to identify and negotiate with millions of different copyright owners to analyze, say, text from the internet. 

The stakes are high, because ML is critical to helping us interpret the world around us. It's being used by researchers to understand everything from space nebulae to the proteins in our bodies. When the task requires crunching a huge amount of data, such as the data generated by the world’s telescopes, ML helps rapidly sift through the information to identify features of potential interest to researchers. For example, scientists are using AlphaFold, a deep learning tool, to understand biological processes and develop drugs that target disease-causing malfunctions in those processes. The developers released an open-source version of AlphaFold, making it available to researchers around the world. Other developers have already iterated upon AlphaFold to build transformative new tools.  

Threats to Competition 

Requiring AI developers to get authorization from rightsholders before training models on copyrighted works would limit competition to companies that have their own trove of training data, or the means to strike a deal with such a company. This would result in all the usual harms of limited competition—higher costs, worse service, and heightened security risks—as well as reducing the variety of expression used to train such tools and the expression allowed to users seeking to express themselves with the aid of AI. As the Federal Trade Commission recently explained, if a handful of companies control AI training data, “they may be able to leverage their control to dampen or distort competition in gene