Traditional copyright policymaking also frequently excludes or overlooks the world of open licensing. But in this new space, many of the tools come from the open source community, and much of the data comes from openly-licensed sources like Wikipedia or Flickr Commons. Industry groups that claim to represent the voice of authors typically do not represent such creators, and their proposed solutions–usually, demands that payment be made to corporate publishers or to collective rights management organizations–often don’t benefit, and are inconsistent with, the thinking of the open world.
Moreover, even aside from openly licensed material, there are vast troves of technically copyrighted but not actively rights-managed content on the open web; these are also used to train AI models. Millions, if not billions, of individuals have contributed to these data sources, and because none of them are required to register their work for phone number database copyright to arise, it does not seem possible or sensible to try to identify all of the relevant copyright owners–let alone negotiate with each of them–before development can continue. of other concerns, the European Union has already codified copyright exceptions which permit the use of copyright-protected material as training data for generative AI models, subject to an opt-out in commercial situations and potential new transparency obligations.
To be sure, there are legitimate concerns over how generative AI could impact creative workers and cause other kinds of harm. But it is important for copyright policymakers to recognize that artificial intelligence technology has the potential to promote the progress of science and the useful arts on a tremendous scale. It is both sensible and lawful as a matter of US copyright law to let the robots read. Let’s make sure that the process described by Professor Litman does not get in the way of building AI tools that work for everyone.