Article by Jack Hardinges, and Irina Bejan: “Attribution has always been a cornerstone of working with other people’s creativity and knowledge.
As Creative Commons reminds us, practicing attribution serves many important functions, from providing verifiable evidence of a claim, to paying respect to the work of others, and creating pathways for traffic and financial value to flow.
Despite the importance of attribution, many of today’s AI systems fail to acknowledge the sources of knowledge and creativity that make them possible.
A recent study led by the AI Disclosures Project found that more than 30% of responses by leading search-enabled Large Language Models (LLMs) provided no attribution whatsoever. Even where attribution is provided, it can be limited in ways that are hidden to users, and deep technical challenges persist in connecting outputs from AI to the sources they are derived from.
Despite this, we believe there are good reasons to be optimistic that AI systems can attribute the sources they use. Not perfectly, and not always, but enough and improving, such that we should reject the argument that a lack of attribution is an inevitable side effect of the way the technology works. The commons needn’t become “hidden substrate.”..(More)”.
