Over the past year, the pervasive role of large language models (LLMs) and artificial intelligence (AI) in text generation has precipitated concerns about ethical usage, authorship, and transparent attribution. This has been true in legal practice, academia, and the corporate world, as well as in countless other arenas. In this Article, we identify the gap that has opened between those demanding proper disclosure (we should know when and to what extent AI is an author) and those struggling to respond to these demands. Part of the problem is that there is no system in place, no lingua franca, no set of norms for such disclosure. In the early aughts, a similar gap threatened copyright law, and legal scholars forged a solution in the Creative Commons. Now, with a similar form but distinct substance and function, we introduce the AIA (Artificial Intelligence Attribution), a system that properly and seamlessly attributes AI text authorship. The system involves the use of badges that delineate the nature of AI involvement—from research to writing to editing. In addition to filling the fundamental gap identified above, the benefits of the AIA vis-à-vis generative AI are at least threefold: (i) minimizing legal risk attendant to AI’s use (i.e., legal exposure stemming from contracts, consumer protection, and intellectual property); (ii) managing public perception of AI use; and (iii) facilitating ethical behavior. We discuss these benefits from both theoretical and empirical lenses. By ‘empirical,’ we are referring to original experimental research that we conducted to vet the AIA. Our findings suggested that use of the AIA, which enhanced attribution of AI authorship, may improve public perception and reduce legal risk. After discussing these benefits, we present three examples as to how AIA badges would look in practice. First, we explore the AIA in the law, a sector in which unacknowledged use of generative AI has already caused consternation and legal action. Then, we explore the AIA in academic (scholarship) and corporate (institutional speech) settings. These real-life applications enable us to illustrate the merits and potential challenges of adoption. In the ever- changing realm of human-AI cooperation, this Article establishes a framework for synergistic collaboration and integration. The AIA promises much-needed transparency, authenticity, and accountability in joint human-AI authored works while allowing for and promoting continued technical innovation.
