Unlike with traditional predictive AI models, today's generative AI models are increasingly designed to be general-purpose, able to perform a wide range of tasks. This makes it challenging to develop a reliable and useful understanding of the ways in which this technology is and could be used. As a result, academic and policy researchers and generative AI providers have started to publish the results of their own investigations about the use of generative AI. This information is, however, fragmented, potentially incomplete, sometimes ambiguous, and often lacking in methodological specificity. In this paper, we conducted an integrative review to build a multi-dimensional framework that specifies what kind of information about generative AI use could be reported and how, and illustrated its analytical utility by applying the framework to a collection of over 110 industry documents. Our analysis reveals systematic patterns and omissions in current industry reporting and reflects on the narratives this reporting collectively advance about generative AI use.
ACM CHI Conference on Human Factors in Computing Systems