A Practical Introduction to Generative AI, Synthetic Media, and the Messages Found in the Latest Medium

Abstract

OpenAI’s text generation program ChatGPT and the text-to-image generators Stable Diffusion and Dall-E have broken records for early public adoption, capital investment, and a technological shift potentially more far-reaching than even the internet itself. The broad category of generative AI has the potential to disrupt industry, art, and culture, both if done poorly and if done well. Despite significant problems with accuracy and deep concerns about the social and legal consequences of the premature adoption of these technologies, global multinational enterprises are moving these projects out of the test labs and into everyday use. This article provides a comprehensive, but introductory overview of the development of generative AI, the training methods used to produce artificially generated content, the industry opportunities for generative AI, and the legal considerations that enterprises adopting these technologies should consider. After discussing the development and implementation of the technology, the article emphasizes the key concerns regarding algorithmic bias, adherence to civil rights laws and community standards, concerns regarding defamation, and legal liability under intellectual property laws including copyright, trademark, and trade secret. The article also provides common sense steps that enterprises adopting early generative AI systems should incorporate into their agreements with the producers of generative AI networks.

Keywords: Artificial intelligence, algorithm, generative AI, large language models, neural networks, generated adversarial networks, synthetic media, training, deepfakes, soundalikes, text generation, videogames, films, videos, designs, bias, digital redlining, civil rights, copyright, trademark, trade secret

JEL Classification: C45, Z11, O35, O34, 033, M14, L26, K13, K10, J11, E24, D23, C55, C38, B1