Ringvorlesung - Generative AI, Responsibility Gaps, and the Credit-Blame Asymmetry

When generative AI technologies generate novel texts, images, or music in response to prompts from users of these technologies, who exactly should be considered as the author of these AI outputs? Are texts created by generative AI perhaps best considered as authorless texts? In my presentation, I will relate the above-mentioned questions to the topic of who (if anyone) can take credit for, or potentially be blameworthy for, outputs created with the help of large language models and other generative AI technologies. I will argue that there is an important asymmetry with respect to how easily people can be praiseworthy or blameworthy for outputs they create with the help of generative AI technologies: in general, it is much harder to be praiseworthy for impressive outputs of generative AI than it is to be blameworthy for harmful outputs that we may produce with generative AI.