  The advent of generative artificial intelligence and the widespread adoption
of it in society engendered intensive debates about its ethical implications
and risks. These risks often differ from those associated with traditional
discriminative machine learning. To synthesize the recent discourse and map its
normative concepts, we conducted a scoping review on the ethics of generative
artificial intelligence, including especially large language models and
text-to-image models. Our analysis provides a taxonomy of 378 normative issues
in 19 topic areas and ranks them according to their prevalence in the
literature. The study offers a comprehensive overview for scholars,
practitioners, or policymakers, condensing the ethical debates surrounding
fairness, safety, harmful content, hallucinations, privacy, interaction risks,
security, alignment, societal impacts, and others. We discuss the results,
evaluate imbalances in the literature, and explore unsubstantiated risk
scenarios.
