Mapping the Ethics of Generative AI: A Comprehensive Scoping Review
This provides a comprehensive taxonomy for scholars, practitioners, and policymakers to navigate ethical risks like fairness and safety, though it is incremental as it reviews existing discourse.
The authors conducted a scoping review to synthesize ethical debates on generative AI, identifying 378 normative issues across 19 topic areas and ranking them by prevalence in the literature.
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.