Supplementing Korean artificial intelligence ethical principles: A five-year update

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Concerns regarding the potential for artificial intelligence to cause harm to humans have intensified. Since approximately 2020, various organizations-including governments, think tanks, and big tech companies-have sequentially enacted artificial intelligence ethical principles. However, prior studies on these principles exhibit several limitations: First, previous studies failed to provide an integrated framework of artificial intelligence ethical principles; second, they lack a consensus on the prioritization of these principles; third, five years have passed since 2020 when the artificial intelligence ethical principles were enacted, but they have not been updated; and fourth, there has been a lack of strategic consideration regarding the relative importance and performance of these principles. To address this gap, this study extracted 17 artificial intelligence ethical principles by synthesizing 37 representative previous studies. Universal priorities were determined based on a comparative analysis of these principles' reference rankings. By evaluating the shifts in these ranking before and after 2020, this study identifies three principles-transparency, robustness, prohibition of infringement-that should be continuously adopted in artificial intelligence system development, and ive principles- responsibility, wellbeing, human rights, controllability, and sustainability-require greater emphasis and implementary development. the existing 10 Korean AI ethical principles are proposed to be expanded to 14. The results of the Importance-Performance Analysis (IPA) indicate that social value principles, such as respect of diversity, human rights, transparency, common good, and solidity, which demonstrated high performance levels, should be continuously integrated into development of artificial intelligence systems. Moreover, it is urgent to establish a governance framework to ensure accountability for responsible artificial intelligence and to develop systems for safe artificial intelligence usage, focusing on privacy, safety, prohibition of infringement, and data management. This study contributes to the foundation for building responsible, trustworthy, and safe AI systems.