Hybrid intelligence for the public sector: A bibliometric analysis of artificial intelligence and crowd intelligence

With the increasing attention paid to artificial intelligence (AI) and crowd intelligence (CI) in government, their connections still need to be explored. This study explores the dynamic relationship between AI and CI that constitutes hybrid intelligence for the public sector. Thus, we adopt a bibliometric analysis to identify trends, emerging themes, topics, and interconnections between these two streams of literature. Our review illustrates the intersection between AI and CI, revealing that AI designs can improve efficiency from CI inputs. Meanwhile, AI advancement depends on the quality of CI data. Furthermore, our review highlights key domains such as smart cities (Internet of Things), personnel design, social media, and governance through cases. Based on these illustrated cases, we conceptualize a hybrid intelligence spectrum, ranging from “engagement” to “efficiency,” with crowd intelligence anchoring the former through its emphasis on public participation and AI anchoring the latter through its focus on automation and optimization. Hybrid intelligence, encompassing various forms, occupies the middle ground to balance maximizing public engagement and achieving computational efficiency. Additionally, we elaborate on components of hybrid intelligence designs regarding input (conscious crowds and unconscious crowds), process (algorithmic management and artificial discretion), and outcome (user-focus benefits and non-user-focus outputs). Finally, we recommend prioritizing questions related to the design, regulation, and governance of hybrid intelligence for the public sector. • Bibliometric analysis reveals applications of hybrid intelligence in smart cities, personnel design, social media, governance. • Our review conceptualizes a hybrid intelligence spectrum, including CI, augmented CI, human-centered AI, and AI. • Hybrid intelligence (HI) balances CI by emphasizing participation and AI by prioritizing automation and optimization. • HI component includes an input (conscious vs. unconscious crowds), process (algorithmic management vs. artificial discretion), outcomes (user- vs. non-user-focus outputs).