Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies

This study investigates the implementation and future potential of neural networks for socio-labor regulation within the urban economies of BRICS megacities, emphasizing a human-centric AI approach. Analysis reveals significant disparities in AI development across these urban centers, with Beijing and Shanghai leading in investment, while Moscow ranks third among all analyzed cities with an AI investment of $620 million, contributing to the growing global urban AI landscape where worldwide smart city spending is projected to reach hundreds of billions of dollars. The research examines key indicators of AI adoption, such as the number of startups and the percentage of companies utilizing AI solutions in these major cities. Specifically, Bangalore stands out with 320 AI startups and 58% of companies implementing AI, while in Russia, Moscow reports 230 startups and 48% company adoption, reflecting varying rates of AI integration within urban business ecosystems globally where the average AI adoption rate for enterprises is still below 30% according to some reports. Beyond general AI adoption, the study analyzes the deployment and effectiveness of neuromorphic AI approaches specifically for socio-labor regulation systems. Current data indicates uneven deployment of neuromorphic labor systems across BRICS megacities, with Shanghai showing a high deployment score of 9.5/10, significantly ahead of cities like Durban at 5.2/10, highlighting the uneven global progress in applying advanced AI for workforce management, including in Russian cities like Moscow with a 7.8/10 deployment score, as the worldwide market for AI in HR is rapidly expanding towards billions. Key metrics examined for these specialized systems include AI-driven job matching efficiency, the number of neural network workforce training programs, and labor market prediction accuracy. The study concludes that achieving effective and ethical socio-labor regulation through AI requires a human-centric approach that addresses disparities and integrates technological, social, and psychological considerations for inclusive urban development. Participation in the article: Irina Karabulatova - general editing, writing the "introduction" and "discussion" sections; Olga Ergunova - project idea, writing the "results" section, working on models (Fig.2-3), compiling tables; Andrey Somov - working on the project methodology and writing the code.