AI-Driven Skill Mapping and Gig Economy Matching Algorithm for Youth Employment within Nairobi's Informal Sector

The issue of unemployment among the youth is still a big challenge in Nairobi, Kenya, where there is a rapidly growing youth population with limited formal job opportunities. This has led to an increase in the numbers found in the informal economy and the gig economy. Despite this, there is a big gap between the two parties, where the youth do not know what is required while those offering work are faced with difficulties to identify those required to do the work. This paper presents a new approach utilizing Artificial Intelligence to close this gap. The new approach uses natural language processing (NLP) and machine learning (ML) to dynamically link and derive required skills from multiple sources such as youth-supplied information, short-term work experience, and recommendations within the community. This provides a rich and dynamic profile of each person beyond work experience. Simultaneously, this paper uses machine learning to derive opportunities within the gig economy and market demands in real time. A final algorithm is developed to link available youth to work opportunities within this economy according to proximity to required skill sets and predicted wages. The pilot project implementation shows a big increase in correct matches within the gig economy and increased reports of wages earned compared to other approaches to informal job search. This paper concludes that there is big potential within utilizing Artificial Intelligence to dynamically map and link youth to required skill sets within Nairobi's informal economy.