From Algorithms to Grants: Leveraging Machine Learning for Research and Innovation Fund Allocation

In today’s rapidly evolving world, research forms a cornerstone of human progress leading to new products, services, and technologies, which, in turn, can stimulate economic growth and enhance the quality of life. Research enables people to learn, innovate and address the complex challenges facing a society. It is a powerful tool for making the world a better place and as such many countries endeavor to support the research landscape by providing research grants in different sectors. The process of allocating research grants plays a pivotal role in fostering scientific progress, innovation and knowledge. The traditional manual selection of grant proposals, while well-established can be resource intensive time-consuming, subjective, and prone to bias. This paper presents an unconventional strategy that leverages machine learning algorithms to enhance the fairness, efficiency, and transparency of the grant allocation process by removing human biases and prejudices that can inadvertently influence funding decisions. The study discusses the design and implementation of a machine learning-based grant allocation system using historical grant data from a reputable funding agency and provides empirical evidence of its effectiveness by selecting the best performing text classification algorithm from a comparative analysis of three models and integrating it into a web based application.