FairIntern: An AI-Powered Smart Allocation Engine for PM Internship Scheme

Abstract: The introduction of the Pradhan Mantri Internship Scheme (PMIS) was aimed at bridging the gap between the students and the employers.The current method of assigning has both practical and rule-based operations, which in turn make the whole process rather translucent and often lead to bias. All these issues have been considered in the proposed paper, and hence, the FairIntern, an AI-based smart allocation engine is introduced that will make up with an internship match that is more accurate, proper and explainable.

The FairIntern system merges Natural Language Processing (NLP) which is used to read and understand the student resumes and the internship descriptions with Machine Learning (ML) that does multi-factor matching based on skills, qualifications, and preferences. Besides, fairness-aware constraints are used to handle the issue of balancing across the gender, region, and other important factors while the system consistently follows an increment based development strategy allowing matching accuracy polished as the data becomes available.

Experimental studies demonstrate that the FairIntern system involves a reduced amount of manual work, better relevant matching, and is more transparent in comparison to the methods of allocating funds traditionally. The proposed system offers a scalable and non-profit internship allocation framework that not only complies with the digitalization of India and Skill development program but at the same time guarantees that internships are distributed in a fair and data-driven manner.

Keywords: Artificial Intelligence, Internship Allocation, Fairness-Aware Machine Learning, Resume Parsing, Natural Language Processing, PM Internship Scheme.