A Data-Driven Fairness-Aware Machine Learning System for Enhancing Equity in Engineering Admissions

Abstract

In India, engineering college admissions continue to be a high stakes affair, especially when being applied in the Tamil Nadu Engineering Admissions (TNEA) system where students of varied socioeconomic backgrounds are subjected to obscured decision-making processes and dearth of guidance. The current paper describes a machine learning system that is both data-driven and fairness-conscious to increase the degree of transparency and fairness in the process of admission. The suggested system has a hybrid architecture which consists of regression-based cutoff prediction engine with a fairness-constrained recommendation engine which matches students to appropriate colleges based on academic and contextual profiles. Ensemble regressors are applied to historical data, such as cut off scores, group codes, the type of college, and counseling marks, yielding a predictive accuracy of 92.6% (R2 score) and mean absolute error (MAE) of 4.12. Such measures of fairness as demographic parity difference (DPD) and equal opportunity difference (EOD) are less than 0.03 between caste and gender groups, which means that there is a small amount of bias. The hybrid recommender combines both content-based and collaborative filtering and has a top-5 recommendation accuracy of 94.6% and Mean Reciprocal Rank (MRR) of 0.837. A mobile/web interface provides real-time and personalized advice to students and families. This system does not only enhance the results of the admissions but also creates an algorithmic transparency and social inclusivity in counseling processes. The architecture is ethically aligned, scalable and modular that it can be deployed in various states. The findings confirm the validity, equity and practicality of the system. Further expansions of it will incorporate reinforcement learning and real-time feedback loops to improve recommendation accuracy and equity adherence.