Fairness-Aware Machine Learning Models for Resource Allocation in Education

Abstract

Fairness considerations are essential when deploying machine learning models for resource allocation in educational settings to ensure equal opportunities for all students. This paper proposes fairness-aware machine learning approaches for tasks such as student admissions, course recommendations, and resource allocation. We discuss fairness metrics, bias mitigation techniques, and ethical implications associated with deploying these models in educational decision-making processes.