# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 1 Introduction  
Machine learning (ML) and artificial intelligence (AI) systems are transforming society and industries by automating critical decision-making processes, influencing areas such as criminal justice, healthcare, finance, and employment. These algorithms are increasingly scrutinized for potential biases and unfair outcomes, which, if unchecked, may harm vulnerable groups. For instance, the use of algorithms in criminal sentencing has disproportionately impacted marginalized communities, as seen with COMPAS, a recidivism prediction tool found to have higher false-positive rates for African Americans than for Caucasians [1, 2, 3]. Since then, researchers and practitioners are increasingly focused on developing methodologies that not only enhance the accuracy and efficacy of ML models but also promote fairness, accountability, transparency, and ethics (FATE). The 2022 review by [PERSON] et al. [4] offers a comprehensive analysis of principles and methods necessary to develop trustworthy artificial intelligence (AI) systems. The authors emphasized the critical aspects of transparency, fairness, accountability, and privacy, which are essential to build reliable AI technologies for high-stakes fields, including healthcare, justice, and education. They highlighted issues such as algorithmic bias, data privacy, and lack of explainability, proposing a framework that incorporates human-centered design and collaborative oversight throughout the AI lifecycle. In the highly competitive landscape of student admissions, institutions face the challenge of evaluating candidates through a complex interplay of academic, professional, and personal attributes. Ensuring fairness and transparency in the admissions process is not only critical for maintaining institutional credibility but also for fostering diversity and equity. Traditional methods often rely on subjective criteria, leading to potential biases thatcan undermine these goals. Machine learning (ML) models offer a promising alternative by providing data-driven insights to predict admission outcomes while maintaining consistency and objectivity. However, concerns about the interpretability and fairness of these models remain paramount, particularly in sensitive applications like admissions. A widely debated approach to achieving fairness in machine learning models is the apparent removal of sensitive or protected attributes (fairness through unawareness). Some researchers argue that including these attributes (fairness through awareness) can help address and correct structural disparities by explicitly accounting for them in the model. However, others caution that incorporating such variables risks reinforcing existing biases, as the model may inadvertently use them in a way that perpetuates discrimination [1; 6; 7]. Addressing fairness in ML involves defining, measuring, and mitigating bias, but this is challenging due to the trade-offs between fairness metrics and other model performance objectives. The research community explore various approaches such as pre-processing data adjustments (e.g., class balancing techniques), in-processing constraints during model training (e.g., adversarial learning), and post-processing corrections to reduce bias and enhance the fairness of outcomes (e.g., group-aware threshold adaptation). These techniques seek to balance the goals of accuracy and fairness, as enhancing one can sometimes impair the other [1; 2; 7; 8]. In particular, the education sector exemplifies the need for fairness in algorithmic decision-making. To build more equitable educational systems, it is essential to prioritize fairness. [PERSON] and [PERSON] [9] presented a framework of ethical principles to align learning analytics practices with legal and ethical standards. The authors in [10] provided an in-depth analysis of ethical guidelines for implementing artificial intelligence (AI) in K-12 education, focusing on children's unique needs and rights in educational settings.