# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 2 Literature Review
### (Un)Fairness in Educational Settings  
The review article by [PERSON] et al. (2018) delved into the application of artificial intelligence (AI) in higher education and highlighted the growing ethical concerns regarding the morality of AI programs. Through a systematic review of 33 articles sourced from databases such as SCOPUS and Web of Science, the authors explored how fairness, accountability, transparency, and ethics (FATE) are defined and studied within this domain. Their analysis categorized definitions of FATE into descriptive and technical types, revealing notable patterns: fairness tended to have more descriptive definitions and was studied quantitatively, whereas ethics was predominantly approached through qualitative definitions and methodologies. The adoption of machine learning systems has profoundly transformed workflows and decision-making across various industries, including education. However, these systems raise critical ethical issues, particularly regarding their fairness and the impact on diverse populations. [PERSON] et al. (2018) conducted a systematic mapping of fairness research in higher education, analyzing 63 primary studies published between 2002 and 2023. This comprehensive study identified key research themes, including traditional machine learning algorithms, sensitive variables such as gender and race, and diverse fairness definitions. [PERSON] et al. [30] shed light on systemic inequalities in higher education, with a focus on the persistent 13% degree-awarding gap for Black, Asian, and Minority Ethnic (BAME) students, alongside disparities affecting other protected groups. Their study evaluated the effectiveness of prediction models in identifying at-risk students and explored whether disaggregating data by protected attributes to create subgroup-specific prediction models could enhance fairness. The findings indicated that existing models disproportionately benefited the majority group, and subgroup-specific models failed to improve either fairness or accuracy, underscoring the complexity of addressing systemic inequities through technical interventions alone. [PERSON] et al. [31] examined the role of sensitive attributes such as gender, race, and disability in learning analytics models used to predict student outcomes. Drawing on data from a three-year study at a major Australian university, the research found that including or excluding these attributes had limited impact on model accuracy or fairness. Using Explainable AI (XAI), the study demonstrated that the relevance of sensitive attributes varied across student cohorts and contexts. This nuanced finding highlights the inadequacy of simplistic approaches--such as excluding sensitive variables--for achieving fairness. The authors advocate for more sophisticated bias mitigation strategies tailored to specific contexts. [PERSON] et al. [32] investigated data biases and their correlation with machine learning (ML) model fairness in education, focusing on distribution bias and hardness bias across five predictive tasks. The study proposed two class-balancing techniques to improve fairness. The results showed that addressing data biases could significantly enhance fairness (up to 66%) with minimal trade-offs in predictive accuracy (less than 1%). This work underscores the importance of tackling biases at the data level to improve equitable outcomes. [PERSON] et al. [33] introduced a Fair Logistic Regression (Fair-LR) algorithm designed to balance predictive accuracy and fairness. Fairness was evaluated using the Equalized Odds metric, which assesses consistency in type I (false positive) and type II (false negative) error rates across demographic groups. Experimental comparisons with traditional models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF), demonstrated that Fair-LR achieved comparable predictive accuracy while significantly improving fairness. This innovation offers a promising avenue for developing fairness-aware predictive models. [PERSON] et al.