Explainable Artificial Intelligence and Social Theory Integration for Advancing Educational Equity in Nepal | Chapter 2| Mathematics and Computer Science: Research Updates Vol. 8

 

This chapter examines entrenched socioeconomic disparities in Nepal’s education system through the integration of explainable artificial intelligence (XAI) and foundational social theories of equity. While Nepal has made progress in enrollment, persistent gaps in access, retention, and learning outcomes remain among groups marginalized by caste, gender, and geography. Existing policy analyses often rely on linear statistics or descriptive methods and lack operational links to sociological theory. To address this lacuna, we develop a mixed-methods framework that blends predictive machine learning with interpretability (SHAP) and qualitative inquiry to ground algorithmic findings in lived experience. Using national-level datasets — notably the Education Management Information System (EMIS) and the Nepal Living Standards Survey (NLSS)—we operationalize a Capability Index and train ensemble models (Random Forest and XGBoost) to predict capability deprivation and dropout risk. SHapley Additive exPlanations (SHAP) are applied to attribute model outputs to observable socioeconomic and school-level features. We formalize the predictive problem and its interpretability as follows: given feature set X = {x1, . . . , xn} and an outcome Y (capability index or dropout probability), we estimate \(\hat{Y}\) = f(X; θ) and decompose \(\hat{Y}\) additively into baseline and feature contributions \(\hat{Y}\) = ϕ0 +\(\Sigma\)i ϕi. This decomposition informs policy levers by quantifying marginal contributions of poverty, distance to school, caste status, and school resources. Beyond technical contributions, the chapter situates model outputs within Sen’s Capability Approach and Bourdieu’s Cultural Capital Theory to interpret how structural constraints and cultural resources shape educational opportunity. Deliverables include a resource allocation framework, SHAP-driven simulation dashboards for policymaking, and early-warning indicators for dropout prevention. Qualitative interviews with educators and community stakeholders are used to validate and contextualize the quantitative results. Together, these elements advance both theory and practice: they demonstrate how XAI can produce socially meaningful, policy-ready evidence for more equitable education in Nepal and similar low- and middle-income contexts.

 

Author(s) Details

Anmol Adhikari
Department of Computer Science, Noida International University, India.

 

Vivek Kumar Sinha
Department of Computer Science and Engineering, Noida International University, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6555

Predicting Diabetes Using Machine Learning: A Comprehensive Framework with Model Interpretability | Chapter 4| New Horizons of Science, Technology and Culture Vol. 3

 

This chapter explores the construction of a detailed machine learning (ML) framework for predicting diabetes using diverse real-world datasets. The alarming rise in diabetes prevalence globally and particularly in developing nations such as India necessitates innovative approaches for early detection and intervention. Traditional diagnostic techniques, though clinically established, often fall short in scalability and adaptability. This study focuses on bridging this gap by integrating ML methodologies that not only offer superior prediction accuracy but also provide transparency through interpretability tools.

 

Key contributions of this work include the comprehensive data preprocessing steps (missing value treatment, normalisation, encoding, and SMOTE-based class balancing), the comparative evaluation of three widely used classifiers (Logistic Regression, Random Forest, and XGBoost), and the use of SHAP values for enhancing model interpretability. Among the models tested, XGBoost achieved the highest performance with an accuracy of 97.93%, AUC of 0.9974, and excellent sensitivity and specificity values, confirming its suitability for real-world healthcare applications. The chapter concludes with discussions on model performance, interpretability, clinical relevance, limitations, and avenues for future research.

 

Author(s) Details

 

Mounika Panjala
Department of Statistics, Osmania University, Hyderabad-7, Telangana, India.

 

Bhatracharyulu N.Ch.
Department of Statistics, Osmania University, Hyderabad-7, Telangana, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v3/5950