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
