Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)
Supriya Manna, Niladri Sett
TL;DR
The paper tackles how parental income influences educational opportunities and decisions within modern education by applying a suite of Explainable AI tools to analyze an Adult Census–based binary inference task. It uses an XGBoost baseline, SHAP/LIME explanations, global surrogate models, and SP-LIME to dissect feature importance and interdependencies, revealing both informative patterns and hidden biases. The study uncovers persistent biases related to race, nationality, and sex, with some explainability methods uncovering unfairness that others miss, highlighting the need for nuanced fairness definitions and stronger transparency. The findings emphasize the policy relevance of xAI for designing more reliable, accountable, and equitable educational systems that address socio-economic disparities.
Abstract
Modern Education is not \textit{Modern} without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children's schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.