Fairness-Driven Explanations: Designing XAI That Actively Surfaces and Mitigates Social Biases in Healthcare and Education NLP

Adewumi et al. (2025) advocate for equitable and explainable AI in high-impact sectors, but most current XAI methods focus on transparency, not equity. In healthcare and education, NLP models risk perpetuating biases—e.g., in triage (Arnaud et al., 2023) or academic assessment (Sen et al., 2023). This research proposes integrating fairness metrics and bias-detection modules directly into the explanation pipeline. For instance, every model prediction would come with an explanation not just of “why,” but also “how much did sensitive features (e.g., gender, socioeconomic status) influence this decision?” and whether these influences deviate from fairness standards. The system could also suggest interventions. This approach extends XAI beyond interpretability, positioning explanations as tools for social accountability—and is distinct from most current work, which rarely integrates fairness assessments into the explanation process itself.

References:

1. Explainable NLP Model for Predicting Patient Admissions at Emergency Department Using Triage Notes. Émilien Arnaud, Mahmoud Elbattah, Pedro A. Moreno-Sánchez, Gilles Dequen, Daniel Aiham Ghazali (2023). BigData Congress [Services Society].
2. Do education and living standard matter in breaking barriers to healthcare access among women in Bangladesh?. K. K. Sen, Shahnaz Nilima, F. Zahura, W. Bari (2023). BMC Public Health.
3. Democratizing public-impact algorithms: Advancing equitable and explainable AI systems for decision-making in U.S. health, finance, and education sectors. Farouk G. Adewumi, Chibuzor Njoku, Uchechukwu Okafor (2025). International Journal of Management & Entrepreneurship Research.