Bias Detection And Fairness Optimization In Nlp-Based Language Assessment Systems

Abstract

Background: Natural Language Processing (NLP) has significantly transformed educational assessment through the development of automated systems such as Automated Essay Scoring (AES). These systems enable scalable, cost-effective, and efficient evaluation of student writing, particularly in large-scale and standardized testing environments. However, concerns have emerged regarding bias and fairness, especially in evaluating essays from students with diverse linguistic, cultural, and socio-economic backgrounds. Such biases may arise from training data, model design, or evaluation processes, potentially disadvantaging non-native speakers and underrepresented groups.

Methods: This study proposes a comprehensive experimental framework to detect bias and optimize fairness in NLP-based language assessment systems. Bias is evaluated at both demographic and linguistic levels using established fairness metrics and explainable artificial intelligence (XAI) techniques. Additionally, multiple fairness optimization strategies, including data balancing, adversarial debiasing, and bias-aware learning approaches, are examined to improve equity without compromising model performance.

Results: The findings reveal the presence of measurable bias in several widely used NLP-based scoring models. The application of fairness-oriented interventions demonstrates a significant reduction in scoring disparities across different groups, while maintaining acceptable levels of predictive accuracy.

Conclusion: Bias in automated essay scoring systems is a critical challenge that must be addressed to ensure equitable educational assessment. The integration of fairness evaluation and optimization strategies, supported by XAI methods, can substantially improve the transparency and fairness of NLP-based systems. This study contributes to the advancement of responsible AI practices in educational technology and supports the development of more inclusive and trustworthy assessment tools.