A Data Centric HitL Framework for Conducting aSsystematic Error Analysis of NLP Datasets using Explainable AI.

Journal: Scientific reports
Published Date: Aug 19, 2025

Abstract

The interest in data-centric AI has been recently growing. As opposed to model-centric AI, data-centric approaches aim at iteratively and systematically improving the data throughout the model life cycle rather than in a single pre-processing step. The merits of such an approach have not been fully explored on NLP datasets. Particular interest lies in how error analysis, a crucial step in data-centric AI, manifests itself in NLP. X-Deep, a Human-in-the-Loop framework designed to debug an NLP dataset using Explainable AI techniques, is proposed to uncover data problems related to a certain task. Our case study addresses emotion detection in Arabic text. Using the framework, a thorough analysis that leveraged two Explainable AI techniques LIME and SHAP, was conducted of misclassified instances for four classifiers: Naive Bayes, Logistic Regression, GRU, and MARBERT. The systematic process has resulted in identifying spurious correlation, bias patterns, and other anomaly patterns in the dataset. Appropriate mitigation strategies are suggested for an informed and improved data augmentation plan for performing emotion detection tasks on this dataset.

Authors

  • Ahmed El-Sayed
  • Aly Nasr
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Youssef Mohamed
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Ahmed Alaaeldin
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Mohab Ali
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Omar Salah
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Abdullatif Khalid
    Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt.
  • Shaimaa Lazem
    Informatics Research Institute, City of Scientific Research and Technological Applications, New Borg El-Arab, Egypt.

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