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

Ahmed El-Sayed1, Aly Nasr2, Youssef Mohamed2

  • 1Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt. ahmed_elsayed@alexu.edu.eg.

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Summary
This summary is machine-generated.

Data-centric AI improves NLP datasets through systematic error analysis. The X-Deep framework, using Explainable AI, identifies and mitigates data issues in Arabic emotion detection.

Keywords:
Arabic emotion analysisError analysisExplainable AIHuman centered AIHuman in the loopLIMEMachine learningSHAPXAI

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI)

Background:

  • Data-centric AI iteratively improves data quality throughout the model lifecycle, unlike traditional model-centric AI.
  • The application and benefits of data-centric AI, particularly error analysis, remain underexplored in NLP datasets.

Purpose of the Study:

  • To investigate the manifestation of error analysis in NLP within a data-centric AI framework.
  • To propose and evaluate the X-Deep framework for debugging NLP datasets using Explainable AI (XAI).

Main Methods:

  • Developed X-Deep, a Human-in-the-Loop framework integrating XAI techniques (LIME, SHAP) for NLP dataset debugging.
  • Conducted a case study on Arabic emotion detection, analyzing misclassified instances across four classifiers (Naive Bayes, Logistic Regression, GRU, MARBERT).

Main Results:

  • Identified critical data anomalies including spurious correlations, bias patterns, and other irregularities within the Arabic emotion detection dataset.
  • Demonstrated the effectiveness of XAI techniques within the X-Deep framework for uncovering nuanced data problems.

Conclusions:

  • Systematic error analysis via X-Deep is crucial for improving NLP dataset quality and model performance.
  • The findings provide a foundation for enhanced data augmentation strategies in Arabic emotion detection and other NLP tasks.