"A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications"

Multi-criteria XAI (Explainable AI ) evaluation system that bridges the gap between technical accuracy and practical usability.

This paper introduces a unified framework for evaluating Explainable AI (XAI) methods across different domains like healthcare and security. It addresses the critical gap in standardized evaluation procedures by incorporating multiple criteria - fidelity, interpretability, robustness, fairness, and completeness - into a dynamic scoring system.

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https://arxiv.org/abs/2412.03884

Original Problem 🤔:

Current XAI evaluation methods lack standardization and comprehensive assessment criteria. They often focus on single aspects like interpretability while ignoring crucial factors like robustness and fairness.

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Solution in this Paper 💡:

→ The framework uses a weighted scoring system that dynamically adjusts based on domain requirements.

→ It incorporates five core evaluation metrics: fidelity, interpretability, robustness, fairness, and completeness.

→ The system employs advanced visualization techniques like Grad-CAM++ for generating interpretable heatmaps.

→ Real-time adaptability ensures the framework stays relevant as data patterns and stakeholder needs evolve.

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Key Insights 🔍:

→ Dynamic weighting mechanism allows domain-specific prioritization of evaluation criteria

→ Integration of quantitative and qualitative metrics enables comprehensive assessment

→ Cross-domain applicability demonstrated through healthcare, agriculture, and security use cases

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Results 📊:

→ Framework achieved 4.5/5.0 score in healthcare applications vs 4.2 for Grad-CAM++

→ Demonstrated 20% higher robustness compared to LIME and SHAP

→ Maintained consistent performance across diverse domains with scores above 4.0