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Open Source XAI Libraries: Trust Analysis of SHAP, LIME, DiCE, and Alibi | Synapse

May 8, 20260 citationsOpen Access

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Open Source XAI Libraries: Trust Analysis of SHAP, LIME, DiCE, and Alibi

OIOleh Ivchenko

Key Points

This research aims to analyze the trustworthiness of popular open-source explainable artificial intelligence (XAI) libraries.
Evaluated trust metrics across SHAP, LIME, DiCE, and Alibi libraries.
Conducted comparative analysis of interpretability and reliability.
Utilized case studies and benchmarks for assessment.
Identified varying levels of trust effectiveness across different libraries.
SHAP demonstrated superior trust metrics compared to LIME and Alibi.
DiCE showed potential for enhanced trust in specific contexts.

Abstract

Research article: Open Source XAI Libraries: Trust Analysis of SHAP, LIME, DiCE, and Alibi

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Oleh Ivchenko (Wed,) studied this question.

synapsesocial.com/papers/69fd7eb0bfa21ec5bbf06f9b https://doi.org/https://doi.org/10.5281/zenodo.20047105

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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1The XAI Frontier: What Comes After SHAP and LIME2026
2The Open Source XAI Ecosystem: Gaps, Opportunities, and Trusted Projects to Watch2026
3From Explanation to Evidence: A Method-Agnostic Pipeline for Regulatory-Grade XAI Artefacts Under the EU AI Act2026
4Community Governance Models for Open Source AI Projects: What Makes XAI Projects Trustworthy2026
5Supply Chain Security in Open Source AI: Auditing XAI Tool Dependencies2026