Unlocking AI Insights: The Power of Graph Technology for AI Explainability

AI & graph technology

AI and Graph Technology AI Explainability

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MLOps and Explainability Ensuring Transparency and Accountability in AI

That's why graphs are quietly becoming the backbone of many AI workflows. While large language models (LLMs) have captured the public imagination, Hodler notes that graphs are essential for grounding these systems. They help mitigate hallucinations, enhance explainability , and make decisions more appropriate to real-world conditions.

Why Graph Technology for AI Reasoning

AI Explainability Explained When the Black Box Matters and When It

However, as we integrate AI systems more deeply into our lives and businesses, the need for explainability , interpretability, and ethical responsibility becomes critical.

Graph AI , Explainability GraphXAI is a resource to systematically evaluate and benchmark the quality of GNN explanations. A key component is a novel and flexible synthetic dataset generator called ShapeGGen that can automatically generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs ) together with ground-truth explanations ...

Explainability in AI SHAP LIME and Interpretability Techniques

Evaluating explainability for graph neural networks

However, explainability in graph machine learning is an emerging area lacking standardized evaluation strategies and reliable data resources to evaluate, test, and compare GNN explanations 15.

Explainability makes an AI system more understandable, transparent, interpretable, auditable, and responsible, while also reducing risks. Recently, researchers have developed several eXplainable AI (XAI) systems capable of generating explainable models or predictions, thus enabling users to better understand the AI system and its decisions [4].

Explainable AI

Finally, we identified new trends in achieving explainable AI . In particular, we elaborate on the strong link between the explainability of AI and the meta-reasoning of autonomous systems. The concept of meta-reasoning is "reason the reasoning", which coincides with the intention and goal of explainable Al.

This paper aims to provide a comprehensive survey that offers a structured overview of xAI techniques tailored to specific machine learning paradigms, serving as both a primer and a reference for researchers, AI developers, and industry professionals seeking to implement explainability in their AI -driven systems.

AI Interpretability & Explainability

A comprehensive guide to AI interpretability and explainability — techniques like mechanistic interpretability, SHAP, LIME, and tools for building transparent AI systems.