Explanation in Machine Learning systems has been identified to be the main asset to have for large scale deployment of Artificial Intelligence (AI) in critical systems. Explanations could be example-, features-, semantics-based or even counterfactual to potentially action on an AI system; they could be represented in many different ways e.g., textual, graphical, or visual. All representations serve different means, purpose and operators. We built the first-of-its-kind XAI (eXplainable AI)

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... rm for critical systems i.e., Thales XAI Platform which aims at serving explanations through various forms. This paper emphasizes on the semantics-based explanations for Machine Learning systems.