Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
Explainability
AI explainability (XAI) refers to the process of explaining to an individual the decision-making process of an AI model
AI explainability (XAI) refers to the process of explaining to an individual the decision-making process of an AI model. XAI focuses on understanding and interpreting predictions made by AI models. It is the process of analyzing ML models and decisions and ensures that we understand why the system made a particular decision. The practice lets us peek inside the AI ‘black box’, to understand the key drivers behind a specific AI decision.
The various approaches to AI Explainability are usually driven by the model type, computational costs, the speed versus accuracy trade-off, AI governance, risk management, compliance needs, and global and local feature importance. Most approaches to AI Explainability include LIME and SHAP.
Is Explainability critical for your AI solutions?
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AryaXAI provides the most accurate explainability and alignment stack to deliver accurate, true-to-model explainability, monitoring, risk management, and alignment techniques essential for highly mission-critical or regulated AI solutions.
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