Explainable AI (XAI) refers to artificial intelligence systems that are able to communicate their actions and decisions in a manner that humans can comprehend. The purpose for XAI to create AI easier to understand, transparent, reliable as ...
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Traditional AI models, especially for deep learning models, are often “black boxes“—they make decisions, but humans don’t understand how. XAI solves this problem by: ✅ Building Trust – Users and businesses can trust AI decisions ...
XAI techniques help interpret AI decisions. Some common methods include: 🔹 Feature Importance – Identifies which factors (or “features”) influenced the AI’s decision the most.🔹 Decision Trees – A step-by-step flowchart that explains AI decisions in a structured ...
Transfer Learning is an AI technique in which a trained model is utilized to address a unique however related issue. Instead of creating the AI machine from scratch the model “transfers” information from a model that has already mastered useful ...