AiVibeExplainable AI (XAI)
Explainable AI (XAI) covers methods that make the decisions of machine learning models understandable to people, showing which inputs influenced a prediction and why, so that results can be trusted, debugged and audited.
Some models, such as linear models and shallow decision trees, are interpretable by design. For complex models, post-hoc methods explain predictions: SHAP attributes a prediction to input features using Shapley values from game theory, LIME fits a simple local model around an individual prediction, and saliency methods such as Grad-CAM highlight the image regions that most influenced a vision model. Global methods summarise overall feature importance.
Explanations help engineers check that an inspection model is looking at the defect rather than the background, help maintenance teams see which sensor trends triggered a failure prediction, and support decisions that must be justified to customers, auditors or regulators, such as credit decisions.
Post-hoc explanations are approximations and can be unstable or misleading, so they should be validated rather than treated as ground truth. Explaining large language models remains an active research area. Transparency requirements appear in regulations such as the EU AI Act for high-risk systems, and the right level of explanation depends on the audience and the decision.
Key points
- Makes model decisions understandable to people
- SHAP, LIME and Grad-CAM are widely used explanation methods
- Helps verify that models rely on the right features
- Post-hoc explanations are approximations, not ground truth
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.