Integrating XAI for Anomaly Interpretation

implementationChallenge

Prompt Content

Once your anomaly detection model is functional, integrate an Explainable AI (XAI) technique (e.g., SHAP or LIME) to interpret the model's decisions for detected anomalies. This XAI output should highlight which specific sensor readings or features contributed most significantly to the anomaly score. Your goal is to generate structured insights that can be fed into a Large Language Model for human-readable explanations.

Try this prompt

Open the workspace to execute this prompt with free credits, or use your own API keys for unlimited usage.

Usage Tips

Copy the prompt and paste it into your preferred AI tool (Claude, ChatGPT, Gemini)

Customize placeholder values with your specific requirements and context

For best results, provide clear examples and test different variations