# Explainable systems and Black box systems

### Types of AI system/Models 

There are two types of AI systems that can be defined 

1. Explainable systems or White box systems
2. Black Box systems

### Explainable systems / White Box systems (models)

![White Box](/files/-Mg3J-FgrNZZA_cuLjTG)

These are the AI systems or Models where one can easily map the input to the output. One knows what is happening during the training phase and how the system makes the appropriate choice.

Examples: Linear Regression and Decision Tree

### **Black Box systems**

![Black Box](/files/-Mg3K8iEDUDnz48LUEiI)

These are the AI systems or Models where one can not map the input to the output. You do not know what is happening within the system.

Example: Deep Learning and Computer Vision Models


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://maheshwarappa-a.gitbook.io/explainable-ai-1/explainable-systems-and-black-box-systems.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.