Home▸Articles▸AI & Machine Learning

Explainable AI: A Comprehensive Guide

Explainable AI aims to make complex machine learning models more transparent and understandable, allowing users to trust their predictions.

mysimulator teamUpdated June 2026≈ 3 min read▶ Open the simulation

A Complete Practical Guide to Interpreting and Explaining Machine Learning Models

Explainable AI makes ‘black boxes’ understandable. From feature importance to counterfactual explanations – methods are used for understanding and trusting ML systems.

1. Core Principles of Explainable AI

The Problem: Explanations Can Be Incorrect or Misleading

Solution: Validate explanations through experiments, user studies.

Utilize multiple explanation methods

live demo · related simulation● LIVE

Forward Pass for Images

Calculating the class gradient with respect to feature maps of the last convolutional layer

Weighted combination of feature maps with gradients as weights

Frequently asked questions

What is Unified SHAP?

Unified SHAP: SHAP values for all modalities

What are cross-modal attention patterns?

Attention: Cross-modal attention patterns

How can gradients be used in different modalities?

Gradient-based: Gradients for each modality

What types of data require different explanation methods?

Challenges: Different data types require different explanation methods.

▶ Try it live

Everything above runs in your browser — open Gradient Descent Visualiser and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Gradient Descent Visualiser simulation