How to Make Artificial Intelligence Explain Its Own Decisions

A system that helps complex machine learning models explain why they made a specific decision by turning their data into simple, readable rules.

What it covers

This system solves the 'black box' problem in artificial intelligence, where a model makes a decision but cannot explain why. It takes the original data used to train the model and creates a 'transformed data set' that links specific input features to the model's final predictions. It then uses rule-mining algorithms to find patterns—essentially 'if-then' statements—that describe how the model behaves. When the model makes a new prediction, the system looks at these pre-calculated rules to provide a human-readable reason for that specific outcome.

What it doesn't cover

• —Does not cover models that do not use a training set of observation records.
• —Does not cover explanations generated without using a rule-mining algorithm.
• —Does not cover systems that explain decisions using non-rule-based methods like feature importance heatmaps or saliency maps.
• —Does not cover real-time model retraining during the explanation generation process.

The clever bit

Instead of trying to interpret the complex internal math of a neural network directly, it treats the model as an object to be studied, mining rules from its outputs just like you would mine data from a database.

Why it matters

As AI is used for high-stakes decisions like loan approvals or medical diagnoses, regulators and users demand transparency. This patent provides a structured way for cloud-based AI services to offer 'explainability' as a feature, which is essential for building trust in automated systems. It helps companies comply with requirements like the 'right to an explanation' found in privacy laws.

Real-world examples

• 1.Amazon SageMaker Model Monitor
• 2.Automated credit scoring systems
• 3.AI-driven fraud detection services