# 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. - **Patent:** US 10824959 - **Original title:** Explainers for machine learning classifiers - **Owner:** Amazon Technologies Inc - **Granted:** 2020 - **Status:** Active - **Times cited:** 37 - **Field:** ai_ml, software, consumer_electronics, finance ## What it does 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 does NOT 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. ## Real-world examples 1. Amazon SageMaker Model Monitor 2. Automated credit scoring systems 3. AI-driven fraud detection services ## 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. ## Frequently asked questions ### What does How to Make Artificial Intelligence Explain Its Own Decisions cover? A system that helps complex machine learning models explain why they made a specific decision by turning their data into simple, readable rules. ### Who owns patent US 10824959? Amazon Technologies Inc owns this patent, granted in 2020. ### When does this patent expire? This patent is expected to expire on February 16, 2036, when the invention enters the public domain. ### What is patent US 10824959 cited by? This patent has been cited by 37 later patents that build on its ideas. ### What problem does this patent solve? 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. ### What does this patent NOT cover? Does not cover models that do not use a training set of observation records. **Full plain-English explainer:** https://patentbrief.org/patent/us/10824959/explainers-for-machine-learning-classifiers **Original patent:** https://patents.google.com/patent/US10824959 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Force AI to Follow Logical Rules During Training](https://patentbrief.org/patent/us/11651227/muzero) — A system that uses a dual-headed neural network to ensure AI models obey specific logical rules by embedding those rules directly into the training process. - [How to Automatically Detect and Fix Changes in AI Model Data](https://patentbrief.org/patent/us/10599957/systems-and-methods-for-detecting-data-drift-for-data-used-in-machine-learning-m) — This patent describes a system that automatically notices when the real-world data an AI model sees changes, causing its predictions to become less accurate, and then fixes the model. - [How Cloud Systems Automatically Create and Train AI Data Models](https://patentbrief.org/patent/us/11615208/dall-e-text-to-image-generation) — A cloud-based system that generates fake, privacy-safe data to train AI models, ensuring they remain accurate while protecting sensitive personal information. - [How to Shrink Large AI Models Using Knowledge Distillation](https://patentbrief.org/patent/us/10289962/deep-q-networks-dqn) — A method for teaching small, efficient AI models to mimic the complex decision-making patterns of much larger, more powerful neural networks. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location.