# 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. - [How a System Finds and Creates Machine Learning Models](https://patentbrief.org/patent/us/12340293/machine-learning-model-repository-management-and-search-engine) — This patent describes a system that helps users find existing machine learning models or algorithms for a specific task and, if needed, automatically trains a new model using a selected algorithm.