{
  "patent_number": "US 10824959",
  "country": "US",
  "title": "How to Make Artificial Intelligence Explain Its Own Decisions",
  "original_title": "Explainers for machine learning classifiers",
  "summary": "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_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."
  ],
  "filed": "2016-02-16",
  "granted": "2020-11-03",
  "expires": "2036-02-16",
  "status": "active",
  "holder": "Amazon Technologies Inc",
  "holder_url": "https://patentbrief.org/company/amazon-technologies-inc",
  "inventors": [
    {
      "name": "Srinivasan Sengamedu Hanumantha Rao",
      "url": "https://patentbrief.org/inventor/srinivasan-sengamedu-hanumantha-rao"
    },
    {
      "name": "Bibaswan Kumar Chatterjee",
      "url": "https://patentbrief.org/inventor/bibaswan-kumar-chatterjee"
    }
  ],
  "times_cited": 37,
  "tags": [
    "ai_ml",
    "software",
    "consumer_electronics",
    "finance"
  ],
  "abstract": "A transformed data set corresponding to a machine learning classifier's training data set is generated. Each transformed record contains a modified version of a corresponding training record, as well as the prediction made for the training record by the classifier. A set of explanatory rules is minded from the transformed data set, with each rule indicating a relationship between the prediction and one or more features corresponding to the training records. From among the rule set, a particular matching rule is selected to provide an easy-to-understand explanation for a prediction made by the classifier for an observation record which is not part of the training set.",
  "url": "https://patentbrief.org/patent/us/10824959/explainers-for-machine-learning-classifiers",
  "markdown_url": "https://patentbrief.org/patent/us/10824959/explainers-for-machine-learning-classifiers/md",
  "google_patents_url": "https://patents.google.com/patent/US10824959",
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}