Methods, non-transitory computer readable media, and causal explanation computing apparatus that assists with generating and providing causal explanation of artificial intelligence models includes obtaining a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation. Next, a plurality of first factors from the disentangled low-dimensional representation of the obtained data that affect an output of the artificial intelligence model is identified. Further, a generative mapping from the disentangled low-dimensional representation between the identified plurality of first factors and the output of the artificial intelligence model, using causal reasoning is determined. An explanation data is generated using the determined generative mapping, wherein the generated explanation data provides a description of an operation leading to the output of the artificial intelligence model using the identified plurality of first factors. The generated explanation data is provided via a graphical user.

Methods, non-transitory computer readable media, and causal explanation computing apparatus that assists with generating and providing causal explanation of artificial intelligence models includes obtaining a dataset as an input for an artificial intelligence model, wherein the obtained dataset is filtered to a disentangled low-dimensional representation. Next, a plurality of first factors from the disentangled low-dimensional representation of the obtained data that affect an output of the artificial intelligence model is identified. Further, a generative mapping from the disentangled low-dimensional representation between the identified plurality of first factors and the output of the artificial intelligence model, using causal reasoning is determined. An explanation data is generated using the determined generative mapping, wherein the generated explanation data provides a description of an operation leading to the output of the artificial intelligence model using the identified plurality of first factors. The generated explanation data is provided via a graphical user.

A method and device for presenting a prediction model, and a method and device for adjusting a prediction model. The method for presenting a prediction model includes: obtaining at least one prediction result of a prediction model for at least one prediction sample; obtaining at least one decision-making tree training sample for training a decision-making tree model according to the at least one prediction sample and the at least one prediction result, the decision-making tree model being used for fitting the prediction model; training the decision-making tree model by using at least one decision-making tree training sample; and visually presenting the trained decision-making tree model. By means of the method, a prediction model hard to understand can be approximated to a decision-making tree model, and the approximated decision-making tree model is presented, so that a user better understands the prediction model according to the presented decision-making tree model.

A control method includes obtaining feature data using at least one sensor, the feature data being acquired by the terminal using the at least one sensor, generating an action instruction based on the feature data and a decision-making mechanism of the terminal, and executing the action instruction. In this application, various aspects of feature data are acquired using a plurality of sensors, data analysis is performed on the feature data, and a corresponding action instruction is then generated based on a corresponding decision-making mechanism to implement interactive control.

An outlier detection mechanism is disclosed that improves transparency and explainability in machine learning models. The outlier detection mechanism can quantify, at prediction time, how a new observation differs from training observations. The outlier detection mechanism can also provide a way to aggregate outputs from decision trees by weighting the outputs of the decision trees based on their explainability.

An outlier detection mechanism is disclosed that improves transparency and explainability in machine learning models. The outlier detection mechanism can quantify, at prediction time, how a new observation differs from training observations. The outlier detection mechanism can also provide a way to aggregate outputs from decision trees by weighting the outputs of the decision trees based on their explainability.

User interactions with a supply chain system are monitored based on a tracked ontology enrichment process, an explainable reasoning graph is constructed based on the monitored user interactions and domain specific reasoning information; and an explainable insight of the monitored user interactions is learned, as is a user interaction embedding for an embedding space, based on the constructed explainable reasoning graph and the explainable insight. External data is incorporated into the embedding space, a joint embedding is learned based on the user interaction embedding, and missing entities and relationships are identified for incorporation into an ontology based on the user interactions and joint embedding. The ontology is revised to incorporate the missing entities and relationships into the ontology to create a revised ontology, and a supply chain is controlled based on the revised ontology.

User interactions with a supply chain system are monitored based on a tracked ontology enrichment process, an explainable reasoning graph is constructed based on the monitored user interactions and domain specific reasoning information; and an explainable insight of the monitored user interactions is learned, as is a user interaction embedding for an embedding space, based on the constructed explainable reasoning graph and the explainable insight. External data is incorporated into the embedding space, a joint embedding is learned based on the user interaction embedding, and missing entities and relationships are identified for incorporation into an ontology based on the user interactions and joint embedding. The ontology is revised to incorporate the missing entities and relationships into the ontology to create a revised ontology, and a supply chain is controlled based on the revised ontology.

The present disclosure relates to systems, methods, and computer readable media that evaluate performance of a machine learning system in connection with a test dataset. For example, systems disclosed herein may receive a test dataset and identify label information for the test dataset including feature information and ground truth data. The systems disclosed herein can compare the ground truth data and outputs generated by a machine learning system to evaluate performance of the machine learning system with respect to the test dataset. The systems disclosed herein may further generate feature clusters based on failed outputs and corresponding features and generate a number of performance views that illustrate performance of the machine learning system with respect to clustered groupings of the test dataset.

Systems, computer-implemented methods, and computer program products that can facilitate sensitive data policy recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an extraction component that can employ an artificial intelligence model to extract compliance data from a data source. The computer executable components can further comprise a recommendation component that can recommend a sensitive data policy based on the compliance data. In some embodiments, the recommendation component can further identify one or more sensitive data entities of a sensitive data dataset that are affected by actionable obligation data of the data source.