Explainable artificial intelligence

An AI system comprising:

• a knowledge model constructor to generate a knowledge model, the knowledge model comprising a plurality of ontologies and a plurality of inferencing rules for generating an explanation to decision made by the AI system, the knowledge model constructor comprising:
• an ontology creator to create the plurality of ontologies based on data procured from real-world interactions of the knowledge model constructor, the data procured being real-world data, the real-world data comprising structured data, unstructured data, and semi-structured data, wherein the ontology creator is to utilize inductive reasoning techniques for creating the ontology;
• and
• a rule constructor to construct the plurality of inferencing rules based on the ontology and the real-world data procured from the real-world interactions, wherein the rule constructor is to utilize deductive reasoning techniques for creating the plurality of inferencing rules;
• and
• an executor for decision-making when faced with a real-world problem, the executor comprising:
• a knowledge model detector to select the knowledge model to be used for resolving the real-world problem, based on the real-world problem;
• and
• an explainer to execute a decision-making process and to generate an explanation for the decision made, the explainer comprising:
• a classifier and cluster-generator to classify and cluster data procured from the real-world problem into an ontology of the determined knowledge model, wherein the classifier and cluster-generator is to use the inductive reasoning techniques for classifying and clustering data;
• a rule identifier to identify inferencing rules to be used for deconstructing the real-world problem, wherein the rule identifier is to utilize the deductive reasoning techniques for deconstructing the real-world problem;
• and
• an inferencer and hypothesis generator to provide a machine-generated reasoning to provide a hypothesis for the problem and an explanation to accompany the hypothesis, wherein the inferencer and hypothesis generator is to utilize abductive reasoning techniques for providing the hypothesis and the explanation.

A non-transitory computer readable medium including machine readable instructions that are executable by a processor to:

• generate a knowledge model, the knowledge model comprising a plurality of ontologies and a plurality of inferencing rules for generating an explanation to decision made by an AI system, wherein generating the ontology comprises creating the plurality of ontologies based on data procured from real-world interactions using inductive reasoning techniques for creating the ontology, and creating the plurality of inferencing rules based on the ontology and the data procured from the real-world interactions using deductive reasoning techniques for creating the plurality of inferencing rules;
• determine the knowledge model to be used for resolving a real-world problem, based on the real-world problem;
• classify and cluster data procured from the real-world problem into an ontology of the determined knowledge model using the inductive reasoning techniques for classifying and clustering data;
• identify inferencing rules to be used for deconstructing the real-world problem using deductive reasoning techniques for deconstructing the real-world problem;
• and
• provide a machine-learning based reasoning to provide a hypothesis for the problem and an explanation to accompany the hypothesis using abductive reasoning techniques for providing the hypothesis and the explanation.