Encoding and transmission of knowledge, data and rules for explainable ai

A system for encoding and transmitting knowledge, the system comprising a processor and a memory and configured to implement steps of:

• partitioning a set of data to form a plurality of partitions based on a plurality of features found in the data, wherein each partition includes data with related features, comprising:
• determining a localization trigger for each partition;
• fitting one or more local models to one or more partitions, wherein a local model in the one or more local models corresponds to each partition in the one or more partitions, wherein fitting one or more local models to the one or more partitions comprises providing a local partition input to each partition in the one or more partitions and receiving a local partition output for said partition in the one or more partitions;
• determining, for each partition, an equation specific to said partition, wherein each equation comprises one or more coefficients, wherein each coefficient corresponds to one or more of:
• a level of importance of each feature, a boundary of a feature, a boundary of a partition, possible feature values, feature discontinuity boundaries, feature continuity characteristics, and a transformed feature value, and wherein each equation is configured to produce an answer given a corresponding input based on a set of relevant coefficients among the plurality of coefficients;
• determining an explanation relating to each partition, the explanation comprising information corresponding to the set of relevant coefficients;
• identifying one or more rules for each partition, each rule comprising the localization trigger and the equation;
• and
• generating explanations associated with each rule.

A non-transitory computer-readable medium containing program code that, when executed, causes a processor to perform steps of:

• partitioning a set of data to form a plurality of partitions based on a plurality of features found in the data, wherein each partition includes data with related features, comprising:
• determining a localization trigger for each partition;
• fitting one or more local models to one or more partitions, wherein a local model in the one or more local models corresponds to each partition in the one or more partitions, wherein fitting one or more local models to the one or more partitions comprises providing a local partition input to each partition in the one or more partitions and receiving a local partition output for said partition in the one or more partitions;
• determining, for each partition, an equation specific to said partition, wherein each equation comprises one or more coefficients, wherein each coefficient corresponds to one or more of:
• a level of importance of each feature, a boundary of a feature, a boundary of a partition, possible feature values, feature discontinuity boundaries, feature continuity characteristics, and a transformed feature value, and wherein each equation is configured to produce an answer given a corresponding input based on a set of relevant coefficients among the plurality of coefficients;
• determining an explanation relating to each partition, the explanation comprising information corresponding to the set of relevant coefficients;
• identifying one or more rules for each partition, each rule comprising the localization trigger and the equation;
• and
• generating explanations associated with each rule.