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US 12,591,795 B2Granted
Method for providing explainable artificial intelligence
AssigneeSamsung Sds Co., Ltd.
InventorsYoung Jun Kim, Jae Sun Shin
Life of this patent
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Abstract
A method performed on a computing device for providing explainable artificial intelligence (XAI) according to an embodiment of the present disclosure includes configuring a first set of prototypes representing each data instance of an entire dataset input to a machine-learned model, configuring a second set of criticisms that are samples of data instances not expressed by the prototypes among each data instance of the entire dataset, and calculating a first feature based rationale of output data for a first data instance of the model by considering all prototypes included in the first set and all criticisms included in the second set.
Claims10
A method performed on a computing device for providing explainable artificial intelligence (XAI), the method comprising:
- receive, from a user terminal, a rationale data request;
- inputting, based on the rationale data request, an entire dataset into a machine-learned model;
- configuring a first set of prototypes representing each instance of data instances of the entire dataset input to the machine-learned model;
- configuring a second set of criticisms that are samples of other data instances not expressed by the first set of prototypes from among the data instances of the entire dataset;
- calculating a first feature based rationale of output data for a first data instance of the machine-learned model by considering all prototypes included in the first set of prototypes and all criticisms included in the second set of criticisms;
- generating determination result content for the first data instance by using the output data for the first data instance by the machine-learned model;
- generating rationale content for the determination result content by using the first feature based rationale of the output data, wherein the rationale content indicates at least one top feature having a first influence on the determination result content and at least one low feature having a second influence on the determination result content lower than the first influence, the at least one top feature being selected from a top feature of a list of first feature based rationales in descending order of rationale values of all features of the entire dataset, and the at least one low feature being selected from a bottom feature of the list of first feature based rationales in descending order of rationale values of all features of the entire dataset;
- generating output data to be output in an output screen of the user terminal, the output data including the determination result content and the rationale content;
- and
- providing, based on the rationale data request, the output data to the user terminal.
7 more elements
An apparatus for providing explainable artificial intelligence, the apparatus comprising:
- one or more processors;
- a memory for loading a computer program executed by the one or more processors and data of a machine-learned model;
- and
- a storage for storing the computer program;
- wherein the computer program, when executed by the one or more processors, cause the apparatus to:
- receive, from a user terminal, a rationale data request;
- input, based on the rationale data request, an entire dataset into the machined-learned model;
- configure a first set of prototypes representing each instance of data instances of the entire dataset input to the machine-learned model;
- configure a second set of criticisms that are samples of other data instances not expressed by the first set of prototypes from among the data instances of the entire dataset;
- calculate a first feature based rationale for output data for a first data instance of the machine-learned model by considering all prototypes included in the first set of prototypes and all criticisms included in the second set of criticisms;
- generate determination result content for the first data instance by using the output data for the first data instance by the machine-learned model;
- generate rationale content for the determination result content by using the first feature based rationale of the output data, wherein the rationale content indicates at least one top feature having a first influence on the determination result content exceeding a first threshold and at least one low feature having a second influence on the determination result content lower than the first influence, the at least one top feature being selected from a top feature of a list of first feature based rationales in descending order of rationale values of all features of the entire dataset, and the at least one low feature being selected from a bottom feature of the list of first feature based rationales in descending order of rationale values of all features of the entire dataset;
- generate output data to be output in an output screen of the user terminal, the output data including the determination result content and the rationale content;
- and
- provide, based on the rationale data request, the output data to the user terminal.
12 more elements
Dependencies are inferred from the claim text.