Abstract: By using a time-series causal model stored in a time-series causal model storage unit and time-series data of an analysis target, a contribution degree restoration rate is calculated by evaluating how much a contribution degree of each time-series variable at each time is required to be restored to the contribution degree of another time-series variable at another time. Further, the contribution degree of each time-series variable at each time is restored, based on the calculated contribution degree restoration rate, to the contribution degree of the other time-series variable at the other time, and the transition of the contribution degree by a root factor with respect to an objective variable is calculated. Additionally, the contribution degree by the root factor with respect to the calculated objective variable is output.

Abstract: A teacher data editing support system includes a determination unit that receives teacher data including sensitive attribution as a variable that potentially causes discrimination, a feature as a variable to be used for prediction, and a correct answer, and calculates contribution as an index indicating contribution of the sensitive attribution to the correct answer, a display unit that visually presents evaluation information indicating a relationship between a level of changing the correct answer in the teacher data and a level of deviation of the correct answer from an initial value or a discrimination level based on the contribution, and an editing unit that accepts designation of how much the correct answer is changed, changes the correct answer in the teacher data in response to the designation, and outputs the changed teacher data.

Abstract: It is possible to effectively and efficiently add training data. A training data evaluation system includes an uncertainty calculation unit configured to calculate, based on a prediction value obtained from a machine learning model using evaluation data for evaluating a shortage of training data as an input and a correct answer for the evaluation data, a data shortage degree representing a training data shortage degree for each piece of the evaluation data; a target selection unit configured to extract target data, which is data to be added to the training data, based on a predetermined selection rule and the data shortage degree; and a tendency analysis planning unit configured to specify a tendency of the target data based on a predetermined analysis rule and specify a property of the training data to be added based on the tendency of the target data.

Abstract: A computer system is accessibly connected to model management information for managing model data, risk assessment management information for managing risk assessment data, and evaluation method management information for managing evaluation method data, and generates, as relation data, association of, model data, risk assessment data, and evaluation method data in a template. The computer system is configured to, when receiving an evaluation request, by referring to the model management information, search for model data of a model to be evaluated, search for the relation data associated with the model data, generate a template based on the relation data, store, in association with the relation data, an evaluation result based on an evaluation method corresponding to the evaluation method data associated with the relation data, and generate a report based on the template and the evaluation result.

Abstract: For a machine learning model that receives control parameters of a semiconductor processing device and outputs shape parameters that express a processed shape of a semiconductor sample processed by the semiconductor processing device, an experiment point obtaining learning data is recommended. A contribution of each control parameter to the prediction of the machine learning model is evaluated from feature quantity data that is a value of a control parameter of the learning data used for learning of the machine learning model, and the experiment point is recommended based on a stability evaluation and an uncertainty evaluation of the prediction by the machine learning model in a space defined by the control parameters selected based on the contribution as axes.

Abstract: A predictor creation device including a processor configured to execute a program and a storage device that stores the program acquires a calibration target ensemble predictor obtained by combining a plurality of predictors based on a training data set which is a combination of training data and ground truth data, calculates a prediction basis characteristic related to a feature of the training data for each of the plurality of predictors, acquires an expected prediction basis characteristic related to the feature based on the prediction basis characteristic related to the feature as a result of outputting the prediction basis characteristic related to the calculated feature, determines a combination coefficient of each of the plurality of predictors based on the prediction basis characteristic related to the feature and the expected prediction basis characteristic related to the feature, and calibrates the calibration target ensemble predictor based on the combination coefficient.

Abstract: Provided is a technique that allows a user to easily determine what kind of future scenario AI is outputting. A preferred aspect of the invention provides an information processing device including: an agent configured to output a response based on a state observed from an environment with stochastic state transitions; an individual evaluation model configured to evaluate the response assuming that a part of the stochastic state transitions occurs; and a plan explanation processing unit configured to output information based on the evaluation in association with information based on the response.

Abstract: A generation apparatus is configured to access a set of pieces of learning data each being a combination of a value of an explanatory variable and a value of an objective variable, a function family list including, of functions each indicating a physical law and an attribute of each of the functions, at least the functions, and search range limiting information for limiting a search range of the function family list, wherein the processor is configured to execute: first generation processing of generating a first prediction expression by setting a first parameter for the explanatory variable to a first function included in the function family list; first calculation processing of calculating, based on the search range limiting information, a first conviction degree relating to the first prediction expression; and first output processing of outputting the first prediction expression and the first conviction degree.

Abstract: To provide an action evaluation system capable of more efficiently planning a new action based on an execution result of a test for limited action conditions. A condition change tracking unit determines, based on execution result information obtained by executing a test related to an action for an action target having a predetermined action condition, a condition change which is a change in the action condition before and after the test. A result verification unit calculates, based on the execution result information, an evaluation value obtained by evaluating an effect of the action. A reward distribution unit calculates a change contribution degree which is a degree of contribution of the condition change to the evaluation value.

Abstract: A computer system accurately selects learning data for improving a prediction accuracy of a predictor, and is connected to a database that stores a plurality of pieces of learning data and information for managing a plurality of predictors generated under different learning conditions. A target predictor is selected, an influence degree representing strength of an influence of the learning data on a prediction accuracy of the target predictor for test data is calculated for each of a plurality of pieces of test data, an influence score of the learning data is calculated for the plurality of predictors based on a plurality of influence degrees of the learning data associated with the predictors, and the learning data to be used is selected from the plurality of pieces of learning data on the basis of a plurality of the influence scores of each of the plurality of pieces of learning data.

Abstract: There is provided is a computer system that outputs a predicted value of data to be evaluated using a predictor generated using learning data. The computer system includes the predictor; an index calculation unit that calculates an interpretation index of the data to be evaluated; and an extraction unit that selects the learning data useful for a user to interpret the predicted value of the data to be evaluated, wherein index management information for managing an interpretation index of the learning data is stored, the index calculation unit calculates the interpretation index of the data to be evaluated, and the extraction unit calculates a selection index based on the interpretation index of the data to be evaluated and the interpretation index of the learning data, selects the learning data based on the selection index, and outputs display information for presenting information indicating a processing result.

Abstract: Provided is a computer system to present information useful for achieving purposes related to an object by utilizing AI prediction. The computer system manages a prediction model for predicting an object event based on evaluation data and feature profiling database that defines a change rule of each of the plurality of feature values included in the evaluation data, generates change policy data by changing the plurality of feature values included in the evaluation data based on the feature profiling database, calculates an evaluation value indicating effectiveness of the change policy data, and generates display data for presenting the change policy data and the evaluation value as information useful for achieving purposes related to the object.

Abstract: A plan evaluation apparatus, which evaluates a schedule planned by combining a plurality of plans, includes: a feature conversion unit that divides the schedule into plan components based on a predetermined conversion rule, and convert the divided plan components into features; a model learning unit that uses the features as an input and creates a machine learning model having a key performance indicator (KPI) of the schedule as an objective variable; a contribution rate calculation unit that calculates a contribution rate of each of the features with respect to the machine learning model; and an influence degree calculation unit that calculates an influence degree of influence, on the KPI of the schedule, of the plan component which is a conversion source of the feature, based on the contribution rate of the feature.

Abstract: Fairness of automated assessments of AI has become a significant issue in recent years, and techniques for adjusting assessment results are being sought. This information processing system is configured to include: a first predicting unit which outputs an assessed value from input information that does not include sensitive attribute information a user has decided is not to be input; a second predicting unit which has been trained in advance, using teacher data, to estimate the sensitive attribute information that the user has decided is not to be input, and which estimates the sensitive attribute information from the input information that does not include the sensitive attribute information; and a first quantizing unit which, on the basis of the estimated value of the sensitive attribute information obtained from the second predicting unit, adjusts the assessed value output by the first predicting unit, and outputs an assessment result.

Abstract: A computer system stores interpretation factor conversion information for managing an interpretation factor interpreting a basis of a prediction result for input data, the interpretation factor is determined by a value of each of a plurality of feature quantities contained in the input data including values of the plurality of feature quantities, and a first evaluation value of each of the plurality of feature quantities contained in the input data. When evaluation target data is input, the computer system calculates a prediction result, calculates a contribution value of each of the plurality feature quantities contained in the evaluation target data, specifies a corresponding interpretation factor, based on a value and a contribution value of each of the plurality of feature quantities contained in the evaluation target data, by referring to the interpretation factor conversion information, and generates and outputs display information for presenting the specified interpretation factor.

Abstract: A computer has: a testing unit obtaining attribute values of attributes from an object to which a measure is implemented and an object to which the measure is not implemented, and calculating a change amount of the attribute values of the attributes due to the implementation of the measure and a test indicator for determining a significant difference of the change amount of the attribute values of the attributes due to the implementation of the measure; an experimental rule updating unit calculating a cumulative change amount indicating a temporal change amount of the attribute values of the attributes and a cumulative test indicator indicating a temporal test indicator of the attributes on the basis of a test result output from the testing unit, and accumulating data in which the identification information of the measure and the cumulative change amount and the cumulative test indicator are associated; and a display unit.

Abstract: In classification problems or regression problems, a prediction rule that is highly accurate, simple, and in match with the knowledge of experts is obtained. A system includes a prediction rule simplification unit that simplifies a prediction rule of a learning model using an evaluation metric and a restriction; a branch condition search unit that updates a part of the simplified branch condition for prediction rule based on calibration information expressing a request for a prediction value or a specific branch condition; and a threshold optimization unit that updates a part of a threshold of the simplified prediction rule based on the calibration information.

Abstract: An information processing system includes a predictor, a contribution calculator, and a supplemental base generator. The system accesses databases that store relevance between feature variables in case data and a contribution of a feature variable in the case data to a result of prediction. The contribution calculator calculates the contribution of each of the feature variables in the evaluation target data to the output of the predictor, and outputs the calculated contributions and the acquired evaluation target data. The supplemental reason generator extracts a group of data proximate to the value and the contribution of a first feature variable, identifies a second feature variable relevant to the first feature variable, generates supplemental reason data based on a distribution of the proximate data group within a distribution of the second feature variable by use of the case data, and outputs the generated supplemental reason data.

Abstract: A computer system includes a calculation unit for extracting specific reference data from a plurality of reference data, configured to calculate a contribution of the each feature amount of explanatory data regarding a predicted value using the specific piece of reference data, the explanatory data, and a predictor, and stores the contribution that has been calculated as a pair contribution in association with the specific piece of the reference data and the explanatory data, the pair contribution being a contribution that has been calculated with the one piece of the reference data and the explanatory data being a pair, for all pairs including each reference data and the explanatory data; and an aggregation unit for reading the pair contribution that has been calculated for the each feature amount of the explanatory data, and configured to calculate by aggregating the contribution of the each feature amount of the explanatory data.

Abstract: To provide a walking mode display method, a walking mode display system and a walking mode analyzer each of which allows for analysis of a walking mode of a user and display thereof in an easily understandable manner. The walking mode display method includes: selecting measurement of a walker and measurement of a reference walker to be compared with the walker; displaying a first walking model that displays a walking for one walking step of the walker as an animation; displaying a second walking model that displays a walking for one walking step of the reference walker as the animation; and displaying a magnitude of predetermined feature amount data related to the measurement of the walker and the magnitude of predetermined feature amount data related to the measurement of the reference walker in a comparable manner.