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 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:
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:
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:
A computer system stores a large-scale language model that receives an instruction sentence as an input and outputs an interpretation sentence for interpreting a result of an inference, and text template information that stores template data in which a characteristic of a contribution value of a feature in a group having features is associated with a template of the instruction sentence, calculates the contribution value of each of a plurality of the features, generates a plurality of groups each constituted with one or more of the features, acquires, for each of the plurality of groups, the template data by referring to the text template information based on the characteristic of the contribution value of the feature included in the group, generates, based on the template data and the feature included in the group, the instruction sentence to be input to the large-scale language model, and outputs the interpretation sentence obtained.
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:
An object of the invention is to provide a simulation system and a simulation method which are capable of efficiently presenting a simulation result which is valuable to a user. The invention achieves the above-mentioned object by performing simulation, displaying a plurality of simulation results as samples, receiving an input of information on a user's evaluation with respect to each of the displayed results by a user interface, and outputting a group of simulation results on the basis of the input information.
Abstract:
A decision-making support system which is a client-server system comprising: multiple servers; a client having a display; a network; and a database. On the basis of data acquisition conditions supplied via the client the multiple servers acquire from the data base on multiple distributed processing platforms, a first data group spanning from the past to the present, and generate a first network graph for the time from the past to the present. The multiple servers also execute multiple simulations based on the first data group, on the basis of provided simulation conditions, and generate second and third network graphs for a time not included in the first data group or for the future. The client receives the results of the generation of these network graphs and displays on the display the first through third network graphs spanning from the past to the present, and to the future, thereby providing the user with a scenario map.