A system for decision support includes a path unit for determining a determined path through a decision tree that leads to a determined recommendation node including a determined recommendation. The decision tree comprises condition nodes and recommendation nodes, wherein a condition node comprises a condition associated with a particular branch of the decision tree. A recommendation node comprises a recommendation associated with the one or more conditions of the one or more condition nodes on a path towards the recommendation node. The path unit is arranged for taking into account the conditions of the condition nodes along the path by applying the conditions to a set of parameters. The system comprises an explanation unit for generating an explanation of a reason for the determined recommendation based on at least one of the condition nodes on the path that leads to the recommendation node.

An information processing apparatus (1) includes a learning unit (32), a calculation unit (33), and a presentation unit (34). The learning unit (32) learns the first model based on predetermined new data acquired from a terminal device (100) possessed by the user and the second model based on joined data obtained by joining shared data stored in advance in the storage unit (4) as additional data with the new data. The calculation unit (33) calculates the improvement degree indicating the degree of improvement in the output precision of the second model to the output of the first model. The presentation unit (34) generates predetermined presentation information based on the improvement degree calculated by the calculation unit (33).

A computerized method for transforming an interactive graphical user interface according to machine learning includes selecting a persona, loading a data structure associated with the selected persona, and generating the interactive graphical user interface. The method includes, in response to a user selecting a first selectable element, inputting a first set of explanatory variables to a first trained machine learning model to generate a first metric, and transforming the user interface according to the selected persona and the first metric. The method includes, in response to the user selecting a second selectable element, inputting a second set of explanatory variables to a second trained machine learning model to generate a second metric, and transforming the user interface according to the selected persona and the second metric. In various implementations, first metric is a first probability of the persona being approved for a first prior authorization prescription.

A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

The techniques herein include using an input context to determine a suggested action and/or cluster. Explanations may also be determined and returned along with the suggested action. The explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. The explanation data may be used to determine whether to perform a suggested action.

Provided is a factor analysis device capable of obtaining more useful knowledge relating to the degree of influence of pieces of data. A factor analysis device according to one embodiment of the present invention is provided with: a classification unit for classifying a type of data into a first group or a second group; and an influence degree calculation unit for calculating, as the degree of influence on target data, the degree of influence of the data of the type classified into the second group on the data of the first group type.

There are provided measures for trust related management of artificial intelligence or machine learning pipelines in relation to the trustworthiness factor “explainability”. Such measures exemplarily comprise, at a first network entity managing artificial intelligence or machine learning trustworthiness in a network, transmitting a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in said network, and receiving a second artificial intelligence or machine learning trustworthiness related message from said second network entity.

A non-transitory computer-readable storage medium storing an explanatory information output program for causing a computer to execute processing includes obtaining a contribution of each of a plurality of factors to an output result of a machine learning model in a case of inputting each of a plurality of pieces of data, each of the plurality of factors being included in each of the plurality of pieces of data; clustering the plurality of pieces of data based on the contribution of each of the plurality of factors to generate a plurality of groups of factors; and outputting explanatory information that includes a diagram representing magnitude of the contribution of each of the plurality of factors to the output result in a case of inputting data included in the group for each of the plurality of groups.

A computer system adapts a model analyzing data. Information sources are analyzed to determine one or more changes for a computerized model employed for analyzing data. One or more current projects each using an implementation of the computerized model with at least one of the determined changes are identified. The implementations are compared to the employed computerized model to determine differences. One or more adaptations for the employed computerized model are determined in response to the determined differences satisfying a threshold, wherein the one or more adaptations for the employed computerized model are based on the determined changes in the corresponding implementation of the computerized model. At least one adaption is installed into a platform hosting the employed model for modification of the employed model. Embodiments of the present invention further include a method and program product for adapting a model analyzing data in substantially the same manner described above.

An apparatus including circuitry configured to determine a probability by combining at least: a probability that an event is present within a current feature of interest given a first set of previous features of interest, and a probability that the event is present within the current feature of interest given a second set of previous features of interest, different to the first set of previous features of interest; circuitry configured to detect the event based on the determined probability; and circuitry configured to control, in dependence on the detection of the event, performance of an action.