Abstract: Techniques regarding the governing of use by a consumer of an artificial intelligence technology are described. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include an analyzing component that can, based on an artificial intelligence usage policy, analyze a proposed use by a consumer of an artificial intelligence technology, resulting in an analyzed use of the artificial intelligence technology. The computer executable components can further include a governing component that can, based on the analyzing, govern use by the consumer of the artificial intelligence technology.

Abstract: Techniques facilitating using flow graphs to represent a data analysis program in a cloud based system for open science collaboration and discovery are provided. In an example, a system can represent a data analysis execution as a flow graph where vertices of the flow graph represent function calls made during the data analysis program and edges between the vertices represent objects passed between the functions. In another example, the flow graph can then be annotated using an annotation database to label the recognized function calls and objects. In another example, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program.

Abstract: Techniques for generating and searching semantic flow graphs are provided that include creating, by a system operatively coupled to a processor employing a semantic flow graph creation process, a semantic flow graph based on an ontology associated with a set of subjects and a raw flow graph determined from an analysis of a data set relating to the set of subjects and searching, by the system, the semantic flow graph to determine a subset of information of the semantic flow graph that is responsive to a query based on the query and information of the semantic flow graph.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: Techniques for filtering artificial intelligence (AI)-designed molecules for laboratory testing provided. According to an embodiment, computer implemented method can comprise selecting, by a system operatively coupled to a processor, a first subset of AI-designed molecules from a set of AI-designed molecules as candidate pharmaceutical agents based on classification of the AI-designed molecules using one or more classifiers. The method further comprises selecting, by the system, a second subset of the candidate pharmaceutical agents for wet laboratory testing based on evaluation of molecular interactions between the candidate pharmaceutical agents and one or more biological targets using one or more computer simulations.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: Techniques facilitating using flow graphs to represent a data analysis program in a cloud based system for open science collaboration and discovery are provided. In an example, a system can represent a data analysis execution as a flow graph where vertices of the flow graph represent function calls made during the data analysis program and edges between the vertices represent objects passed between the functions. In another example, the flow graph can then be annotated using an annotation database to label the recognized function calls and objects. In another example, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program.

Abstract: Techniques facilitating using flow graphs to represent a data analysis program in a cloud based system for open science collaboration and discovery are provided. In an example, a system can represent a data analysis execution as a flow graph where vertices of the flow graph represent function calls made during the data analysis program and edges between the vertices represent objects passed between the functions. In another example, the flow graph can then be annotated using an annotation database to label the recognized function calls and objects. In another example, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program.

Abstract: Techniques facilitating using flow graphs to represent a data analysis program in a cloud based system for open science collaboration and discovery are provided. In an example, a system can represent a data analysis execution as a flow graph where vertices of the flow graph represent function calls made during the data analysis program and edges between the vertices represent objects passed between the functions. In another example, the flow graph can then be annotated using an annotation database to label the recognized function calls and objects. In another example, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate computational creativity are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a learner component that learns mappings of data features from a feature space to a creativity attribute of a model to define a creativity control function of the model. The computer executable components can further comprise a generator component that employs the model to generate a creative data sample based on the creativity control function.

Abstract: A bias detection method, system, and computer program product to evaluate bias in an artificial intelligence service include selecting a bias context, the bias context having a bias specification associated with the bias context, generating test data for determining the bias in the artificial intelligence service based on the bias specification and the bias context, and testing the artificial intelligence service for the bias with the generated test data.

Abstract: A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.

Abstract: Techniques for generating and searching semantic flow graphs are provided that include creating, by a system operatively coupled to a processor employing a semantic flow graph creation process, a semantic flow graph based on an ontology associated with a set of subjects and a raw flow graph determined from an analysis of a data set relating to the set of subjects and searching, by the system, the semantic flow graph to determine a subset of information of the semantic flow graph that is responsive to a query based on the query and information of the semantic flow graph.

Abstract: Methods and systems for generating prediction data are described. In an example, a processor may retrieve preferential data from a memory. The preferential data may include a set of preferences that corresponds to a first subset of objects among a set of objects, and may exclude preferences associated with a second subset of objects among the set of objects. Each preference may indicate a preferred object between a respective pair of objects among the first subset of objects. The processor may determine first predicted ratings corresponding to the first subset of objects based on the preferential data. The processor may determine second predicted ratings corresponding to the second subset of objects based on the preferential data. The processor may generate prediction data by populating entries of the prediction data with the first and second predicted ratings, where the prediction data may include predicted ratings of the set of objects.

Abstract: A method includes performing contextual association of entities using multi-source data. For each context the method performs co-clustering to identify distinct expert-skill associations; constructing single-entity unipartite graph representations and performing a random walk within each single-entity unipartite graph; for each single-entity unipartite graph, obtaining steady state distributions using the random walks to obtain clusters of experts and skills; performing a weighted two-way random walk across entity graphs (graph edges), giving preference to traversal within members of the same co-cluster; and performing link prediction for each context by dynamically adding edges, and obtaining overall skills predictions, analyses and inferences by merging the contexts and weighting the links of each context.

Abstract: A computer-implemented method includes generating a raw flow graph based on execution of a program. A semantic flow graph is generated based on the raw flow graph and based on an ontology in a subject matter-domain associated with the program. The semantic flow graph illustrates one or more semantic concepts of the program.

Abstract: A bias detection method, system, and computer program product to evaluate bias in an artificial intelligence service include selecting a bias context, the bias context having a bias specification associated with the bias context, generating test data for determining the bias in the artificial intelligence service based on the bias specification and the bias context, and testing the artificial intelligence service for the bias with the generated test data.

Abstract: A computer-implemented method includes generating a raw flow graph based on execution of a program. A semantic flow graph is generated based on the raw flow graph and based on an ontology in a subject matter-domain associated with the program. The semantic flow graph illustrates one or more semantic concepts of the program.

Abstract: Methods and systems for generating prediction data are described. In an example, a processor may retrieve preferential data from a memory. The preferential data may include a set of preferences that corresponds to a first subset of objects among a set of objects, and may exclude preferences associated with a second subset of objects among the set of objects. Each preference may indicate a preferred object between a respective pair of objects among the first subset of objects. The processor may determine first predicted ratings corresponding to the first subset of objects based on the preferential data. The processor may determine second predicted ratings corresponding to the second subset of objects based on the preferential data. The processor may generate prediction data by populating entries of the prediction data with the first and second predicted ratings, where the prediction data may include predicted ratings of the set of objects.

Abstract: A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.