Abstract: An example operation may include one or more of identifying an external system that passes an input attribute to a process based on a workflow representation of the process, building a simulator of the external system based on attributes of the external system identified from the workflow representation, simulating future values of the input attribute to be passed to the process by the external system based on the simulator of the external system and a previous simulation run of the process performed via a workflow software application, and executing a new simulation of the process via the workflow software application based on the simulated future values of the input attribute.

Abstract: A method, computer program, and computer system are provided for online fairness monitoring. A dataset having one or more entries with one or more protected attributes and data corresponding to a trained machine learning model is received. An entry having a maximum reward is selected based on a reward probability associated with the entry. A determination is made as to whether bias has developed in the trained machine learning model toward one or more of the one or more protected attributes based on a change to the reward probability or a distribution of reward probabilities exceeding a threshold value.

Abstract: A method, computer program product, and a system where a processor(s) generates a digital wardrobe for each user of a set of users. The processor(s) obtains a prospective theme(s) for a given event and a list of participants comprising a portion of the set of users. The processor(s) identify preferences, in the digital wardrobes of the portion of the set of users, relevant to each of the one or more prospective themes for the given event. The processor(s) determine, based on analyzing the relevant preferences in the digital wardrobes of the portion of the set of users, if a consensus exists in the relevant preferences of the portion of the set of users, where the consensus represents a given prospective theme, where the respective relevant preferences for the given prospective theme are aligned across the portion of the set of users. Based on determining that the consensus exists, the processor(s) generates an event theme for the given event, wherein the event theme is the consensus.

Abstract: One embodiment provides a method, including: receiving, at a central system, a query requesting access to a dataset, wherein the central system communicates with a plurality of data storage locations, each having a governance policy for data stored at the data storage location, wherein different portions of the dataset are stored within different of the plurality of data storage locations; sending a sub-query formulated based upon the query; receiving a governance enforcement actions listing corresponding to the portion of the dataset stored within the corresponding data storage location; generating a meta-policy of enforcement actions for all of the plurality of data storage locations storing portions of the dataset, wherein the meta-policy identifies enforcement actions and an order of the enforcement actions to be applied to the dataset; and providing the meta-policy to each of the plurality of data storage locations.

Abstract: An example system includes a processor to receive a data set. The processor can generate a data slice rule based on a data observation for a data point in the data set. The processor can generate an instance of data based on the generated data slice rule.

Abstract: An example system includes a processor to receive a test set, data slices, and a measure of interest. The processor can rank the data slices based on the test set, the data slices, and the set of measures of interest. The test set includes data points from the same feature space used to train a machine learning model. Each data slice is ranked according to generated slice grades representing unique information contribution of each data slice to the measure of interest with respect to the other data slices. The processor can then present the ranked data slices.

Abstract: Methods, systems, and computer program products for automated fairness-driven graph node label classification are provided herein. A computer-implemented method includes obtaining at least one input graph; predicting one or more node labels associated with the at least one input graph by processing at least a portion of the at least one input graph using a graph node label prediction model, wherein the graph node label prediction model includes at least one loss function; generating an updated version of the graph node label prediction model based at least in part on the one or more predicted node labels and one or more group fairness-based constraints relevant to the at least one input graph; and performing one or more automated actions using the updated version of the graph node label prediction model.

Abstract: Methods, systems, and computer program products for privacy-preserving class label standardization in federated learning settings are provided herein. A computer-implemented method includes determining, using one or more data privacy-preserving techniques, a signature for each of one or more classes of data for each of multiple client devices within a federated learning environment; identifying one or more signature matches across at least a portion of the multiple client devices; generating one or more class labels for the one or more classes of data associated with the one or more signature matches; labeling, across the at least a portion of the multiple client devices, the one or more classes of data associated with the one or more signature matches with the one or more generated class labels; and performing one or more automated actions based at least in part on the one or more labeled classes of data.

Abstract: Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.

Abstract: Methods, systems, and computer program products for federated machine learning based on partially secured spatio-temporal data are provided herein. A computer-implemented method includes obtaining temporal data from a plurality of distributed client devices in conjunction with a federated machine learning process, wherein at least a portion of the data comprises encoded private data and at least a portion of the data is public data; generating a spatio-temporal graph comprising nodes representing the plurality of distributed client devices, wherein the generating comprises identifying at least one pair of similar nodes based at least in part on the public data and adding an edge to the spatio-temporal graph between the pair of similar nodes; and aligning encoders of at least two of the distributed client devices based on the spatio-temporal graph.

Abstract: A method for predicting the behavior of an electronic social network (ESN) includes identifying one user's connections with other users and creating a data structure in a memory that represents the users and their connections in the ESN. A plurality of data sources for electronic communications between users are analyzed and assigned a relative importance value. A weight is also assigned to each of the connections between the users. The weight is an encoded value computed based on a link structure of the connections where the link structure includes metadata indicating a category and a status of the respective connection. The probability that one user will communicate with one of the other users is calculated based on the analyzed plurality of data sources calculating, and the user's connections with respect to other users are ranked based on the calculated probabilities.

Abstract: One embodiment provides a method, including: receiving, at a central server, data from each of a plurality of data sources, the plurality of data sources being within a plurality of data storage locations, wherein the central server includes a validation dataset having a plurality of annotated datapoints; computing, at the central server, an influential score for each of the plurality of data sources based upon the data provided to the central server from each of the plurality of data sources, wherein an influential score of a data source identifies an influence of the data source in accurately predicting annotations of the validation dataset; selecting, at the central server and based upon the influential score of the plurality of data sources, a subset of the plurality of data sources; and generating, at the central server, the training dataset utilizing the data of the data sources included within the subset.

Abstract: A system, method, and computer program product for implementing automated digital agent communication and control is provided. The method includes retrieving from a digital agent, a query associated with knowledge based control process. Digital knowledge elements, associated digital skills, and a sequence of control operations are received to obtain a response to the query. A first possible set of knowledge of a set of digital knowledge elements, skills, and an associated sequence of operation are selected and the first possible set of knowledge, skills, and associated sequence of operation are transmitted to the digital agent. A sequence of skills are executed with respect to digital knowledge elements and components and a hardware interface device is enabled to interact with and control various devices for enabling operational functionality associated with devices. Knowledge based fabric code associated with future instances of enabling the hardware interface device is updated.

Abstract: One embodiment provides a method, including: receiving, at a central system, a query requesting access to a dataset, wherein the central system communicates with a plurality of data storage locations, each having a governance policy for data stored at the data storage location, wherein different portions of the dataset are stored within different of the plurality of data storage locations; sending a sub-query formulated based upon the query; receiving a governance enforcement actions listing corresponding to the portion of the dataset stored within the corresponding data storage location; generating a meta-policy of enforcement actions for all of the plurality of data storage locations storing portions of the dataset, wherein the meta-policy identifies enforcement actions and an order of the enforcement actions to be applied to the dataset; and providing the meta-policy to each of the plurality of data storage locations.

Abstract: Methods, systems, and computer program products for automatically detecting outliers in federated data are provided herein. A computer-implemented method includes obtaining local outlier-related data from multiple client systems within a federated learning environment; detecting one or more federated learning environment-level outliers from at least a portion of the multiple client systems by processing at least a portion of the obtained local outlier-related data using one or more artificial intelligence models; determining at least one calibration parameter for detecting federated learning environment-level outliers based at least in part on the one or more detected federated learning environment-level outliers; and outputting the at least one determined calibration parameter to at least a portion of the multiple client systems within the federated learning environment.

Abstract: Methods, systems, and computer program products for federated data standardization using data privacy techniques are provided herein. A computer-implemented method includes obtaining multiple datasets from multiple clients in accordance with one or more data privacy techniques; determining one or more similar data columns across at least a portion of the multiple datasets; generating one or more column labels for the one or more similar data columns; standardizing at least a portion of data within the one or more similar data columns by processing the one or more generated column labels using at least one federated learning technique; and performing one or more automated actions based at least in part on results of the standardizing of the at least a portion of data within the one or more similar data columns.

Abstract: Techniques for qualitatively assessing unlabeled data in an unsupervised machine learning environment are disclosed. In one example, a method comprises the following steps. A dataset of unlabeled data points is converted into a graph structure. Nodes of the graph structure represent the unlabeled data points in the dataset and weighted edges between at least a portion of the nodes represent similarity between the unlabeled data points represented by the nodes. A metric is computed for each node of the graph structure. A value generated by the metric for a given node represents a measure of dissimilarity between the corresponding unlabeled data point of the given node and one or more other unlabeled data points of one or more other nodes. A subset of the dataset is generated by removing one or more unlabeled data points from the dataset based on one or more values of the computed metric.

Abstract: Methods and systems for selecting a set of fast computable functions to assess core properties of entities are disclosed. A method includes: receiving a request to select a set of fast computable functions to determine core properties of an entity; determining, for each of a plurality of fast computable function nodes in a directed graph, a set of core property nodes in the directed graph that are connected to the fast computable function node; adding, to a solution set, a fast computable function node that is connected to a highest number of core property nodes that are currently unconnected to nodes in the solution set; repeating the adding until each of the core property nodes is connected to at least one of the nodes in the solution set; and outputting the fast computable function nodes in the solution set in response to the request.

Abstract: A method, computer system, and a computer program product for computing a product drag effect is provided. The present invention may include receiving a plurality of transaction record data. The present invention may then include tuning a plurality of parameters based on the received transaction record data. The present invention may further include determining a product drag frequency based on the authorized parameter tuning and received transaction record data. The present invention may then include calculating a drag probability based on the determined product drag frequency. The present invention may then include deriving an observation from the calculated drag probability. The present invention may lastly include outputting the derived observation to a user.

Abstract: Methods, systems, and computer program products for a collaborative cognition platform for creating and hosting social machines are provided herein. A computer-implemented method includes creating a social machine for collaborative tasks, wherein the social machine comprises (i) one or more human agents, (ii) one or more machine-based agents, (iii) an algorithm, and (iv) a set of rules prescribed for executing the collaborative tasks. The method also includes generating one or more collaborative resolutions for the collaborative tasks by executing, in an automated fashion, the collaborative tasks via implementation of the algorithm, wherein the algorithm facilitates, in accordance with the set of rules, systematic iterations of collaboration among (i) the one or more human agents and (ii) the one or more machine-based agents. Further, the method includes outputting the one or more collaborative resolutions to at least one user.