Abstract: A computer-implemented method, a computer program product, and a computer system for misinformative content mitigation. A computer receives from a social network system a request for curated content, where the curated content is related to misinformative content that has been identified by the social network system. The computer sends to a system of a paid curated content producer a request for making the curated content public. In response to that the system of the paid curated content producer accepts the request for making the curated content public, the computer provides to the social network system a link to the curated content on the system of the paid curated content producer. On the social network system, the misinformative content is flagged and the link to the curated content is presented aside the misinformative content.

Abstract: A system for providing permissioned images in media transactions is described. The system processes images in an image set for a media transaction to identify the image owner and other persons depicted in the image and determines use permissions for the images. Various images in the image set are processed to remove or anonymize various persons in order to meet use conditions. The processed images are compiled and provided to an entity requesting the image set.

Abstract: A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations that include analyzing interactions by a user within a network and generating a user profile for the user. The operations by the processor may further include identifying an attempt by the user to share a post via the network and prompting the user with a personalized alert to evaluate the post, wherein the personalized alert is generated based on the interactions, the user profile, and the properties of the post.

Abstract: To reduce misinformation consumption in the media, a computer-implemented method is described for presenting thought-provoking information about a media product that includes receiving media consumption data indicating a media product was consumed via a computing device user interface; determining claims for the media product; identifying a plurality of related media products based at least on a topic of the media product; determining positions for the plurality of related media products with respect to the one or more claims; determining a most contested claim as a claim that satisfies a condition corresponding to having a predetermined number of disagreeing related media products; generating a question based on the most contested claim and a paragraph including the most contested claim; generating an answer to the question based on the question and the related media product that disagrees with the most contested claim; and presenting the question and answer via the user interface.

Abstract: In an approach to training a chatbot for remote troubleshooting, one or more computer processors receive a request for help with an issue from a user via a chatbot. One or more computer processors extract information associated with the issue from the request. One or more computer processors determine a script is not available to resolve the issue. One or more computer processors request an agent to resolve the issue. One or more computer processors monitor one or more actions performed by the agent to resolve the issue. Based on the one or more actions, one or more computer processors generate a script, wherein the script includes a sequence of commands corresponding to the one or more actions. One or more computer processors store the script.

Abstract: A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Abstract: In an approach to training a chatbot for remote troubleshooting, one or more computer processors receive a request for help with an issue from a user via a chatbot. One or more computer processors extract information associated with the issue from the request. One or more computer processors determine a script is not available to resolve the issue. One or more computer processors request an agent to resolve the issue. One or more computer processors monitor one or more actions performed by the agent to resolve the issue. Based on the one or more actions, one or more computer processors generate a script, wherein the script includes a sequence of commands corresponding to the one or more actions. One or more computer processors store the script.

Abstract: A computer-implemented method, a computer program product, and a computer system for misinformative content mitigation. A computer receives from a social network system a request for curated content, where the curated content is related to misinformative content that has been identified by the social network system. The computer sends to a system of a payed curated content producer a request for making the curated content public. In response to that the system of the payed curated content producer accepts the request for making the curated content public, the computer provides to the social network system a link to the curated content on the system of the payed curated content producer. On the social network system, the misinformative content is flagged and the link to the curated content is presented aside the misinformative content.

Abstract: A method and system of mitigating bias in a decision-making system are provided. A presence of bias is identified in one or more machine learning models. For each of the machine learning models, a presence of bias in an output of the model is determined. One or more options to mitigate a system bias during a processing stage, based on the identified presence of bias for each of the one or more models, are determined. One or more options to mitigate the system bias during a post-processing stage, based on the identified presence of bias in each output of the models, are determined. A combination of options is provided, including (i) a processing option for the processing stage, and (ii) a post-processing option for the post-processing stage, wherein the combination of options accommodates a threshold bias limit to the system bias and a total bias mitigation cost threshold.

Abstract: Aspects discussed herein include a computer-implemented method comprising receiving a support request from a user, and during a current support session responsive to the support request, performing textual analysis of one or more inputs provided by the user during one or both of: (i) the current support session and (ii) one or more previous support sessions. The method further comprises applying one or more features determined by the textual analysis to a model to classify the user into a first class of a predefined plurality of classes that describe a pace of conversation during the current support session. The method further comprises, based on the classification of the user, assigning the support request to be fulfilled by a first operator of a predefined plurality of support operators.

Abstract: A computer system includes a processor configured to execute a conversational agent generator function. The conversational agent generator function is configured to receive a plurality of model input queries. Each model input query includes an intent and a corresponding answer to the intent. A database stores a corpus including sets of textual responses indicative of a common meaning. Each textual response in a given set of textual responses indicates a common meaning constructed in a different style indicative of a corresponding tone of communication. The processor is further configured to execute a rephrasing function configured determine the answer of one of the plurality of model input queries, and convert the answer of one of the plurality of model input queries into a plurality of different rephrased answers from the corpus stored in the database. The processor further outputs the plurality of different rephrased answers to the conversational agent generator function.

Abstract: Techniques relate to adapting communications. A computer system receives initial content and event constraints related to a to-be-delivered communication and extracts information for recipients designated to receive the to-be-delivered communication. The computer system determines content utility values for topics of the initial content for each of the recipients, the content utility values for each of the topics being based on the information for each of the recipients. The computer system determines total content utility values for each of the topics in the initial content and selects elements of the to-be-delivered communication from a database based at least in part on the total content utility values for the topics and the event constraints.

Abstract: Aspects discussed herein include a computer-implemented method comprising receiving a support request from a user, and during a current support session responsive to the support request, performing textual analysis of one or more inputs provided by the user during one or both of: (i) the current support session and (ii) one or more previous support sessions. The method further comprises applying one or more features determined by the textual analysis to a model to classify the user into a first class of a predefined plurality of classes that describe a pace of conversation during the current support session. The method further comprises, based on the classification of the user, assigning the support request to be fulfilled by a first operator of a predefined plurality of support operators.

Abstract: Systems and methods are described for supply chain vulnerability forecasting using external event data is described. The method may include identifying historic data from each entity of a plurality of entities connected in a supply chain, aggregating temporal-based information relating to the entities and to relations among the entities into one or more time periods based on the historic data, training a cognitive prediction model using the aggregated temporal-based information, generating a vulnerability score for each entity, generating a recommendation for at least one entity if the vulnerability score for the at least one entity is above a preset threshold, retraining the cognitive prediction model using changed data of the at least one entity, and adjusting the vulnerability score based on the retraining.

Abstract: A computer system includes a processor configured to execute a conversational agent generator function. The conversational agent generator function is configured to receive a plurality of model input queries. Each model input query includes an intent and a corresponding answer to the intent. A database stores a corpus including sets of textual responses indicative of a common meaning. Each textual response in a given set of textual responses indicates a common meaning constructed in a different style indicative of a corresponding tone of communication. The processor is further configured to execute a rephrasing function configured determine the answer of one of the plurality of model input queries, and convert the answer of one of the plurality of model input queries into a plurality of different rephrased answers from the corpus stored in the database. The processor further outputs the plurality of different rephrased answers to the conversational agent generator function.

Abstract: Techniques relate to adapting communications. A computer system receives initial content and event constraints related to a to-be-delivered communication and extracts information for recipients designated to receive the to-be-delivered communication. The computer system determines content utility values for topics of the initial content for each of the recipients, the content utility values for each of the topics being based on the information for each of the recipients. The computer system determines total content utility values for each of the topics in the initial content and selects elements of the to-be-delivered communication from a database based at least in part on the total content utility values for the topics and the event constraints.

Abstract: Techniques for image analysis and configuration modification are provided. A first context of a camera device is determined, where the first context includes a current location of the camera device. A plurality of prior images is identified based on the first context, where each of the plurality of prior images was captured within a predefined distance from the current location of the camera device. Further, a respective popularity score is generated for each of the plurality of prior images, based on one or more social media networks. Finally, a first set of configuration parameters is generated for the camera device, based at least in part on (i) a degree of difference between the first context and a second context associated with a first prior image of the plurality of prior images, and (ii) a first popularity score of the first prior image.

Abstract: A system for providing permissioned images in media transactions is described. The system processes images in an image set for a media transaction to identify the image owner and other persons depicted in the image and determines use permissions for the images. Various images in the image set are processed to remove or anonymize various persons in order to meet use conditions. The processed images are compiled and provided to an entity requesting the image set.

Abstract: Techniques for image analysis and configuration modification are provided. A first context of a camera device is determined, where the first context includes a current location of the camera device. A plurality of prior images is identified based on the first context, where each of the plurality of prior images was captured within a predefined distance from the current location of the camera device. Further, a respective popularity score is generated for each of the plurality of prior images, based on one or more social media networks. Finally, a first set of configuration parameters is generated for the camera device, based at least in part on (i) a degree of difference between the first context and a second context associated with a first prior image of the plurality of prior images, and (ii) a first popularity score of the first prior image.