International Business Machines Corporation

Rebecca Rose James

Andrew Ronald Freed

Lalit Agarwalla

Stephan Joseph Roorda

Eliza Salkeld

Aaron T Smith

Shane M Kilmon

Brianne Boldrin

Jasmeet Singh

Leo Mazzoli

Disclosed is a computer implemented method to identify relevant feedback. The method includes training, with a set of training data, a generative adversarial network (GAN), wherein the GAN includes a generator and a discriminator, and the set of training data is comprised of a plurality of feedback, each instance of feedback includes a value in a plurality of categories, the categories including a positivity value, and a validity value. The method also includes receiving, from a user, a new instance of feedback. The method further includes determining, by the discriminator, a first validity value and a first positivity value of the new instance of feedback. The method includes updating, in response to the first validity value being valid, and the first positivity value being negative, the GAN. The method further includes, displaying the new instance of feedback.

Feedback discriminator

A processor may identify one or more data fields of interest. The processor may receive data for each of the data fields of interest. The processor may compute one or more bias scores for the data fields of interest based on the data. The processor may develop a bias matrix with the bias scores. The processor may display the bias matrix to a user.

Bias detection

A dialog change service builds a directed graph of nodes, each described by code for a conversational agent stored in source control, the code comprising one or more dialog-based features. The dialog change service, responsive to receiving a request for a dialog change to a particular node of the conversational agent, evaluates the directed graph to identify an additional selection of nodes that require a similar changes as the dialog change based on textual and structural content. The dialog change service updates the code for the particular node and the additional selection of nodes to propagate the dialog change in the conversational agent stored in source control.

Efficiently managing predictive changes for a conversational agent

Embodiments include a method, system and computer program product for detecting impersonation attempts in social media messaging. Aspects include receiving, via a social media network, a message from a sender to a recipient and analyzing a content of the message to extract factual statements from the message. Aspects also include analyzing a profile of the recipient to extract facts from the profile and comparing each of the factual statements to the facts from the profile. Based on a determination that one of the factual statements are verifiable by at least one of the facts, aspects include assigning a likelihood score to the factual statements. Aspects further include calculating a legitimacy score for the message based at least in part on the likelihood score of each verified factual statement from the message and transmitting the legitimacy score and the message to the recipient.

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Cary, NC, United States of America

Rebecca Rose James