Abstract: Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.

Abstract: According to one embodiment of the present invention, a system for modifying content associated with an item comprises at least one processor. Features of interest of the item to a plurality of different groups are determined based on user comments produced by members of the plurality of different groups. The members within each group have a common characteristic. The features of interest to each group within the content associated with the item are identified, and the content associated with the item is modified by balancing the features of interest to the plurality of different groups within the content associated with the item. Embodiments of the present invention further include a method and computer program product for modifying content associated with an item in substantially the same manner described above.

Abstract: Machine learning model training corpus debiasing includes identifying an attribute of input text selected from the training corpus, the attribute including word(s) of the input text, and the attribute corresponding to an attribute class encompassing different possible class values, recognizing bias in the input text with respect to the attribute class, and generating output text corresponding to the attribute and imparting diversity with respect to the attribute class and relative to the input text, where generating the output text uses an optimization function based on loss objectives to minimize loss in the generated output text as compared to the input text.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: Methods, systems, and computer program products for detecting contextual bias in text are provided herein. A computer-implemented method includes identifying, by a machine learning network, a protected attribute in one or more data samples; processing the identified data samples using a first sub-network of the machine learning network, wherein the first sub-network is configured to determine a plurality of contexts of the protected attribute across the identified data samples; determining an impact of each of the plurality of contexts on a second sub-network of the machine learning network, wherein the second sub-network of the machine learning network is configured to classify a given data sample into one of a plurality of classes; and adjusting the second sub-network of the machine learning to account for the impact of at least one of the plurality of contexts on the second sub-network.

Abstract: Methods, systems, and computer program products for automatically modifying responses from generative models using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining data pertaining to at least one conversation involving at least one automated conversation exchange software program and at least one user; identifying, among words proposed by the at least one automated conversation exchange software program in connection with the at least one conversation, words qualifying as belonging to one or more predetermined categories by processing the obtained data using artificial intelligence techniques; determining, by processing the identified words and at least one word-based data source, one or more alternate words; modifying at least a portion of the proposed words by replacing at least a portion of the identified words with at least a portion of the one or more alternate words; and performing at least one automated action based on the modifying.

Abstract: Methods, systems, and computer program products for priority-based, accuracy-controlled individual fairness of unstructured text are provided herein. A method includes identifying one or more samples in a set of data used to train a machine learning model having at least one attribute; generating counterfactual samples for each of the one or more identified samples; calculating scores for the one or more identified samples based at least in part on output of the machine learning model with respect to the counterfactual samples, wherein the scores indicate a relative level of bias between the one or more identified samples corresponding to the at least one attribute; creating an enhanced set of data at least in part by supplementing at least a portion of the identified samples with the corresponding counterfactual samples based on the calculated scores; and training the machine learning model using the enhanced set of data.

Abstract: Machine learning model training corpus debiasing includes identifying an attribute of input text selected from the training corpus, the attribute including word(s) of the input text, and the attribute corresponding to an attribute class encompassing different possible class values, recognizing bias in the input text with respect to the attribute class, and generating output text corresponding to the attribute and imparting diversity with respect to the attribute class and relative to the input text, where generating the output text uses an optimization function based on loss objectives to minimize loss in the generated output text as compared to the input text.

Abstract: A processor may receive a record. The record may include one or more segments of text. The processor may automatically generate a first summary of the record. The processor may determine an overall bias of the first summary. The overall bias of the first summary may be identified from one or more instances of bias in the first summary. The processor may generate a second summary of the record. The second summary of the record may include an indicator of the overall bias of the first summary. The indicator may include a description of a type of overall bias of the first summary and a numerical value of the overall bias of the first summary. The processor may determine an overall bias of the second summary. The processor may display the second summary of the record to a user.

Abstract: According to one embodiment of the present invention, a system for modifying content associated with an item comprises at least one processor. Features of interest of the item to a plurality of different groups are determined based on user comments produced by members of the plurality of different groups. The members within each group have a common characteristic. The features of interest to each group within the content associated with the item are identified, and the content associated with the item is modified by balancing the features of interest to the plurality of different groups within the content associated with the item. Embodiments of the present invention further include a method and computer program product for modifying content associated with an item in substantially the same manner described above.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: Embodiments provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method of removing a cognitive terminology from a news article at a news portal, the method including: receiving, by the processor, a first news article from a user; configuring, by the processor, a cognitive terminology filter list to add one or more entities and one or more cognitive terminology types associated with each entity in the cognitive terminology filter list; dividing, by the processor, the first news article into a plurality of text segments; identifying, by the processor, one or more key entities and one or more inter-entity relationships of each text segment; detecting, by the processor, one or more cognitive terminologies in the first news article; and providing, by the processor, one or more suggestions to remove the one or more cognitive terminologies.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: A processor may receive a record. The record may include one or more segments of text. The processor may tag each segment of text with an indicator. The indicator may denote a specific instance of bias in each of a respective segment of text. The processor may automatically generate a summary of the record. The summary of the record may include a set of segments of text. The set of segments of text may have a different overall bias than the record. The processor may display the summary of the record to a user.

Abstract: A processor may receive a record. The record may include one or more segments of text. The processor may automatically generate a first summary of the record. The processor may determine an overall bias of the first summary. The overall bias of the first summary may be identified from one or more instances of bias in the first summary. The processor may generate a second summary of the record. The second summary of the record may include an indicator of the overall bias of the first summary. The indicator may include a description of a type of overall bias of the first summary and a numerical value of the overall bias of the first summary. The processor may determine an overall bias of the second summary. The processor may display the second summary of the record to a user.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: An article is automatically augmented. The article and one or more comments are received. Comment elements are extracted from the one or more comments, and article elements are extracted from the article. Alignment scores are generated for comment-article pairs based on the extracted comment and article elements. Further, it is determined that at least one comment-article pair has an alignment score at or above a threshold alignment score. At least one augmentation feature is then generated.

Abstract: Embodiments provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method of removing a cognitive terminology from a news article at a news portal, the method including: receiving, by the processor, a first news article from a user; configuring, by the processor, a cognitive terminology filter list to add one or more entities and one or more cognitive terminology types associated with each entity in the cognitive terminology filter list; dividing, by the processor, the first news article into a plurality of text segments; identifying, by the processor, one or more key entities and one or more inter-entity relationships of each text segment; detecting, by the processor, one or more cognitive terminologies in the first news article; and providing, by the processor, one or more suggestions to remove the one or more cognitive terminologies.