Abstract: A method for blockchain certification of artificial intelligence factsheets that includes receiving by a computing device, an artificial intelligence model. The computing device generates an artificial intelligence factsheet based upon logic of the artificial intelligence model. The computing device generates a blockchain link for a blockchain. The blockchain link certifies the artificial intelligence factsheet. The computing device transmits the blockchain link certifying the artificial intelligence factsheet to other computing devices.

Abstract: Methods, systems, and computer program products for generating explanations for a semantic parser are provided herein. A computer-implemented method includes providing to a generative model (i) at least one query and (ii) a context of at least one dataset applicable to the at least one query, wherein the generative model generates a plurality of perturbations for the at least one input query based on the context; providing the plurality of perturbations as inputs to a context aware sequence-to-sequence model, thereby obtaining a plurality of outputs; and generating, for (i) an additional query provided as input to the context aware sequence-to-sequence model and (ii) a context applicable to the additional query, an explanation indicative of one or more parts of the additional query that contributes to an output corresponding to the additional query, based at least in part on the plurality of outputs corresponding to the perturbations.

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: Methods, systems, and computer program products for generating collaborative orderings of information pertaining to products to present to target users are provided herein.

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 determining model-related bias associated with training data are provided herein. A computer-implemented method includes obtaining, via execution of a first model, class designations attributed to data points used to train the first model; identifying any of the data points associated with an inaccurate class designation and/or a low-confidence class designation; training a second model using the data points from the dataset, but excluding the identified data points; determining bias related to at least a portion of those data points used to train the second model by: modifying one or more of the data points used to train the second model; executing the first model using the modified data points; and identifying a change to one or more class designations attributed to the modified data points as compared to before the modifying; and outputting identifying information pertaining to the determined bias.

Abstract: Methods, systems, and computer program products for determining data representative of bias within a model are provided herein. A computer-implemented method includes obtaining a first dataset on which a model was trained, wherein the first dataset contains protected attributes, and a second dataset on which the model was trained, wherein the protected attributes have been removed from the second dataset; identifying, for each of the one or more protected attributes in the first dataset, one or more attributes in the second dataset correlated therewith; determining bias among at least a portion of the identified correlated attributes; and outputting, to at least one user, identifying information pertaining to the one or more instances of bias.

Abstract: One embodiment provides a method, including: accessing historical deployment information for a plurality of deep-learning models, wherein the historical deployment information identifies values for model parameters of a deep-learning model during deployment of the deep-learning model; receiving information related to a target deep-learning model that a developer is creating, wherein the received information identifies components being utilized in the target deep-learning model; determining, by comparing the received information to the historical deployment information, expected values for target model parameters of the target deep-learning model based upon the components utilized within the target deep-learning model; and providing a recommendation for a modification to the target deep-learning model based upon the expected values, wherein the modification comprises a change to at least one component of the target deep-learning model.

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: Methods, systems, and computer program products for domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository are provided herein. A computer-implemented method includes obtaining a set of data and information indicative of a domain of said set of data; obtaining constraints from a domain-indexed constraint repository based on said set of data and said information, wherein the domain-indexed constraint repository comprises a knowledge graph having a plurality of nodes, wherein each node comprises an attribute associated with at least one of a plurality of domains and constraints corresponding to the attribute; detecting anomalies in said set of data based on whether portions of said set of data violate said retrieved constraints; generating an explanation corresponding to each of the anomalies that describe the attributes corresponding to the violated constraints; and outputting an indication of the anomalies and the corresponding explanation.

Abstract: One embodiment provides a method, including: assigning a machine learning model signature to a machine learning model, wherein the machine learning model signature is generated using (i) data points and (ii) corresponding data labels from training data; receiving input comprising identification of a target machine learning model; acquiring a target signature for the target machine learning model by generating a signature for the target machine learning model using (i) data points from the assigned machine learning model signature and (ii) labels assigned to those data points by the target machine learning model; determining a stolen score by comparing the target signature to the machine learning model signature and identifying the number of data labels that match between the target signature and the machine learning model signature; and classifying the target machine learning model as stolen based upon the stolen score reaching a predetermined threshold.

Abstract: One embodiment provides a method, including: receiving, from a client, (i) a task of annotating information, (ii) a set of instructions for performing the task, and (iii) client annotations for a subset of the information within the task; assigning the subset to a plurality of annotators; obtaining (i) annotator annotations for the subset and (ii) a response time for providing the annotator annotation for each piece of information within the subset; identifying improvements to the set of instructions by (i) comparing the annotator annotations to the client annotations and (ii) identifying discrepancies made by the annotators in view of the response time; and generating a new set of instructions, wherein the generating comprises (i) identifying at least one feature of the information that distinguishes correctly annotated information from incorrectly annotated information and (ii) generating an instruction from the at least one feature.

Abstract: Aspects of the present invention provide an approach for reducing bias in active learning. In an embodiment, a data point is selected from a training dataset for a current training iteration while monitoring for data bias at each addition of data to a virtual training dataset. In addition, a machine learning model is examined for bias after adding the selected data point to the virtual training dataset. When data bias and/or model bias is detected, the data point is considered for potential label modification. The selected data point is modified and, if the raw value of the modified data point is within a predefined tolerance and within a bin of a desired class, the modified data point having a label of the target class is retained. Otherwise, it can be discarded.

Abstract: Methods, systems, and computer program products for generating a framework for prioritizing machine learning model offerings via a platform are provided herein. A computer-implemented method includes processing, via a computing platform, a machine learning model input by a first user and metadata corresponding to the machine learning model input by the first user; automatically comparing, via the computing platform, the metadata corresponding to the machine learning model with metadata corresponding to one or more existing machine learning models stored by the computing platform; automatically calculating, via the computing platform, initial pricing information for the machine learning model based on the comparison; and outputting, via an interactive user interface of the computing platform, the machine learning model to one or more additional users for purchase in accordance with the calculated initial pricing information.

Abstract: One embodiment provides a method, including: receiving, at a data service provider, a request from an information purchaser, wherein the request comprises (i) a budget identifying an amount of money to be spent on information and (ii) an objective function identifying a type of information that the information purchaser is requesting; accessing at least a subset of at least one information set of at least one information seller, wherein each of the at least one information sets comprises an information set available for purchase from the information seller; identifying whether at least one accessed information set that fulfills the received request; and providing, if at least one accessed information set fulfills the received request, a recommendation of an information set for purchase by the information purchaser, wherein the provided recommendation comprises at least one of the identified information sets that fulfills the received request.

Abstract: One embodiment provides a method, including: receiving, from a user, a dataset for encryption before its storage at a data storage location, wherein the dataset comprises a plurality of portions; identifying (i) attributes of the dataset and (ii) dataset dependencies; generating a recommendation for an encryption scheme to be used for the dataset, wherein the generating comprises (i) generating, based upon the attributes and the dataset dependencies, a recommendation of an encryption scheme for each portion of the dataset and (ii) identifying, based upon the dataset dependencies, a key label for each portion of the dataset, wherein the key label identified for a portion of the dataset that is dependent on another portion of the dataset is the same as the key label identified for said another portion of the dataset; and providing, to the user, (i) the generated recommendation and (ii) a description identifying reasons for the generated recommendation.

Abstract: A method, computer system, and a computer program product for generating deep learning model insights using provenance data is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include generating model insights based on the collected provenance data. Embodiments of the present invention may include generating a training model based on the generated model insights. Embodiments of the present invention may include reducing the training model size. Embodiments of the present invention may include creating a final trained model.

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: A method, computer system, and a computer program product for utilizing provenance data to improve machine learning is provided. Embodiments of the present invention may include collecting provenance data. Embodiments of the present invention may include identifying model quality improvements based on the collected provenance data. Embodiments of the present invention may include identifying related models based on the collected provenance data. Embodiments of the present invention may include recommending model quality improvements to a user.