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: 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: Aspects of the present disclosure relate to neural-based ontology generation and refinement. A set of input data can be received. A set of entities can be extracted from the set of input data using a named-entity recognition (NER) process, each entity having a corresponding label, the corresponding labels making up a label set. The label set can be compared to concepts in a set of reference ontologies. Labels that match to concepts in the set of reference ontologies can be selected as a candidate concept set. Relations associated with the candidate concepts within the set of reference ontologies can be identified as a candidate relation set. An ontology can then be generated using the candidate concept set and candidate relation set.

Abstract: A method, a system, and a computer program product are provided for analyzing an instructional video. Video data of an instructional video is analyzed to form multiple units of work. Each unit of work is a respective grouping of video frames of the instructional video based on a respective logical combination of activities associated therewith. Each unit of work is analyzed to produce a respective action graph of activities included in the unit of work, the respective action graph indicating interdependencies among the activities included therein. Interdependencies among activities across the units of work are determined to form a critical path graph. A received query is processed to provide troubleshooting assistance with respect to the instructional video based on the units of work, the action graphs, the critical path graph, and a knowledge base including information related to a subject matter of the instructional video.

Abstract: Asset recommendation for a particular input dataset is provided. Candidate data analysis assets having a corresponding relatedness score associated with the particular input dataset greater than a defined relatedness score threshold value are selected. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are ranked by score. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are listed by rank from highest to lowest. A justification for each candidate data analysis asset is inserted in the ranked list of candidate data analysis assets. The ranked list of candidate data analysis assets along with each respective justification is outputted on a display device.

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 identifying anomalous transformations using lineage data are provided herein. A computer-implemented method includes generating a set of column profiles for a corresponding set of columns within one or more datasets based at least in part on lineage data and glossary data, wherein the lineage data comprises information related to transformations performed on each column in the set by a computing platform, and wherein the glossary data comprises information related to one or more terms assigned to one or more of the columns; obtaining information related to a new transformation involving at least one column in the set of columns; comparing the new transformation to the set of column profiles to determine whether the new transformation is anomalous; and in response to determining the new transformation is anomalous, outputting an alert to a user of the computing platform.

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: 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: Embodiments are disclosed for a method. The method includes receiving an annotation set for a machine learning model. The annotation set includes multiple data points relevant to a task for the machine learning model. The method also includes determining total weights corresponding to the data points. The total weights are determined based on multiple ordering constraints indicating multiple data classes and corresponding weights. The corresponding weights represent a relative priority of the data classes with respect to each other. The method further includes generating an ordered annotation set from the annotation set. The ordered annotation set includes the data points in a sequence based on the determined total weights.

Abstract: Methods, systems, and computer program products for factchecking artificial intelligence models using blockchain are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and at least one set of data related to the at least one artificial intelligence model; determining a set of characteristics based at least in part on the at least one artificial intelligence model and the at least one set of data; selecting one of a plurality of networks based at least in part on a target deployment of the at least one artificial intelligence model to verify the set of characteristics; generating a report based at least in part on verifying the set of characteristics using the selected network, wherein the report establishes a threshold level of trust for the at least one artificial intelligence model; and storing the report on a blockchain.

Abstract: One embodiment provides a computer implemented method, including: obtaining an information document corresponding to an entity, wherein the information document includes redacted information spans; identifying an entity type for each of the redacted information spans, wherein the entity type identifies a relationship between a redacted information span and at least one other entity within the information document; replacing the redacted information spans with replacement entities corresponding to the entity type of a given redacted information span, wherein the replacing is performed in view of a frequency distribution of actual information and wherein the replacing includes maintaining relationships of the redacted information spans; and controlling bias within the replacement entities, wherein the controlling includes detecting bias within the replacement entities.

Abstract: A method to automatically assess data quality of data input into a machine learning model and remediate the data includes receiving input data for an automated machine learning model. Selections for a multiple data quality metrics are displayed. A selection for data quality metrics is received. The data quality metrics are determined according to the selection. Selections for data remediation strategies based on the selection of the data quality metrics are displayed. A selection for remediation recommendation strategies is received. The selected data remediation strategies are performed on the input data. Learning from the selection of the data quality metrics and the selection for the remediation strategies is performed. A new customized machine learning model is generated based on the learning.

Abstract: One embodiment provides a computer implemented method, including: receiving, from a user, a natural language query for data contained within at least one data repository; identifying at least one concept from the natural language query, wherein the at least one concept includes an entity and an intent; identifying a plurality of datasets satisfying the natural language query by querying the at least one data repository utilizing the at least one concept; ranking the dataset based on relevance to the query; generating an extract-transform-load script that extracts, transforms, and loads a dataset selected by the user from the plurality of datasets; and retrieving data included in the dataset utilizing the extract-transform-load script, wherein the retrieving includes returning the data to the user.

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: 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: One embodiment provides a method, including: obtaining a dataset for use in building a machine-learning model; assessing a quality of the dataset, wherein the quality is assessed in view of an effect of the dataset on a performance of the machine-learning model, wherein the assessing comprises scoring the dataset with respect to each of a plurality of attributes of the dataset; for each of the plurality of attributes having a low quality score, providing at least one recommendation for increasing the quality of the dataset with respect to the attribute having a low quality score; and for each of the plurality of attributes having a low quality score, providing an explanation explaining a cause of the low quality score for the attribute having a low quality score.

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: Aspects of the present disclosure relate to neural-based ontology generation and refinement. A set of input data can be received. A set of entities can be extracted from the set of input data using a named-entity recognition (NER) process, each entity having a corresponding label, the corresponding labels making up a label set. The label set can be compared to concepts in a set of reference ontologies. Labels that match to concepts in the set of reference ontologies can be selected as a candidate concept set. Relations associated with the candidate concepts within the set of reference ontologies can be identified as a candidate relation set. An ontology can then be generated using the candidate concept set and candidate relation set.

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.