International Business Machines Corporation

Sandeep Hans

Diptikalyan Saha

Aniya Aggarwal

Samiulla Zakir Hussain Shaikh

Sameep Mehta

Pranay Kumar Lohia

Rema Ananthanarayanan

Gagandeep Singh

Manish Anand Bhide

Praduemn K Goyal

Soujanya Soni

Methods, systems, and computer program products for localization-based test generation for individual fairness testing of AI models are provided herein. A computer-implemented method includes obtaining at least one artificial intelligence model and training data related to the at least one artificial intelligence model; identifying one or more boundary regions associated with the at least one artificial intelligence model based at least in part on results of processing at least a portion of the training data using the at least one artificial model; generating, in accordance with at least one of the one or more identified boundary regions, one or more synthetic data points for inclusion with the training data; and executing one or more fairness tests on the at least one artificial intelligence model using at least a portion of the one or more generated synthetic data points and at least a portion of the training data.

Localization-based test generation for individual fairness testing of artificial intelligence models

Methods, systems, and computer program products for automatically testing AI models in connection with enterprise-related properties are provided herein. A computer-implemented method includes obtaining an artificial intelligence model and information pertaining to one or more existing enterprise-related properties associated with the artificial intelligence model; generating test data for the artificial intelligence model by processing at least a portion of the information pertaining to one or more existing enterprise-related properties; executing at least a portion of the generated test data against the artificial intelligence model; generating, based at least in part on results of the executing of the at least a portion of the generated test data against the artificial intelligence model, one or more enterprise-related property recommendations for the artificial intelligence model; and performing one or more automated actions based at least in part on the one or more enterprise-related property recommendations.

Automatically testing artificial intelligence models in connection with enterprise-related properties

An embodiment for identifying and sorting duplicate datasets within a large pool of heterogeneous datasets may include received a plurality of heterogeneous datasets. The embodiment may automatically compare schema information and metadata within each of the received plurality of heterogeneous datasets to generate name-based similarity scores for each dataset. The embodiment may also automatically compare data distribution information within each of the received plurality of heterogeneous datasets to generate a plurality of data distribution similarity scores for each heterogeneous dataset. The embodiment may further include automatically calculating an overall distance metric using the name-based similarity scores and plurality of data distribution similarity scores. The embodiment may also include based on the calculate overall distance metric, automatically generating distance graphs that identifying clusters of similar datasets and illustrate inferred lineage for the clusters of similar datasets.

Scalable identification of duplicate datasets in heterogeneous datasets

Methods, systems, and computer program products for improving trustworthiness of artificial intelligence models in presence of anomalous data are provided herein. A method includes obtaining a machine learning model and a set of training data; determining one or more anomalous data points in said set of training data; for a given one of said anomalous data points, identifying attributes that decrease confidence with respect to at least one output of said machine learning model; determining that a root cause of said decreased confidence corresponds to one of: a class imbalance issue related to said at least one attribute, a confused class issue related to said at least one attribute, a low density issue related to said at least one attribute, and an adversarial issue related to said at least one attribute; and performing step(s) to improve said confidence based at least in part on said determined root cause.

Trustworthiness of artificial intelligence models in presence of anomalous data

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.

Domain aware explainable anomaly and drift detection for multi-variate raw data using a constraint repository

A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving, via the user interface, a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database and an address of a location of executable code corresponding to the data validation rule. The data validation service also includes transmitting the configuration file and remote methods to the computer device over the network. The remote methods are configured to execute the data validation rule with respect to the data and compile results of the execution.

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New Delhi, India

Sandeep Hans