Abstract: Techniques regarding the governing of use by a consumer of an artificial intelligence technology are described. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include an analyzing component that can, based on an artificial intelligence usage policy, analyze a proposed use by a consumer of an artificial intelligence technology, resulting in an analyzed use of the artificial intelligence technology. The computer executable components can further include a governing component that can, based on the analyzing, govern use by the consumer of the artificial intelligence technology.

Abstract: A method, system, and computer program product for generating lineage events of machine learning models. The method may include identifying a machine learning model with missing lineage. The method may also include generating a creation event and deployment event for the machine learning model. The method may also include generating a version change event for the machine learning model. Generating the version change event may include identifying one or more predicted data points with a low model confidence; rescoring the one or more predicted data points based on the machine learning model at a second time period; determining that the updated one or more predicted data points are significantly different than the one or more predicted data points; and determining that there is a new version of the machine learning model. The method may also include creating a lineage path for the machine learning model.

Abstract: A method, computer system, and computer program product for explaining time series machine learning model are provided. The embodiment may include determining a first order difference in time series input data and historical training data. The embodiment may also include performing perturbation of time series input data based on the determined first order difference and the determined historical training data. The embodiment may further include computing closeness of the determined first order difference in the historical training data to the determined first order difference in the time series input data. The embodiment may also include generating a uniform random sample of first value input to a time series machine learning model. The embodiment may further include determining values of other inputs to the time series machine learning model based on the generated random sample and a random sample from the historical training data first order differences.

Abstract: In an approach to facilitate contactless remote shopping assistance, a computer-implemented method includes one or more processors configured for receiving first order data from a user device corresponding to an order of items to be retrieved from a parking lot of a retail entity proximate to a retail entity location, transmitting the first order data to the retail entity, and assigning the order to a mobility device stationed at the retail entity. The method further includes transmitting order retrieval instructions to the mobility device causing the mobility device to navigate the retail to secure the items, receiving an indication that the mobility device secured the items, and transmitting order delivery instructions to the mobility device. The method further includes receiving a second indication that the mobility device transported the items to the parking lot and placed the items in a vehicle associated with a user of the user device.

Abstract: In an approach, a processor receives a request to access an electronic resource from a device. A processor causes the device to generate a Completely Automated Public Turing test (CAPTCHA), where the CAPTCHA comprises: a virtual keyboard; an ordered string of characters required to be input; and presentation of a highlighted key of the virtual keyboard on the device, wherein (i) the highlighted key is a first key visually distinct from other keys of the virtual keyboard and (ii) the first key corresponds to a character of the ordered string of characters. A processor receives a result of the CAPTCHA from the device. A processor performs an action based on the result.

Abstract: A computer-implemented method includes: receiving, by a computing device and from a user device, a request to validate an application program interface (API); validating, by the computing device, the API by performing a fetch analysis using different user profiles; returning, by the computing device and to the user device, a result of the fetch analysis; validating, by the computing device, the API by performing an insert/update analysis using the different user profiles; and returning, by the computing device and to the user device, a result of the insert/update analysis.

Abstract: Computer-implemented methods, systems and program products establishing dynamically compliant and secure vehicle communication networks. Software bill of materials (SBOMs) are leveraged by route mapping services or other applications to ensure vehicle components and dependencies thereof comply with locational policies along a vehicle's route, as well as individual vehicle policies required to engage in vehicle-to-vehicle communications. Vehicle SBOMs are fetched and compared to policies for each location or zone a vehicle may pass through during a trip. Applicable policies can be set by government, or other institutions. Vehicles failing to comply with global policies of locations can be prevented from communicating or connecting to vehicle networks or may be re-routed to avoid locations where compliance is not achieved.

Abstract: Provided are a computer program product, system, and method for rendering an extended-reality representation of a virtual meeting. An extended reality representation is generated of a virtual meeting including avatars associated with participants in the virtual meeting to render in extended-reality displays of the participants. An extended-reality representation of a virtual controller corresponding to a real-world controller in a real-world location is generated to render in at least one extended-reality display of at least one participant in the virtual meeting. Participant input is received from one of the at least one participant to control the virtual controller. The participant input is translated to control the virtual controller to commands to control the real-world controller. The commands are transmitted to the real-world controller at the real-world location to control the real-world controller to perform actions in the real-world location to carry out the transmitted commands.

Abstract: A machine learning model is trained. A set of drifted records are determined during payload time of the machine learning model. The determined set of drifted records are pruned to use in retraining of the machine learning model.

Abstract: An example operation may include one or more of storing a batch scoring engine and an application programming interface (API) for the batch scoring engine, receiving a trigger to perform a batch prediction process, reading input data from a source data store and executing, via the batch scoring engine, one or more predictive models on the input data to generate a predictive output and metadata associated with the predictive output, storing the predictive output and the metadata in a target data store, and updating the API with a location of the predictive output within the target data store and a location of the metadata within the target data store.

Abstract: Explaining decisions or predictions of an AI model can include generating perturbed instances by perturbing one or more encoded features of a multimodal AI model instance. For each of the perturbed instances, a distance between one or more encoded features of each perturbed instance and a corresponding one or more of the encoded features of the multimodal AI instance can be determined. Each distance can be converted to a weight using a kernel function. For each weight, a modality-specific Shapley value can be determined, and each weight can be adjusted by post-weighting each weight with the modality-specific Shapley value associated with the weight to obtain final weights. An interpretable surrogate model based on the final weights can be output.

Abstract: Provided are techniques for correcting a classification model. For each original record of a plurality of original records that are processed by a classification model: the original record is perturbed; for the original record, an original confidence value is obtained for each class of a plurality of classes; for the perturbed record, a perturbed confidence value is obtained for each class of the plurality of classes; a final confidence value is determined using each original confidence value, each perturbed confidence value, and a direction of distance travelled; and a determination is made of whether the original record is biased based on the final confidence value. Then, it is determined whether the classification model is biased based on the original records that are determined to be biased. In response to determining that the classification model is biased, the classification model is corrected, otherwise, the classification model is deployed.

Abstract: In an approach, a processor receives a photograph requirement from an image of an application, the photograph requirement associated with a photograph to be uploaded to the application. A processor adjusts a camera setting based on the photograph requirement. A processor captures the photograph in accordance with the adjusted camera setting.

Abstract: A system and related method score a fairness of an outcome model. The method comprises receiving a set of original transaction records (OTRs), and selecting an OTR subset of the OTRs according to a subset selection criteria in order to reduce a number of OTRs to send to outcome model. For each OTR in the subset a perturbed transaction record (PTR) is created based on the OTR that includes changing at least one attribute in the PTR from the OTR, sending the OTR and the PTR to the outcome model, receiving an OTR outcome and a PTR outcome from the outcome model, and determining a record bias score for the OTR outcome and the PTR outcome respectively that indicates bias in the respective outcome. The OTR and the PTR bias score are stored in a bias determination system (BDS) database.

Abstract: Early indications of application programming interface (API) usage are identified by correlation to particular issues with the API including singular and mutual consistency, completeness, accuracy, and staleness. Analysis of API input and output along with data type and formatting information facilitates identification of the API issues. Establishing a correlation between API usage and issues supports early detection of potential usage reduction on a case-by-case level. Corrective action to resolve identified issues may be performed in a timely manner to maintain usage levels.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate abnormal document self-discovery. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a fairness component, an identification component, a removal component, and an evaluation component. The fairness component can receive a dataset for a tree-based model and calculates a first fairness, and the identification component can identify root to leaf paths in the tree-based model for one or more records of the dataset and one or more corresponding perturbed records. The removal component can remove at least one record of the one or more records having similar root to leaf paths to the other records of the one or more records.

Abstract: Disclosed aspects include computer-implemented method for preventing Carpal Tunnel Syndrome (CTS), a computer program product, and a laptop computer. One embodiment of the method may comprise collecting, by a device having an associated keyboard, data of a user's interaction with the keyboard. The method may further comprise analyzing, by a first machine learning model, the audiovisual data to identify ergonomic issues in the user's interaction with the keyboard. The method may further comprise notifying the user of the identified ergonomic issues in real time.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to predicting bias in an artificial intelligence (AI) model. A system can comprise a memory configured to store computer executable components; and a processor configured to execute the computer executable components stored in the memory, wherein the computer executable components can comprise a data generation component that can generate a set of structured test data to test likelihood of an AI model generating biased outputs, based on analysis of payload logging data; and an alerting component that can alert a user of likelihood that the AI model will generate the biased outputs, wherein the alerting component can generate an alert in response to at least a first set of records approaching a defined threshold.

Abstract: Embodiments are disclosed for a method for machine-learning model accuracy. The method includes generating prediction training data based on training predictions and corresponding probabilities of the training predictions. A classifier of a machine-learning model generates the training predictions. The method also includes training a prediction accuracy model to determine whether the training predictions generated by the machine-learning model are correct. Additionally, the method includes generating predictions in response to corresponding client transactions for the machine-learning model. Further, the method includes determining whether the predictions are accurate using the prediction accuracy model. Also, the method includes providing client predictions corresponding to the client transactions based on the determination.

Abstract: A marking onto a collaboration board, located in a first location, is detected. The marking includes the creation of one or more content elements on the collaboration board. Responsive to detecting the markings one or more content elements on the collaboration board are analyzed. A first content element of the one or more content elements is classified based on analyzing the one or more content elements. The first content element is classified as a first privileged content element. The first location of the collaboration board is scanned. The scanning is based on the classifying of the first privileged content element. An unauthorized person is identified based on scanning the first location. The unauthorized person is not permitted to view the collaboration board. A security response is performed on the collaboration board regarding the one or more content elements. The security response is performed responsive to identifying the unauthorized person.