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.

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: 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 example of a system comprises using a processor for identifying a model to be validated that is stored in a repository; automatically computing and recording one or more model metrics for the model to be validated in a tamper-proof manner; comparing the computed tamper-proof metrics with one or more encoded rules and policies to determine if the model to be validated complies with the one or more encoded rules and policies; and outputting a notification to a device indicating a validation status of the model to be validated based on the comparison of the computed tamper-proof metrics with the one or more encoded rules and policies.

Abstract: One or more computer processors determine a storage strategy for each chunked data block in a training dataset based on a respective computed usefulness score and a series of usefulness thresholds, wherein the storage strategy comprises RAID strategies that include striping, mirroring, parity, and double parity. The one or more computer processors distribute each data block in the training dataset according to the respective determined storage strategy.

Abstract: A system and method is provided to selectively process and store tables of a relational database by calculating an overall data sensitivity score for each table based on predefined attribute rules; performing column-wise splitting of at least one of the tables into a first table and a second table based on the overall data sensitivity score of each table, thereby generating a total number of relational database tables; storing a first subset of the total number of relational database tables in a private cloud storage database in a distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables; and storing a second subset of the total number of relational database tables in a public cloud storage database of the distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables.

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: An embodiment includes activating, responsive to receiving an update notification, an update mode of a mobile device, wherein the activating of the update mode includes disabling a primary communication interface and enabling a secondary communication interface, and wherein the update notification includes notification of a software update available for the mobile device. The embodiment also includes initiating execution of the software update on the mobile device while the mobile device remains in the update mode. The embodiment also includes deactivating, responsive to completing the software update, the update mode of the mobile device, wherein the deactivating of the update mode includes enabling the primary communication interface and disabling the secondary communication interface.

Abstract: A computer-implemented method, a computer program product, and a computer system for automated model lineage inference. A computer system identifies training datasets which is used to train a machine learning model. A computer system identifies parent datasets from which the training datasets are derived. A computer system identifies associated feature transformations when the training datasets are derived from the parent datasets.

Abstract: A computer-implemented method for refining dataset to accurately represent output of an artificial intelligence model includes generating a plurality of data points used to interpret a decision of an artificial intelligence model. A subset of data points from the generated plurality of data points satisfying one or more constraints is identified. A linear model is applied on the identified subset of data points satisfying the one or more constraints. One or more insights illustrating the decision of the artificial intelligence model is generated.

Abstract: A method, computer system, and a computer program product for image protection are provided. A first device includes a computer and a first camera. The first device identifies a picture that has a hallmark. The first device transmits a message requesting authorization to have a copy of the picture. The message contains data representative of the hallmark. The first device receives a response message. The first device determines whether the response message authorizes capture of a copy of the picture.

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: A processor may receive an original dataset. The processor may segment, automatically, the original dataset into a plurality of data groups. The plurality of data groups may include a model training dataset and a holdout dataset. The processor may generate a model with the model training dataset. The processor may validate the model with the holdout dataset.

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 health status of a first user is monitored from a wearable device. The health status is based on a set of one or more health metrics captured by the wearable device. The health status is measured while the wearable device is worn by the first user. It is determined that the first user is participating in a travel event that is related to a travel vehicle. A potential health concern of the first user is detected based on the health status. An intervention action related to the health status is performed, in response to the potential health concern. The intervention action is performed based on the travel event.

Abstract: One example of a system comprises using a processor for identifying a model to be validated that is stored in a repository; automatically computing and recording one or more model metrics for the model to be validated in a tamper-proof manner; comparing the computed tamper-proof metrics with one or more encoded rules and policies to determine if the model to be validated complies with the one or more encoded rules and policies; and outputting a notification to a device indicating a validation status of the model to be validated based on the comparison of the computed tamper-proof metrics with the one or more encoded rules and policies.

Abstract: Computer technology for determining a possibility of an emerging hearing issue in a user based on the user's auditory interactions with a mobile device (for example, a smartphone), a wearable computer device (for example, a smart watch) and/or a virtual assistant (for example, an internet of things (IoT) enabled doorbell).

Abstract: A computer implemented method manages an artificial intelligence model. A number of processor units detect a change in a format used to exchange information between the artificial intelligence model and an application using the artificial intelligence model. The number of processor units changes the format of the information into an expected format used by the artificial intelligence model and the application. The number of processor units exchanges the information between the artificial intelligence model and the application using the expected format.