Abstract: In some implementations, a verification device may receive a request for an electronic exchange of information. The verification device may receive geographic location information associated with the request for the electronic exchange of information. The verification device may determine, using a machine learning model and based on the geographic location information, a validity of the request for the electronic exchange of information. The verification device may determine whether to execute the electronic exchange of information based on the determination of the validity of the request for the electronic exchange of information. The verification device may perform one of executing the electronic exchange of information or rejecting the request for the electronic exchange of information based on the determination of whether to execute the electronic exchange of information.

Abstract: In some implementations, a machine learning system may generate a predicted event level, associated with an entity, based on event information associated with the entity. The machine learning system may determine that the predicted event level satisfies a threshold. The machine learning system may select, in response to determining that the predicted event level satisfies the threshold, a set of users. The machine learning system may transmit at least one communication to one or more user devices associated with the set of users.

Abstract: In some implementations, a machine learning system may receive a set of first data structures associated with a set of users, may receive a set of second data structures associated with a set of entities, and may receive event information associated with the set of users and the set of entities. The machine learning system may generate an embedding space that represents the set of first data structures, the set of second data structures, and the event information. The machine learning system may disregard a portion of the set of second data structures, using a condition, in order to generate a subset of second data structures. The machine learning system may identify at least one relevant entity, from the subset of second data structures, using the embedding space. The machine learning system may generate a communication, associated with the at least one relevant entity, and may output the communication.

Abstract: The present disclosure describes training machine learning models to determine one or more attributes and/or information associated with a location. The one or more attributes and/or information may be based on location traffic and/or connectivity information. After the machine learning model is trained and deployed, the machine learning model may determine one or more attributes and/or information about a location based on received location traffic and/or connectivity information. The machine learning model may receive a plurality of connectivity information associated with a location. Each of the plurality of connectivity information may correspond to a respective user device. Based on the connectivity information, the machine learning model may determine one or more attributes and/or information associated with the location. The one or more attributes and/or information may be provided to a user device in response to inquiries about the location.

Abstract: In some implementations, a system may receive a data stream input. The system may identify one or more sets of content parameters associated with the data stream input, each set of content parameters being associated with a different entity. The system may determine a ruling set of content parameters based on the one or more sets of content parameters. The system may selectively modify the data stream input based on a determination of whether a condition indicated by the ruling set of content parameters is satisfied in the data stream input. The system may provide a data stream output resulting from the selective modification of the data stream input.

Abstract: Disclosed embodiments may include a system for fraud detection. The system may identify, using a web browser extension, that a user has navigated to a webpage on a user device. The system may receive, via the webpage, data associated with a transaction. Responsive to receiving the data, the system may retrieve search history data corresponding to a searching session associated with the data, and may identify a searching session path corresponding to the transaction. The system may determine, using a machine learning model (MLM) and based on the search history data and the searching session path, a likelihood of fraud associated with the data. The system may determine whether the likelihood exceeds a predetermined threshold. Responsive to determining the likelihood exceeds the predetermined threshold, the system may conduct one or more fraud prevention actions.

Abstract: Disclosed embodiments may include a system for executing programmable macros based on external device interactions. The system may include one or more processors, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to execute programmable macros based on external device interactions. The system may receive a device identification of an external device. The system may receive a rule associated with the external device, wherein the rule comprises a triggering event and transfer instructions. The system may receive, from the external device, device data. Responsive to determining, from the device data, that the triggering event has occurred, the system may execute the transfer instructions.

Abstract: In some aspects, a computing system may generate graph data structures that can be compared with each other to make recommendations. A first graph data structure of the contrasting graphs may be generated based on a first subset of a dataset. For example, the first graph data structure may be generated based on users that have left an organization and graph inference techniques may be used to identify sources that caused the users to leave. A second graph data structure may be generated based on high-performing users of the organization and graph inference techniques may be used to identify potential causes of the users' high performance. The computing system may compare the first and second graph data structures to make recommendations. For example, the computing system may compare the two graphs to determine a modification to make the first graph more similar to the second graph.

Abstract: In some aspects, a computing system obtain a first dataset including a set of original data samples and a first set of spurious data samples. Based on a time period expiring, the computing system may replace the first set of spurious data samples in the first dataset with a second set of spurious data samples. The computing system may obtain an indication that a second dataset is available via a third-party computing device. Based on a determination that a subset of samples of the second dataset correspond to the first set of spurious data samples, the computing system may determine a time window in which an incident occurred. As an example, the time window may be determined to correspond to a time before the first set of spurious data samples were replaced with the second set of spurious data samples.

Abstract: In some aspects, a computing system obtain a first dataset including a set of original data samples. The computing system may generate a key that indicates a location within the first dataset where spurious data should be stored. The computing system may determine a modified value associated with a first data sample of the set of original data samples, where the modified value causes a machine learning model to generate output that does not match a label associated with the first data sample. Based on the first data sample, the computer system may generate a spurious data sample comprising the modified value. Based on the key, the computer system may add the spurious data sample to the first dataset. In some aspects, based on a request for the first dataset, the computing system may remove the spurious data sample from the first dataset.

Abstract: In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.

Abstract: In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.

Abstract: In some aspects, a computing system may iterate between adding spurious data to the dataset and training a model on the dataset. If the model's performance has not dropped by more than a threshold amount, then additional spurious data may be added to the dataset until the desired amount of performance decrease has been achieved. the computing system may determine the amount of impact each feature has on a model's output. The computing system may generate a spurious data sample by modifying values of features that are more impactful than other features. The computing system may repeatedly modify the spurious data that is stored in a dataset. If a cybersecurity incident occurs (e.g., the dataset is stolen or leaked), the system may identify when the cybersecurity incident took place based on the spurious data that is stored in the dataset.

Abstract: In some aspects, a computing system may use a surrogate machine learning model to detect whether a production or other machine learning model has a tendency to generate different output depending on which subpopulation a particular sample belongs to. The surrogate machine learning model may be trained using features/outputs that are not included in the data used by the production model. For example, by using demographic information in lieu of the original labels of a dataset that was used to train a production model, a surrogate model may be used to detect whether the production model is able to discern one or more characteristics associated with but not present in a sample using other features of the dataset. Output of the surrogate machine learning model may be clustered to detect whether certain subpopulations are treated differently by the production model.