Abstract: Disclosed herein are methods and systems for generating a merged dataset, comprising: accessing data comprising a core dataset and an additional dataset; identifying a plurality of common attributes between the core dataset and the additional dataset; determining a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities: calculating a similarity score for the candidate entity based at least in part on a distance-based score and a weight influence score; selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores; and generating the merged dataset by adding the one or more selected matches for the inquiring entity to the core dataset.

Abstract: An exemplary system and method are provided for monitoring concept drift in a trained model, such as a chatbot. The system is configured to perform a method including receiving a first dataset representing model operations executed by the trained model; applying a data processing operation to the first dataset to determine a first result data of the first dataset; determining, based on the first result data, a difference between the first result data and a second result data; determining, based on the difference, whether concept drift has occurred; and in accordance with a determination that concept drift has occurred, transmitting an instruction to update training of the trained model. The exemplary system may be implemented as a drift detection system in communication with the trained model.

Abstract: Systems and methods for generating and utilizing an interactive causal loop diagram using a causal loop designer are provided. In one or more examples, a computer-implemented method for creating a causal loop diagram comprising visually emphasized elements can comprise displaying a first element and a second element, wherein the first element and the second element comprise visually emphasizable elements. In response to receiving a user command to connect the first element to the second element, the method can comprise displaying a connection between the first element and the second element, wherein the connection comprises a visually emphasizable element. In one or more examples, the method comprises displaying a causal loop diagram comprising the first element, the second element, and the connection. In response to a user activating a visual emphasis tool, the method can comprise visually emphasizing one or more of the visually emphasizable elements.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

Abstract: Systems and methods for generating and utilizing an interactive causal loop diagram using a causal loop designer are provided. In one or more examples, a computer-implemented method for creating a causal loop diagram comprising visually emphasized elements can comprise displaying a first element and a second element, wherein the first element and the second element comprise visually emphasizable elements. In response to receiving a user command to connect the first element to the second element, the method can comprise displaying a connection between the first element and the second element, wherein the connection comprises a visually emphasizable element. In one or more examples, the method comprises displaying a causal loop diagram comprising the first element, the second element, and the connection. In response to a user activating a visual emphasis tool, the method can comprise visually emphasizing one or more of the visually emphasizable elements.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.