Arthur Ai, Inc.

John Dickerson

Keegan Hines

Sahil Verma

Priscilla Alexander

Elizabeth O'sullivan

Reese M E Hyde

Rowan Cheung

Karthik Rao

Adam Wenchel

In an embodiment, the systems and methods discussed herein are related to generating, via a processor, a Markov Distribution Problem (MDP), the MDP including a state space, an action space, a transition function, a reward function, and a discount factor. A reinforcement learning (RL) model is applied, via the processor, to the MDP to generate a RL agent. An input data associated with a first user is received at the RL agent. At least one counterfactual explanation (CFE) is generated via the processor and by the RL agent and based on the input data. A representation of the at least one CFE and at least one recommended remedial action is caused to transmit, via the processor, to at least one of a compute device of the first user or a compute device of a second user different from and associated with the first user.

Media for generating fast counterfactual explanations using reinforcement learning

A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.

Machine learning monitoring systems and methods

In some embodiments, a first plurality of representations are extracted from a first data set. A first set of distributions are generated based on the first plurality of representations. A machine learning model is trained based on the first plurality of representations and the first set of distributions. A second plurality of representations are extracted from a second data set different from the first data set. The machine learning model is executed based on the second plurality of representations to produce a second set of distributions. An anomaly score is determined for each datum from the second data set to produce a set of anomaly scores. The set of anomaly scores are determined based on the first set of distributions and the second set of distributions. A notification is generated when at least one anomaly score from the set of anomaly scores is larger than a predetermined threshold.

Systems and methods for detecting drift between data used to train a machine learning model and data used to execute the machine learning model

In an embodiment, the systems and methods discussed herein are related to generating, via a processor, a Markov Decision Process (MDP), the MDP including a state space, an action space, a transition function, a reward function, and a discount factor. A reinforcement learning (RL) model is applied, via the processor, to the MDP to generate a RL agent. An input data associated with a first user is received at the RL agent. At least one counterfactual explanation (CFE) is generated via the processor and by the RL agent and based on the input data. A representation of the at least one CFE and at least one recommended remedial action is caused to transmit, via the processor, to at least one of a compute device of the first user or a compute device of a second user different from and associated with the first user.

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Washington, DC, United States of America

John Dickerson