Jpmorgan Chase Bank, N.a.

Freddy Lecue

Daniele Magazzeni

Sanjay Kariyappa

Giang Nguyen

Allan Anzagira

Leonidas Tsepenekas

Shubham Sharma

Saumitra Mishra

Ivan Brugere

Methods and systems for using a non-linear machine learning model to generate explanations that relate to decisions made by the model and for repairing such explanations in order to improve quality and accuracy of model outputs are provided. The method includes: receiving a data set that corresponds to attributes that pertain to a decision to be made; inputting the data set to a machine learning model; generating a baseline decision that corresponds to an output of the model with respect to data set; computing, based on the baseline decision, an explanation that relates to at least one of the attributes; estimating one or more errors associated with the explanation; and computing, based on the estimated error(s), at least one repair that corresponds to a modification of the explanation, and a cost for repairing the explanation.

Method and system for repairing explanations for non-linear machine learning models

Various methods and processes, apparatuses or systems, and media for generating a grounded answer to a query using a corresponding table are disclosed. The present disclosure provides decomposing a complex query into natural-language sub-queries or steps, which are then translated into database manipulation commands to sequentially transform input table into intermediate or simplified tables, until the input table is simplified for performing a final decision query. Each of the sub-queries may be associated with corresponding intermediate or simplified tables to ground the sub-queries.

System and method for grounding outputs in tabular generative artificial intelligence

Methods and systems for detecting attempted manipulation of a machine learning model via explanation poisoning are provided. The method includes: computing explanations based on outputs of the model that include information that relates to features that affect the output of the model with respect to the first data point; assigning labels to the explanations based on the features; generating an explanation ensemble that resides in an N-dimensional space, N being equal to a number of assigned labels plus one; determining a region within the N-dimensional space for which a subsequent introduction of data causes a subsequent explanation that does not relate to the features; and when the additional data is introduced to the determined region, generating an alert message for notifying a user that a likelihood of adverse manipulation of the model is high based on the additional data.

Method and system for detecting model manipulation through explanation poisoning

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Mamaroneck, NY, United States of America

Freddy Lecue