Intuit, Inc.

Elhanan Mishraky

Yair Horesh

Yehezkel Shraga Resheff

Aviv Ben Arie

Liron Hayman

Ido Meir Mintz

Tsofit Efroni Zazon

Limor Ikar

Benjamin Cates

Asher Asaf Dadon

Shir Meir Lador

Natalie Grace De Shetler

Aspects of the present disclosure provide techniques for detecting latent bias in machine learning models. Embodiments include receiving a data set comprising features of a plurality of individuals. Embodiments include receiving identifying information for each individual of the plurality of individuals. Embodiments include predicting, for each respective individual of the plurality of individuals, a probability that the respective individual belongs to a given class based on the identifying information for the given individual. Embodiments include providing, as inputs to a machine learning model, the features of the plurality of individuals from the data set. Embodiments include receiving outputs from the machine learning model in response to the inputs. Embodiments include determining whether the machine learning model is biased against the given class based on the outputs and the probability that each respective individual of the plurality of individuals belongs to the given class.

Machine learning model bias detection

Systems and methods that implement a paired-consistency-based process for evaluating and or regulating fairness in machine learning models.

Paired-consistency-based model-agnostic approach to fairness in machine learning models

Certain aspects of the present disclosure provide techniques for adjusting access control policies of access controlled systems, such as techniques for identifying a vulnerability or for identifying parameters and values achieving a specified result from a system whose access is controlled by the policy. Requests to the system can be made using a testing system that executes test scripts using avatars having various parameter types and values. The avatar information and results of the test scripts are provided as training data to a machine learning model training system to generate a model that provides recommendations for parameter types and values likely to achieve a particular result. The recommendations are used to execute the test script to determine results including a rate of success for the recommended parameters and/or values. Various actions, such as adjusting or adding a rule to an access control policy, can be performed based on the results.

Testing complex decision systems using outcome learning-based machine learning models

Systems and methods for predicting account action failures are disclosed. An example method is performed by one or more processors of a system and includes detecting a character sequence being entered for identifying an account to be used in an account action, extracting a set of subsequences from the character sequence, determining, for the character sequence and each subsequence extracted from the character sequence, a feature value for each of a predefined set of features for characterizing sequences, the determining based on a number of characters included in at least one portion of the character sequence, and generating, using the feature values as input to a model trained in conjunction with a machine learning (ML) algorithm, a predictive value suggestive of a likelihood that the detected character sequence, if submitted, will result in the account action failing.

Validation of account identifier

Systems and methods that may be configured to implement a nearest neighbor-based bank account validation process that may be used with electronic payments, transactions and or services.

System and method for nearest neighbor-based bank account number validation

Certain aspects of the present disclosure provide techniques for improving a prediction of whether a non-sufficient funds fee will be incurred by a user utilizing machine learning techniques. For example, a predictive model may be trained using machine learning techniques based on historical data and derived data for a plurality of users. The predictive model may then be used to predict a probability of a particular user incurring an insufficient funds fee. The probability of the particular user may be used to generate an alert and suggestion to be presented to the particular user to avoid incurring the insufficient funds fee.

Insufficient funds predictor

A feature generator generates a feature data structure (FDS) by pre-processing a tax return of a user by extracting inherent features and generating derived features based on the tax return; and populating entries of the FDS based on the inherent and derived features. A prediction engine generates a prediction data structure (PDS) based on the FDS. The PDS includes a prediction that the user has a credit score above a first credit score threshold by applying the features to a machine learning model (MLM). The MLM is trained using first tax returns for first tax payers, each having a credit score above the first threshold, and second tax returns for second tax payers, each having a credit score below the first threshold. A task manager executes, based on the prediction, a task that requires the credit score of the user exceed a second credit score threshold less than the first threshold.

Machine learning tax based credit score prediction

A method and system includes generating a scalable application for mobile electronic devices, according to one embodiment. The method and system include determining a number of screen dimensions for the mobile electronic devices, according to one embodiment. The method and system include converting first display units into second display units for each of the number of screen dimensions, according to one embodiment. The method and system include generating the scalable application based at least partially on the second display units, according to one embodiment.

Growing community of inventors

Nofim, Israel

Elhanan Mishraky