International Business Machines Corporation

Hogun Park

Peifeng Yin

Mu Qiao

Heiko Hary Ludwig

Shun Jiang

Aly Megahed

Sunhwan Lee

Shubhi Asthana

One embodiment provides a method comprising receiving training data and experts' intuition, training a machine learning model based on the training data, predicting a class label for a new data input based on the machine learning model, estimating a degree of similarity of a target attribute of the new data input relative to the training data, and selectively applying a correction to the class label for the new data input based on the degree of similarity prior to providing the class label as an output. The target attribute is an attribute related to the experts' intuition.

Resolving conflicts between experts' intuition and data-driven artificial intelligence models

Embodiments relate to systematic explanation of neural model behavior and effective deduction of its vulnerabilities. Input data is received for the neural model and applied to the model to generate output data. Accuracy of the output data is evaluated with respect to the neural model, and one or more neural model vulnerabilities are identified that correspond to the output data accuracy. An explanation of the output data and the identified one or more vulnerabilities is generated, wherein the explanation serves as an indicator of alignment of the input data with the output data.

Diagnosis of neural network

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West Lafayette, IN, United States of America

Hogun Park