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公开(公告)号：US11769080B2
公开(公告)日：2023-09-26
申请号：US17865152
申请日：2022-07-14
Applicant: Kyndryl, Inc.
Inventor： Sreekrishnan Venkateswaran , Debasisha Padhi , Shubhi Asthana , Anuradha Bhamidipaty , Ashish Kundu
Abstract: A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.
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公开(公告)号：US20220351082A1
公开(公告)日：2022-11-03
申请号：US17865152
申请日：2022-07-14
Applicant: Kyndryl, Inc.
Inventor： Sreekrishnan Venkiteswaran , Debasisha Padhi , Shubhi Asthana , Anuradha Bhamidipaty , Ashish Kundu
Abstract: A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.
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公开(公告)号：US11423334B2
公开(公告)日：2022-08-23
申请号：US16870202
申请日：2020-05-08
Applicant: KYNDRYL, INC.
Inventor： Sreekrishnan Venkateswaran , Debasisha Padhi , Shubhi Asthana , Anuradha Bhamidipaty , Ashish Kundu
Abstract: An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations. If the application's overall inconsistency score exceeds a threshold value, the system forwards information about the explanation, the offending modules, and the input dataset to a downstream machine-learning component that uses this information to train the application to better respond to future input that shares certain characteristics with the current input.
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