公开(公告)号：US20210295210A1

公开(公告)日：2021-09-23

申请号：US16826478

申请日：2020-03-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Kenneth P. BACLAWSKI ,  Guang C. WANG ,  Kenny C. GROSS ,  Anna CHYSTIAKOVA ,  Dieter GAWLICK ,  Zhen Hua LIU ,  Richard Paul SONDEREGGER

IPC: G06N20/00 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27

Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.

公开(公告)号：US20230376837A1

公开(公告)日：2023-11-23

申请号：US17751083

申请日：2022-05-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Matthew T. GERDES ,  Kenneth P. BACLAWSKI ,  Dieter GAWLICK ,  Kenny C. GROSS ,  Guang Chao WANG ,  Anna CHYSTIAKOVA ,  Richard P. SONDEREGGER ,  Zhen Hua LIU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with associated with dependency checking for machine learning (ML) models are described. In one embodiment, a method includes applying a repeating probe signal to an input signal input into a machine learning model. An estimate signal output from the machine learning model is monitored, and the repeating probe signal is checked for in the estimate signal. Based on the results of the checking for the repeating probe signal, an evaluation of dependency in the machine learning model is presented.

公开(公告)号：US20240256959A1

公开(公告)日：2024-08-01

申请号：US18226522

申请日：2023-07-26

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Kenneth P. BACLAWSKI ,  Richard P. SONDEREGGER ,  Dieter GAWLICK ,  Anna CHYSTIAKOVA ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with detecting unfairness in machine learning outcomes are described. In one embodiment, a method includes generating outcomes for transactions with a machine learning tool to be tested for bias. Then, actual values for a test subset of the outcomes that is associated with a test value for a demographic classification are compared with estimated values for the test subset of outcomes. The estimated values are generated by a machine learning model that is trained with a reference subset of the outcomes that are associated with a reference value for the demographic classification. The method then detects whether the machine learning tool is biased or unbiased based on dissimilarity between the actual values and the estimated values for the test subset of the outcomes. The method then generates an electronic alert that the ML tool is biased or unbiased.