- 专利标题: TECHNIQUES FOR TRAINED MODEL BIAS ASSESSMENT
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申请号: US17508734申请日: 2021-10-22
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公开(公告)号: US20230131834A1公开(公告)日: 2023-04-27
- 发明人: Hari Bhaskar Sankaranarayanan , Shahid Reza , Arpit Katiyar
- 申请人: Oracle International Corporation
- 申请人地址: US CA Redwood Shores
- 专利权人: Oracle International Corporation
- 当前专利权人: Oracle International Corporation
- 当前专利权人地址: US CA Redwood Shores
- 主分类号: G06N3/08
- IPC分类号: G06N3/08 ; G06N3/04
摘要:
A system is disclosed that is configured to perform various bias checks on an machine learning (ML) model in order to identify one or more biases, if any, that may be inherent to the ML model. Bias evaluation results generated from performing the checks are then reported to a user, such as to a consumer of the ML model, a data scientist responsible for modeling and training the ML model, and others. The bias evaluation system performs one or more bias checks by generating synthetic datasets using attributes present in the ML model or a training dataset used to train the ML model. Prediction data is then generated by inputting the synthetically generated input data points of the synthetic datasets into the ML model. The prediction data is then processed and evaluated for biases. Results of the evaluation may be compiled into a bias evaluation report.
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