Artificial intelligence model training that ensures equitable performance across sub-groups
US12039003B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 22, 2021 |
| Grant date | Jul 16, 2024 |
| Priority date | — |
| Expiry date | Jan 13, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V2201/03
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described that facilitate training an artificial intelligence (AI) model in a manner that ensures equitable model performance across different sub-groups. According to an embodiment, a system is provided that includes a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components include a training component that trains a machine learning (ML) model on training data to perform an inferencing task using an equitable loss function that drives equitable performance of the ML model across different sub-groups represented by the training data, resulting in trained version of the ML model that provides a defined equitable performance level across the different sub-groups. The equitable loss function is “sub-group aware” and penalizes variation in model performance across the sub-groups during model training and validation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.