Capital one services, llc (20240112092). COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS simplified abstract
Contents
- 1 COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS
Organization Name
Inventor(s)
Samuel Sharpe of Cambridge MA (US)
Christopher Bayan Bruss of Washington DC (US)
Brian Barr of Schenectady NY (US)
COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112092 titled 'COUNTERFACTUAL SAMPLES FOR MAINTAINING CONSISTENCY BETWEEN MACHINE LEARNING MODELS
Simplified Explanation
The abstract of the patent application describes a computing system that utilizes machine learning and counterfactual samples to generate explanations for model predictions, train models to be consistent with previous outputs, and create counterfactual samples based on user preferences.
- The computing system aggregates multiple counterfactual samples to generate machine learning explanations for sub-populations.
- Machine learning and counterfactual samples are used to determine text for explanations of model predictions.
- The system trains machine learning models to generate output consistent with previous models.
- Counterfactual samples are generated based on user preferences, with adjustments made to features indicated by the preferences.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, and marketing for generating explanations for machine learning predictions, ensuring model consistency, and personalizing recommendations based on user preferences.
Problems Solved
This technology addresses the challenges of understanding machine learning predictions, ensuring model consistency over time, and creating personalized recommendations that align with user preferences.
Benefits
The benefits of this technology include improved transparency in machine learning models, enhanced model performance through consistency training, and personalized user experiences based on preferences.
Potential Commercial Applications
Potential commercial applications of this technology include predictive analytics software, recommendation systems, and personalized marketing platforms.
Possible Prior Art
One possible prior art for this technology could be the use of counterfactual explanations in machine learning models to improve interpretability and transparency.
Unanswered Questions
The technology does not provide details on how user preferences are collected, stored, and used in generating counterfactual samples. This raises questions about data privacy and security measures implemented in the system.
What are the limitations of using counterfactual samples in generating explanations for machine learning models?
The abstract does not mention any potential limitations or challenges associated with using counterfactual samples. Understanding the constraints of this approach could provide insights into its applicability in real-world scenarios.
Original Abstract Submitted
in some aspects, a computing system may aggregating multiple counterfactual samples so that machine learning explanations can be generated for sub-populations. in addition, methods and systems described herein use machine learning and counterfactual samples to determine text to use in an explanation for a model's prediction. a computing system may also train machine learning models to not only determine whether a request to perform an action should be accepted, but also to generate output that is consistent with output generated by previous machine learning models. further, a computing system may generate counterfactual samples based on user preferences. a computing system may obtain preferences and then apply a penalty or adjustment parameter such that when a counterfactual sample is created, the computing system is forced to change one or more features indicated by the preferences to create the counterfactual sample.
